Meta-Scientific Study of Artificial Intelligence (Advances in Research on Russian Business and Management) 9781648025150, 9781648025167, 9781648025174, 1648025153

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Meta-Scientific Study of Artificial Intelligence

A volume in Advances in Research on Russian Business and Management Elena G. Popkova, Series Editor

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Meta-Scientific Study of Artificial Intelligence

edited by

Elena G. Popkova MGIMO University

Victoria N. Ostrovskaya Center for Marketing Initiatives

INFORMATION AGE PUBLISHING, INC. Charlotte, NC • www.infoagepub.com

Library of Congress Cataloging-in-Publication Data   A CIP record for this book is available from the Library of Congress   http://www.loc.gov ISBN: 978-1-64802-515-0 (Paperback) 978-1-64802-516-7 (Hardcover) 978-1-64802-517-4 (E-Book)

Copyright © 2021 Information Age Publishing Inc. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, microfilming, recording or otherwise, without written permission from the publisher. Printed in the United States of America

CONTENTS PA RT I META-SCIENTIFIC APPROACH TO THE BALANCED USE OF HUMAN AND ARTIFICIAL INTELLIGENCE 1 Challenges of the Modern Global World: Dialetics of Scientific and Technological Development and Property Relations................... 3 Dmitry P. Sokolov and Angelica P. Buevich 2 Socio-Philosophical Understanding of Artificial Intelligence.......... 13 Aza D. Ioseliani and Nelli V. Tskhadadze 3 Philosophical and Epistemological Factors of Artificial Intelligence........................................................................................... 23 Tair M. Makhamatov, Timur T. Makhamatov, and Saida T. Makhamatova 4 Research Philosophy as Tools of Cross-Disciplinary Framing.......... 31 Alexander M. Starostin 5 The Human Capital in the Conditions of Digital Economy (Assessment Problem).......................................................................... 39 Lyubov Yu. Arkhangelskaya, Victor N. Prasolov, and Marina V. Vachrameeva 6 Features and Models of Human Capital Reproduction in the Conditions of Artificial Intelligence Development............................ 49 Valentina V. Gorbunova, Lilianna Yu. Grazhdankina, Natalya K. Mayatskaya, Marina M. Shulga, and Nina Yu. Zhelnakova 

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vi   Contents

7 Methodology for Measuring the Quality of Life of the Population on the Basis of Regional Differentiations....................... 59 Vera I. Menshchikova, Natalia V. Zlobina, Dmitry D. Logvin, and Mkhran Khavashki 8 Life Is Grey, but the Tree of Theory Is Evergreen............................. 69 Ladislav Zhak 9 Forecast for the Development of Human Capital in the Agricultural Sector at the Regional Level.......................................... 75 Alexander W. Turyanskiy, Andrei F. Dorofeev, Alina I. Dobrunova, and Tatiana V. Kasaeva 10 A Modern Man: The Dialectic of His Place and Role in the Modern Digital Society............................................................. 85 Marina L. Alpidovskaya 11 Artificial Intelligence in Journalism: Prospects, Challenges, and Problems........................................................................................ 91 Svetlana N. Gikis 12 Artificial Intelligence as a Space of Speech Reflection..................... 99 Viacheslav I. Shulzhenko, Leokadiya V. Vitkovskaya, Igor F. Golovchenko, Tatiana D. Savchenko, and Marina Yu. Sumskaya 13 Cognitive Aspects of Artificial Intelligence Semiolinguistics: Signs, Concepts, Discourse................................................................ 107 Andrey V. Olyanich, Zaineta R. Khachmafova, Susanna R. Makerova, Marjet P. Akhidzhakova, and Tatiana A. Ostrovskaya

PA RT I I FEATURES OF SUCCESSFUL DEVELOPMENT OF THE INFORMATION ECONOMY UNDER THE CONDITIONS OF TECHNOLOGICAL PROGRESS BASED ON ARTIFICIAL INTELLIGENCE 14 Digitalization of Key Sectors of Russia: Main Problems and How They Could Be Overcome..........................................................117 Olga V. Danilova, Irina Yu. Belayeva, Galina V. Kolodnyaya, and Alexey Yu. Zhdanov 15 The Use of Probabilistic Model of Remote Cyptographic Transformation in Multilateral Treaties Online.............................. 127 Gennady A. Vorobyev, Vladimir A. Kozlov, Victoria A. Ryndyuk, Irina I. Pavlenko, and Igor V. Kaliberda

Contents    vii

16 Modeling of Knowledge Based by Means of Pre-Fractal Graphs....... 137 Ilyas Z. Batchaev, Aleksandra V. Ryzhuk, Irina V. Sklyarova, Olga V. Timchenko, and Irina I. Pavlenko 17 Information Technologies in the Development Management of the Municipal Socioeconomic System.......................................... 145 Tatiana Yu. Anopchenko, Anton D. Murzin, Kometa T. Paytaeva, Svetlana G. Chumachenko, and Alla V. Temirkanova 18 The Use of Computer Vision and Artificial Neural Networks to Assess the Technological Advantages of Wheat.......................... 157 Pavel V. Medvedev, Vitaly A. Fedotov, and Irina A. Bochkareva 19 IT-Technologies and Artificial Intelligence in Modern Media and Media Education......................................................................... 165 Arevik A. Gevorgyan, Irina N. Karapetova, and Tatiana V. Kara-Kazaryan 20 Automated System of Environmental Monitoring as a Tool to Improve Socio-Ecological and Economic Efficiency of Environmental Management at Micro- and MesoEconomic Levels................................................................................. 175 Roman V. Revunov, Vladimir A. Gubachev, Vladimir B. Dyachenko, Kometa T. Paytaeva, and Kseniya Yu. Boeva 21 The Impact of Artificial Intelligence on the Development of Human Resources Technologies................................................... 185 Galina N. May-Boroda, Elena Yu. Shatskaya, Natalia P. Kharchenko, Vitaliy F. Zhuravel, and Ekaterina V. Efimova 22 Management of Professional Training Process as a Key Factor of Digital Economy Development...................................................... 193 Galina V. Vorontsova, Nadezhda V. Miroshnichenko, Ekaterina V. Efimova, Elena V. Baboshina, and Irade S. Guseynova 23 The Mechanism of Adaptation of the Educational and Labor Markets to the Meeting of Human Intelligence and Artificial Intelligence.................................................................. 203 Svetlana V. Lobova, Aleksei V. Bogoviz, and Tatiana V. Aleksashina 24 The Problems of Employment and Unemployment on Regional Labor Markets in the Digital Economy....................... 211 Sergey M. Gorlov, Inna N. Kazakova, Sofiya G. Kilinkarova, Elena V. Sharunova, and Evgeny A. Shevchenko

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25 Prospects for Technological Growth of Russia in Terms of Digitalization of the Economy...................................................... 223 Galina V. Vorontsova, Olga N. Kusakina, Nikolai V. Eremenko, Viktoriya M. Vlasova, and Sergey I. Lugovskoy 26 Artificial Intelligence in Digital Type Logistics Systems................. 233 Ivan D. Afanasenko and Vera V. Borisova

PA RT I I I IMPLEMENTATION OF THE SUBJECT APPROACH IN PSYCHOLOGY AND PEDAGOGY BASED ON ARTIFICIAL INTELLIGENCE 27 Cognitive Assessment of the Students’ Morality and Identity With the Help of Artificial Intelligence Programs.......................... 243 Irina A. Kolinichenko, Ekaterina N. Asrieva, Tatyana V. Varfolomeeva, Natalia V. Gordienko, and Elena A. Enns 28 Artificial Intelligence and Morality: Psychological Aspects of Interaction...................................................................................... 251 Dmitriy A. Medvedev, Inna V. Kicheva, Nina M. Shvaleva, Elena I. Gorbacheva, and Natalia M. Skripnik 29 Development of Professional Tolerance as a Factor of Competitiveness of Specialists in the Sphere of State and Municipal Management in the Context of the Widespread Adoption of Artificial Intelligence............................... 263 Ekaterina S. Borisova, Aleksey V. Komarov, and Ekaterina R. Bezsmertnaya 30 Tools of Intellectual Systems in the Context of Problems of Organization of Open Education................................................. 271 Olga V. Timchenko, Andrey B. Timchenko, Svetlana I. Abakumova, Alla A. Mansurova, and Gul’zhan B. Suyunova 31 Artificial Intelligence in Foreign Languages Teaching: Important Trends and Application Possibilities.............................. 279 Tatyana O. Bobrova, Elena N. Pronchenko, Nataliya V. Gurova, Stoyana V. Znamenskaya, and Irina V. Shatokhina 32 System of Optimization of Cognitive Development of the Subject of Engineering Activity in the Conditions of Use of Intelligent Computer Programs.................................................... 293 Tatyana A. Mayboroda, Natalia K. Mayatskaya, Karina M. Oganyan, Galina V. Stroi, and Andrey B. Chernov

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33 Information Culture of Personality in the Conditions of Artificial Intelligence..................................................................... 303 Magomet D. Elkanov, Fatima H. Laipanova, and Marina N. Kubanova 34 Digital Economy: New Requirements and Approaches in Lawyers Training............................................................................311 Elena N. Bazurina, Lidia N. Ivanova, Vladimir Yu. Karpychev, Sergey I. Kuvychkov, and Andrey M. Terekhov 35 The Experience of Organization of Educational Space and Increasing the Financial Literacy of all Layers at Minin University......319 Irina S. Vinnikova, Anastasia O. Egorova, Ekaterina A. Kuznetsova, Olga I. Kuryleva, and Larisa V. Lavrentyeva 36 Experience of Modern Pedagogical Technologies in Teaching Physical Culture and Sport Implementation.................................... 327 Svetlana M. Markova, Lyubov I. Kutepova, Olga I. Vaganova, Zhanna V. Smirnova, and Maxim M. Kutepov 37 Retrospective of the Mentoring System............................................ 335 Elena A. Chelnokova, Svetlana N. Kaznacheeva, Natalia V. Bystrova, Antonina L. Lazutina, and Yuri S. Zhemchug

PA RT I V POLITICAL AND LEGAL ASPECTS OF CREATING, IMPLEMENTING, AND DEVELOPING ARTIFICIAL INTELLIGENCE 38 The Metascientific Analysis of the Legal Regime of Artificial Territories in the International and Russian Legislation................ 343 Elena A. Grin, Luiza T. Kokoeva, and Anastasia S. Malimonova 39 The Experience of the Use of Artificial Intelligence in Legal Practice................................................................................. 351 Victoria N. Ostrovskaya, Marine Z. Abesalashvili, Radmila E. Arutyunyan, Raphael F. Mustafin, and Svetlana A. Mustafina 40 Artificial Intelligence as an Object of Intellectual Rights............... 359 Vitaly V. Kovyazin, Elena V. Serdyukova, Anna A. Minina, Olga A. Perepadya, and Gennady V. Shevchenko 41 Problems of Criminal Liability for Damage Caused by an Unmanned Vehicle....................................................................... 367 Konstantin V. Chemerinsky, Konstantin A. Amiyants, Annette A. Mordovina, Olga A. Zakharyan, and Aksana F. Bunina

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42 Securitization of the Problem of Political Bots................................ 373 Gennady V. Kosov, Sergey A. Nefedov, Galina V. Stankevich, Arsen V. Gukasov, and Nadezhda Yu. Shlyundt 43 The Realization of the Potential of Artificial Intelligence in Criminal Law, Prospects of Development of Forensic Profiling..... 383 Lev V. Bertovsky, Natalia S. Burmistrova, Evgeny N. Petukhov, Irina M. Vilgonenko, and Yurij N. Shapovalov 44 Civil Law Aspects of the Phenomenon of Artificial Intelligence.... 391 Polina N. Durneva, Irina V. Pogodina, Elvira T. Mayboroda, Olga A. Perepadya, and Galina V. Stankevich 45 The Legal Nature of Artificial Intelligence Through the Prism of Copyright: Theoretical and Legal Aspect.................................... 401 Inessa Sh. Galstyan, Lyudmila A. Tkhabisimova, Yevgeny E. Nekrasov, Galina V. Stankevich, and Irina M. Vilgonenko 46 Artificial Intelligence in Politics: Global Leadership and the Risks of Competitive Struggle........................................................... 409 Maria A. Adamova, Mariana L. Kardanova, Alexandra V. Yakusheva, Maria A. Dyakonova, and Aza V. Mankieva 47 Legal Aspects of Using Artificial Intelligence (Digital Technology) in the Field of Taxation...................................................................... 419 Olga Yu. Bakaeva, Eugeniy G. Belikov, Elena V. Pokachalova, Margarita B. Razgildieva, and Marina A. Katkova

PA RT V THE IMPACT OF ARTIFICIAL INTELLIGENCE ON THE ECONOMY AND FINANCIAL SERVICES 48 Artificial Intelligence as an Economic Category: The Essence, Specifics, and Perspectives of Practical Application........................ 429 Svetlana V. Lobova, Aleksandra V. Zakharova, Viktor I. Dobrosotskiy, and Dmitry V. Bateikin 49 Economic Factors of AI and the Tools of Their Control................. 437 Yury L. Talismanov, Elena A. Kirova, Sergei V. Shkodinsky, Natalia A. Rykhtikova, and Andrey G. Nazarov 50 Post-Economy of AI: New Challenges and Perspectives of Sustainable Development of Socio-Economic Systems............... 445 Evgenii M. Buhvald, Elena I. Larionova, Pavel T. Avkopashvili, Syuzanna T. Adamyants, and Alexander N. Alekseev

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51 The Role of the System of Provision of Cyber Security Among the Economic Factors of AI............................................................... 453 Viktor I. Dobrosotskiy, Elena L. Gulkova, Mikhail Y. Zakharov, and Pavel T. Avkopashvili 52 Digitalization as a Factor of Development of Russia’s Banking System.................................................................................. 461 Alim B. Fiapshev and Oksana N. Afanasyeva 53 A Paradigm Shift in Business Management in the Context of Industry 4.0..................................................................................... 469 Kirill A. Gorelikov, Aleksey V. Komarov, and Ekaterina R. Bezsmertnaya 54 Remote Banking in Modern Russia in the Conditions of Artificial Intelligence Development............................................. 477 Nelli V. Tskhadadze and Aza D. Ioseliani 55 The Transformation in Collective Investment Under the Influence of Artificial Intelligence.................................................... 489 Yana N. Radzievskaya, Yuriy Yu. Shvets, Olga V. Karamova, Evgeny V. Sumarokov, and Alexandra E. Sergeyeva 56 Features of Artificial Intelligence Technologies and Their Use and Impact on Transformation in the Banking Sector................... 499 Galina V. Stankevich, Gayane Yu. Atayan, Olga N. Amvrosova, Catherine V. Kasevich, and Tatiana V. Kara-Kazaryan 57 The Role and Importance of Electronic Trading Platforms in Terms of Digitalization of the Economy...................................... 507 Nina V. Demina, Marina V. Chistova, Olga S. Eremina, Olga I. Natkho, and Aleksandra V. Ryzhuk 58 Trend Analysis in the Use of Artificial Intelligence in Financial Management...................................................................517 Andrey V. Efimov, Anna V. Savtsova, Olga N. Pakova, Yuliya N. Dyakova, and Alfiia A. Sokolova 59 Prospects of the Use of Artificial Intelligence and Automatization Systems in Accounting and Auditing in the Realities of the Digital Economy............................................ 527 Natalia N. Balashova, Spartak A. Vardanyan, Maria V. Volodina, Nataliya A. Ishkina, and Ilya A. Koshkarev 60 Clustering of the Central Federal District Regions by the Quality of Life of the Population...................................................... 535 Vera I. Menshchikova, Elena Y. Merkulova, Sergey P. Spiridonov, Irina A. Andreeva, and Anatoly N. Berezhnoy

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61 Modeling of Innovative Development of the Bank in the Conditions of Competition and Inflation........................................ 541 Alexander P. Gorbunov, Tatiana V. Kasaeva, Alexander P. Kolyadin, and Leyla D. Tokova 62 The Role of Opportunity Costs in the Organization and Production of Medical and Health Services Using Blockchain Technologies................................................................... 549 Ekaterina A. Pogrebinskaya, Galina A. Rybina, and Valentina V. Kuznetsova

PA RT V I MODERNIZATION OF MANAGEMENT OF PRODUCTION AND DISTRIBUTION PROCESSES AND SYSTEMS BASED ON ARTIFICIAL INTELLIGENCE 63 Study of Restructuring Strategies: Decentralization of Management and Enterprise Structure....................................... 559 Ekaterina P. Garina, Elena V. Romanovskaya, Natalia S. Andryashina, Elena P. Kozlova, and Anastasia D. Efremova 64 The Principle of Subjectification in Assessment of the Management Performance of the Organization in Terms of the Development of Artificial Intelligence................................... 567 Natalia A. Zhuravleva and Martin G. Grigoryan 65 Quality Assessment and Improving the Efficiency of Resource Management of the Industrial Enterprise........................................ 575 Ekaterina P. Garina, Victor P. Kuznetsov, Alexander P. Garin, Natalia S. Andryashina, and Elena V. Romanovskaya 66 Prospects for Marketing Management in the Context of Artificial Intelligence Development............................................. 583 Ekaterina S. Kovanova, Oksana N. Momotova, Ilyas Z. Batchaev, Irina V. Sklyarova, and Elena A. Ponomareva 67 Experience in Implementation of Crowdsourcing Technologies in an Advertising Campaign...................................... 591 Evgeny E. Egorov, Tatyana E. Lebedeva, Maria P. Prokhorova, Sergey V. Semenov, and Dmitry Yu. Vagin 68 Making Marketing Decisions in an Unstable Economic Environment....................................................................................... 599 Nataliia N. Muraveva, Lyudmila V. Belokon, Milena A. Ignatova, Alexander V. Shuvaev, and Natalya N. Yakovenko

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69 Special Aspects of Modern Production Systems Organization....... 609 Evgeny A. Semakhin, Ekaterina P. Garina, Elena V. Romanovskaya, Natalia S. Andryashina, and Dmitry S. Mokerov 70 Impact on Risk Factors of Industrial Enterprises.............................617 Yaroslav S. Potashnik, Ekaterina P. Garina, Elena P. Kozlova, Svetlana N. Kuznetsova, and Alexander P. Garin 71 Investment Attractiveness of Artificial Intelligence Technologies in Industrial Parks....................................................... 625 Victor P. Kuznetsov, Svetlana N. Kuznetsova, Sergey D. Tsymbalov, Elena V. Romanovskaya, and Natalia S. Andryashina 72 Macro-Planning of Innovative Strategies in BRICS Countries: Study of Methodological Features of Evaluation, Comparative Advantages, Opportunities, and Challenges in the Light of Accelerating the Digitalization of the Economy and the Intensive Development of Artificial Intelligence............................. 633 Bahadyr J. Matrizaev, Leyla M. Allakhverdieva, and Muslima K. Sultanova 73 Features of Process of Formation of Innovative Economy on the Basis of Artificial Intelligence............................................... 641 Svetlana V. Panasenko, Vyacheslav P. Cheglov, Elena A. Mayorova, and Alexander F. Nikishin 74 Prospects and Pitfalls of Innovation Development.......................... 649 Alexey M. Kornilov 75 Evaluation of the Economic Efficiency of Investment Projects With Potentially Infinite Lifecycle.................................................... 661 Yaroslav S. Potashnik, Victor P. Kuznetsov, Alexander P. Garin, Elena V. Shpilevskaya, and Elena S. Gailomazova 76 The Role of Foreign Economic Relations in Development of Digital Technologies in Oncological Service: The Experience of Modern Russia........................................................... 669 Yuri V. Przhedetsky, Natalia V. Przhedetskaya, and Ksenia V. Borzenko About the Editors............................................................................... 677

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PART I META-SCIENTIFIC APPROACH TO THE BALANCED USE OF HUMAN AND ARTIFICIAL INTELLIGENCE

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CHAPTER 1

CHALLENGES OF THE MODERN GLOBAL WORLD Dialetics of Scientific and Technological Development and Property Relations Dmitry P. Sokolov Financial University Angelica P. Buevich Financial University

ABSTRACT The property relations comprise a dialectical unity of the relations of appropriation and alienation of means of production, labor and results of labor in the phases of production, distribution, exchange, and consumption of the process of social reproduction. Transformation of property relations is a permanent process of change of the system of property relations, its elements and links between them, determined by the dynamics of development of production methods (both basic and additional) within the framework of this socioeconomic system. The modern Russian system of property relations is determined by the dialectics of the development of productive forces and industrial relations Meta-Scientific Study of Artificial Intelligence, pages 3–11 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

3

4    D. P. SOKOLOV and A. P. BUEVICH both at the national and global level, based on the logic of the functioning of the geo-economic system, the constituent element of which is the domestic economic system. The Russian Federation is characterized by a special peripheral system of relations of appropriation of alienation, the key element of which is the integration into the world system of division of labor of the main industries in the field of mining and their primary processing followed by nonequivalent exchange of goods in international trade for high-tech goods with high added value. Within the framework of such a system of exchange and the corresponding institutional structure of the Russian economy, national enterprises producing high-tech and knowledge-intensive products are separated from development, which negatively affects the national security of Russia and the implementation of national economic interests in general.

The intensive development of convergent NBIC-technologies in the last decade in the context of the crisis state of the global economy indicates the formation of a new long cycle of technological development, which would result in expansion of markets with the reformatting of the world system of division of labor (Akaev, 2014, p. 37). Over the past decade, advanced industrial countries have been actively competing to build national innovation systems which due to accelerated introduction of advanced technologies could ensure higher profitability of production and the financial sector and, consequently, higher rates of economic growth through the maximum intellectual rent arising in such conditions (Tolkachev, 2017, para. 6). The implementation of Russia’s industrial policy to create competitive high-tech industries largely depends on the current system of appropriation and alienation relations in the country. METHODOLOGY This chapter analyzes the special features of modern processes of development of nano- and bio-information and cognitive technologies as a factor of transformation of the geo-economic system. The process of replacing key technologies with the latest ones is defined in the economic literature in different ways: as a change of technological orders, as a change of technological paradigms (K. Freeman), as a transition from one innovation pause to another (Frolov, 2013). RESULTS The sixth technological order is developed on the basis of existing production of the fifth order: it is emerging, forming a specific superstructure over the production of the fifth order, is forming its own technological,

Challenges of the Modern Global World    5

V TY

V TY

Origin

VI

VI TY

Superstructure

Substitution

Figure 1.1  The emergence and development of a new technological order.

industrial, and marketing base and is developing steadily to reach the limits of growth (Figure 1.1). The change of technological orders in the global economy creates uncertainty about global leadership: new markets are emerging spontaneously, and as they are not yet divided, competition for leadership among national innovative industrial systems is becoming increasingly fierce. The capture of new high-tech markets means nonequivalent foreign trade exchange, that is inflating the price of goods by an amount equal to intellectual rent, due to monopoly access to appropriate production technologies (Glazyev, 2010). As a result of nonequivalent exchange, the centro-peripheral system of world reproduction is constantly reproduced. The most technologically advanced countries, to which the value-added chains are moving, constitute the core of this system that can be seen in Figure 1.2. The process of changing the technological order starts the mechanism of reformatting the core-peripheral relations: the periphery countries get a chance to develop, and the core countries—the risk of losing global leadership (Alpidovskaya, Gryaznova, & Sokolov, 2018, p. 643). The development within the national economy of globally competitive industries of the sixth technological order involves two main points: innovation activity (generation of innovations and their introduction into movement of capital Good X (a) predominantly high value-added; (b) goods with lower cost than in the periphery; (c) rare goods

Good X the price is much more than the cost Core

Periphery

the price is slightly more than the cost Good Y economic pressure military force

Figure 1.2  Trade turnover in the centro-periphery system.

Good Y (a) predominantly low value-added: raw materials, semifinished products— intermediate goods; (b) public goods (c) labor force

6    D. P. SOKOLOV and A. P. BUEVICH

production) and the availability of sufficient resources—financial, logistic, and intellectual. The concentration of all types of resources in the core countries of the current system—the United States, Japan, and the European Union—corresponds to large-scale national innovation development programs (Arkhipova, 2014). According to the World Bank, in 2012, the leaders in the volumes of high-tech exports were China (over $500 billion), Germany, the United States, Singapore, Japan, Korea and France ($100–$180 billion); the 29th place in the ranking with a result of $7 billion is taken by Russia (The World Bank, 2014). Thus, China, Korea, Singapore also have a significant resource in building an effective innovation system—in these countries, in addition to a developed industrial, scientific and technical base, there is a high level of innovation activity (GII, 2014). In the context of competition of national innovation systems for the emerging markets of new products, it is expected to restructure the relations of nonequivalent exchange in international trade, which can subsequently lead to the restructuring of the centro-peripheral system of global appropriation and alienation. There are three variants of reformatting: (a) conservation of the current core of the system, (b) the transfer of the core of the system (a cluster of transnational corporations) to the countries of the Asia-Pacific region, (c) the disintegration of the global economy into regional systems of division of labor with its financial infrastructure and markets (Moscow Economic Forum, 2014). Determining the impact of the development of NBIC-technologies on the transformation of property relations in the Russian Federation includes the consideration of the following groups of problems: 1. prospects of the domestic socioeconomic system in the process of transformation of the global system of relations of appropriation and alienation under the influence of development of NBIC-technologies; 2. influence of the current system of property relations in Russia on the process of production development of the sixth technological order; and 3. determination of mechanisms for correcting the process of transformation of property relations in Russia in order to develop a competitive high-tech industry. As illustrated in Figure 1.3, property relations in Russia are interdependent with the development of modern “breakthrough” technologies. On the one hand, the development of NBIC-technologies is determined by the peculiarities of the domestic system of property relations and the place of Russia in the modern global system of division of labor. On the other hand,

Challenges of the Modern Global World    7 Place of Russia in the global division of labor The system of property relations in Russia

Development of NBICtechnologies State regulation of property relations

Figure 1.3  Interdependence of technological development and property relations in Russia.

the need to develop high-tech industrial production determines the correction of the current system of relations of appropriation and alienation in Russia (primarily by the state) in order to provide high-tech industries with the necessary development resources. The level of development of domestic production based on NBIC-technologies will determine the competitiveness in new markets and the place of Russia in the world system of appropriation and alienation, which is being transformed in the new economic conditions. The domestic system of relations of appropriation and alienation at the present stage of its development is characterized by the interaction of two fundamentally different contours of property relations (see Figure 1.4), between which a rigid vertical hierarchy is formed. The first contour includes the large business companies in the sphere of mining and primary processing of minerals built into the world system of division of labor in combination with the state in terms of the bureaucratic apparatus that implements the economic interests of these large corporations in state policy. The second contour in this system is represented by the national-oriented segment of the Russian economy, in particular, the manufacturing

Large Business

Population

International division of labor in global economy

“Sovereign” economy of Russia State (presented by bureaucracy)

Small- and midsized businesses

Figure 1.4  Bipolar model of property relations in modern Russia. Source: Sokolov, 2014, p. 182

8    D. P. SOKOLOV and A. P. BUEVICH

industry (competing with the products of foreign transnational corporations), agriculture, industries working on the reproduction of human potential. The source of the hierarchical relations between the presented contours is the relations concerning the appropriation and alienation of rent, which includes mining rent, export rent, monopoly rent, as well as bureaucratic rent. Rents are received at the expense of economic entities and households of the second, “sovereign” contour. At the same time, the very presence of rental flows reduces to a minimum the need for large business in the development of high technologies and in the institutional environment of such development. The domestic system of property relations has focused on the creation (and partly reconstruction) of high-tech industries based on NBIC-technologies mainly in relation to the objects of property (resources for the implementation of industrial policy). The relations of appropriation and alienation concerning these objects reveal a number of the adverse factors demanding minimization of their negative influence from all three key subjects of the relations—the state, business, and society—according to the scheme presented in Figure 1.5. The positive features of the Russian property system include the following: 1. Significant reserves of national wealth, not only in terms of natural resources, but also means of production and other assets. 2. The strengthening of the central role of the state in the socioeconomic system, which began in the 2000s, is an important factor in making the innovative development more manageable not only in terms of its institutional framework, but also in terms of direct state regulation of high-tech industries, which include defense, space, nuclear industry. 3. Significant human and intellectual potential that exists despite the degradation of education systems and fundamental and applied science. The level of education in Russia is still relatively high—in the ranking of countries according to their level of cognitive skills and education the 13th place is taken by Russia (GII, 2014). At the system level, however, the relations of appropriation and alienation that have developed in Russia during the period of “market” reforms and subsequent transformations of 1980–2010 do not contribute to the creation of new or reconstruction of high-tech industries with the use of NBIC-technologies, acting as a constituent element of a new round of geoeconomic scientific and technical development. The key negative factors of the modern Russian property system include:

Challenges of the Modern Global World    9 Significant amounts of national wealth

Increasing role of the state in the economy

1

The priority of the extractive and related industries

2

The rents received from the strategic sectors

3

Payment of intellectual rent to other countries

7

Weak protection of property rights

Property relations

High potential of human capital development

Capital outflow, foreign capital

4

The problem of bureaucratic rent

5

The problem of domestic demand

6

The degradation of applied sciences and pilot production, engineering

8

Figure 1.5  Factors of development of advanced production on the basis of NBICtechnologies in Russia from the perspective of the current system of appropriation and alienation relations.

1. Mining and primary processing industries are in a priority position comparison to other industries for which they are suppliers of resources. 2. In the course of rent mutual payments, the development funds of the sixth technological order are withdrawn from strategic industries in favor of supplier companies and the bureaucracy. 3. Intellectual rent is paid in favor of foreign suppliers and technologies, and high-tech products that fully correspond to the model of nonequivalent exchange. 4. A large group of problems caused by capital outflows and the negative impact of foreign capital. 5. The problem of bureaucratic rent, which stems from the high level of corruption in Russia, is deepening. 6. The domestic demand of the Russian Federation, being catastrophically low, is declining due to the tightening of tax policy, increase in tariffs and prices for fuel and energy products. 7. Significant challenges remain in the area of property rights protection, both in the area of ownership of tangible assets and in the area of intellectual labor products. 8. A large group of problems is associated with the degradation of applied science, pilot production, and engineering.

10    D. P. SOKOLOV and A. P. BUEVICH

CONCLUSIONS AND RECOMMENDATIONS NBIC-technologies are a set of convergent technologies in the field of nano- and bio-information, and cognitive technologies (Partnership of Civilizations, 2010, p. 55). The synergy resulting from the merger of several branches of knowledge, contributes to the acceleration of the innovation process (Kazantsev, Rubvalter, 2012, pp. 13–14). The effect of the introduction of NBIC-technologies, according to expert estimates, is comparable to the impact of the “computer revolution” of the 1980s and the spread of internal combustion engines and conveyor production in the 1930s. National innovation systems formed in industrialized countries with the active participation of the state are of key importance in the development of NBIC-technologies. The development of new high-tech industries contributes both to overcoming the crisis in the world economy and reformatting property relations in the global context (in particular by the active industrial policy of the leading national economies of Asia; Arrighi, 2006). The strategy of accelerated development of industries based on advanced nano- and bio-information, and cognitive technologies is able to come into force only under the conditions of joint action of state, society, and corporate segment directed to the transformation of property relations in Russia, carried out in accordance with national socioeconomic interests. In the long term, such a controlled transformation of the relations of appropriation and alienation will bring Russia to a qualitatively different place in the geo-economic system, even with the current geo-economic and geopolitical transformations. REFERENCES Akaev, A. A. (2014). NBIC-technologies overcome depression. Mir (Modernization, Innovation, Development), 2(18), 36–43. Alpidovskaya, M. L., Gryaznova, A. G., & Sokolov, D. P. (2018). Regress economy vs progress economy: “Alternatives of senses.” In E. G. Popkova (Ed.), Advances in intelligent systems and computing (pp. 638–646). Switzerland: Springer International Arkhipova, M. Yu. (2014, June). Statistical monitoring of innovation activity in the field of nanotechnology. XII All-Russian Conference on Management Problems–2014, Moscow, Russia, 6061–6070. Arrighi, J. (2006). Long twentieth century. Money, power, and origins of our time. Moscow, Russia: Publishing house “Territory of the Future.” Frolov, A. V. (2013). NBIC-technologies and their development in the United States in the future. Economic Review, 7(177), 63–73. GII. (2014). Global innovation index 2014. Retrieved from http://www.globalinnovation index.org/content.aspx?page=GII-Home

Challenges of the Modern Global World    11 Glazyev, S. Yu. (2010). The Strategy of advanced development of Russia in the global crisis: Monograph. Moscow, Russia: Economics. Kazantsev, A. D., & Rubvalter, A. K. (2012). NBIC-technologies: The XXI the XXI century. Moscow, Russia: INFRA-M. Moscow Economic Forum. (2014). Non-resource future of Russia. Director of marketing and sales, 9(1), 12. Partnership of Civilizations. (2010). NBIC-technologies and their impact on the dynamics of the world economy in the first half of the XXI century (the report’s thesis.). Partnership of Civilizations, 3(1), 54–58. Sokolov, D. P. (2014). Prospects of transformation of property relations in Russia. Philosophy of economy, 4(94), 182. Tolkachev, S. A. (2017). Law “on industrial policy in the Russian Federation: From defense to offensive.” Retrieved from http://expert.ru/2014/11/11/zakon-o-promy ishlennoj-politike-v-rossijskoj-federatsii_-ot-oboronyi-k-nastupleniyu/ World Bank. (2014). Indicators. Retrieved from http://data.worldbank.org/indicator/ TX.VAL.TECH.CD/countries

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CHAPTER 2

SOCIO-PHILOSOPHICAL UNDERSTANDING OF ARTIFICIAL INTELLIGENCE Aza D. Ioseliani Financial University Nelli V. Tskhadadze Financial University

ABSTRACT The aim of the authors in this chapter is to study the problem of artificial intelligence (AI) from a philosophical point of view. The spheres of application of AI are considered. The present research provides an analysis of the following issues: whether machines can think like a human; what level of development AI can reach; whether AI can have the same consciousness, mental state in the sense that a human has; whether AI is able to feel, to show emotions; what dangers are posed by AI; how unpredictable its potential is. Several scenarios for the development of AI are proposed. The philosophical analysis of the vector of transformation of modern technogenic civilization and new social reality is carried out as well. The authors come to the conclusion that although AI provides a lot of advantages and conveniences for people, nevertheless, a person should take Meta-Scientific Study of Artificial Intelligence, pages 13–21 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

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14    A. D. IOSELIANI and N. V. TSKHADADZE a responsible approach to its creation in order to avoid the risk that AI could get out of human’s control. Otherwise, the authors believe, pessimistic development scenarios can be realized, which indicate not only the dangers of sociocultural nature, but also pose an existential threat to humanity comparable in its scale to the complete extinction of Homo sapiens.

Science, both technical and humanitarian, has been actively exploring AI and the Internet since the ’90s of the 20th century. With the introduction and spread of these two correlates of social life, the ways and forms of social and interpersonal communication have changed, and we have to make adjustments in the culture and traditions. The processes of emergence of new material and spiritual realities of life have become irreversible, as well as new principles, a new reality have created that effect on people’s everyday activities. In science, the system designed to solve intellectual problems is called artificial intelligence (AI). In turn, an intellectual task is a task that people solve with the help of their own intelligence. Note that in this case, experts deliberately avoid the definition of “intelligence,” because before the emergence of AI systems the only example of intelligence was human’s one. The concept of “intelligence” can be interpreted as the ability to solve certain intellectual problems in the absence of a known algorithm for solving them. Another characteristic of intelligence is the ability of rational choice in conditions of lack of information. There is also an analytical approach to its definition as a reaction of the thought process to external stimuli at the level of neurons (Petrunin, Ryazanov, & Savelyev, 2010). In 1992, the editors of the Explanatory Dictionary of AI defined the term artificial intelligence as a property of intelligent systems to perform creative functions that traditionally have been thought of as human prerogatives. An intelligent system, in turn, is a technical or software system capable of solving problems belonging to a specific subject-matter field, the knowledge of which is stored in the memory of such a system (Averkin, HaaseRapoport, & Pospelov, 1992). METHODOLOGY The methodological basis of this work is the principles of unity of sociophilosophical and logical, concreteness, objectivity, comprehensiveness of consideration, as well as the analysis of reflexive concepts of the object under study. To achieve scientific results, the chapter uses methods of analysis used in modern social philosophy, logic and methodology of science, as well as heuristics of scientific research. Such methods include dialectical, logical, scientific-historical, comparative, and others.

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RESULTS Artificial intelligence can significantly simplify our lives. The scope of application of AI is very wide, and it can be used wherever only a person can imagine. Here are some areas in which it has already been successfully used (Trapeznikov, Brynza, & Matasova, 2017): 1. Medicine. The advantage of AI in this area is the ability to remember and process a huge amount of information that has resulted in the emergence of applications that can not only give advice to doctors, but also programs that can detect the disease in the early stages, when the symptoms have not yet become evident. For example, the application Face2Gene scans your face and is able to identify 3,500 different genetic diseases. 2. Industry and agriculture. In these areas, AI has developed to such an extent that soon people will be completely unnecessary. Thus, LG will open a plant in 2023, where all stages will be performed by AI starting with the purchase of goods and the shipment of finished commodity. In the rural industry, AI monitors the condition of plants, the level of humidity, the amount of nutrients in the soil. Moreover, it is able to detect weeds and pull them out without harm to plants. 3. Traffic. Artificial intelligence is already being used to prevent traffic jams. To do this, it collects real-time information from traffic lights, analyzes the distance between cars, existing accidents and analyzes it to improve the traffic situation. Similar systems have already been implemented in many countries. Another direction of AI in this area is self-driving cars. 4. Smart house. For example, AI can wake you up in the morning and push the curtains. It will be also possible in the near future to enjoy all the convenience of smart batteries that adapt to the temperature of the person’s body. Intelligent vacuum cleaner now cleans the house without any person’s assistance. 5. Smart translators. Artificial intelligence has reached the level that they often perform their functions no worse than a person. 6. Virtual assistants on smartphones. In this case everything is implemented through AI, starting with speech recognition and ending with the provision of a ready-made solution. 7. In 2016, Alexey Tikhonov, a leading analyst at Yandex, created a neural network capable of writing poetry (Petrunin et al., 2010).

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Thus, AI has shown power and effectiveness in various fields, including medicine, commerce, finance, media, crime control, and more. Therefore, the study of AI, today has entered a new stage of its development. The question “Can a machine think?” has given rise to the most heated debate in the philosophy of AI. Thinking is a process of information processing in memory: synthesis, analysis, and self-programming (Lyushnina, 2014). In reality, AI and a human are very different. They only have the same ability to think, but this is done in different ways. It should be noted that there are two points of view on AI, these are hypotheses of weak and strong AI. Let us consider them. Applied AI (the terms “weak AI,” “applied AI,” or “narrow AI” are also used) is AI, designed to solve any one intellectual problem or a small set of them. This class includes systems for playing chess, pattern recognition, speech, the issues of bank loans, and so on. In contrast to the applied AI, the concept of universal AI is introduced (the terms “strong AI” and artificial general intelligence [AGI] are used). The term “strong (or general) AI” was introduced by the American philosopher John Rogers Searle (Kornienko & Sorokina, 2018). A strong AI is a program that exhibits behavior similar to what humans have. Accordingly, artificial superintelligence (ASI) will have intellectual capacities that far surpass those of humans in a wide range of categories and areas of activity. According to Nick Bostrom (2016), a superintelligent artificial system will possess “intelligence that is far smarter than the best human brains in almost all areas, including scientific creativity, wisdom, and social skills” (pp. 84–85). Bostrom says that there will hardly be any problem that the superintelligence will not be able to solve, or at least help us solve. Diseases, poverty, environmental destruction, problems of all kinds can be solved in a moment by superintelligence with the help of nanotechnology. Also, superintelligence can give us unlimited life, stopping and reversing the aging process using nanomedicine. Superintelligence can help us to create a world in which we will live in joy and understanding, approaching our ideals and regularly realizing our dreams (Bostrom, 2016; Diyun & Seredinskaya, 2017). Supporters of the weak (narrow) AI consider programs as a tool that can be used to address a particular problem, but it is not able to think like a human. The famous British scientist Alan Matheson Turing published his work “Computer Machinery and Intelligence” in 1950. He posed the question: “Whether machines can perform actions that are indistinguishable from reasoned ones.” According to the author, this is quite possible. And all his work is devoted to the proof of this theory, as well as the denial of opposing views. Turing provides many variants of such proofs, and all of them are presented in the form of tests. The initial and main stage in this work is the so-called “The Imitation Game.” There are three participants in it: a man,

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a machine, and an examiner. All of them are placed in separate rooms and communicate with each other via teletype (or intermediary). And everyone, except for the examiner, is trying to prove to him that he is a human. As it is impossible for the examiner to determine who is who, it is believed that AI wins. This test has different options. As cognitive psychology expert and philosophy Professor Stevan Harnad points out, “Turing’s question has sounded like this “Can machines do what we can do as thinking beings?” (Harnad, 2008). However, passing the Turing’s test cannot be a criterion that the machine possesses the ability to think. The emergence of consciousness in machines requires the presence of physical and chemical processes, similar to those that occur in the human brain. Many modern AI researchers have taken analogous positions. Strong AI really does not exist yet. Almost all achievements of the last decade in the area of AI have been advances in the development of application systems. These technological advancements cannot be underestimated, as application systems in some cases are able to solve intellectual problems better than universal human intelligence does. Narrowly focused AI is superior to human one in certain activities or operations. A computer with a narrowly focused AI is able to beat the world chess champion, park a car, or find the most relevant search results in the search system (Lyushnina, 2014). Therefore, the supporters of the weak AI are right so far, and they express the view that intelligent machines are only able to solve some of their tasks and are not able to have a full range of human abilities. The English physicist and mathematician Roger Penrose is among them (Penrose, 2003). It can be stated that for some researchers AI is the ability to solve complex problems, others understand AI as the ability to learn, generalize, analytical thinking, and still others consider AI as the ability to interact with the outside world through communication, transmission, and conscious perception of information. That is, the understanding of intelligence in its various manifestations is absolutely different. Epistemology, the science of knowledge and intellect within the framework of philosophy, is called to resolve these disputes. Here, philosophers work to help engineers creating AI in understanding how to better perceive, represent, and use knowledge and information (Yaroslavtseva, 2017). Every breakthrough technology has advantages and disadvantages. And AI is no exception. The advantages of AI are the following: 1. The ability to instantly remember information and process a huge amount of it in the shortest possible time. In order to develop a thorough knowledge and not to forget it, it is necessary for a person to repeat information within 3–4 days, and then at least once in

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6 weeks to refresh it in memory, if only indirectly. Artificial intelligence, in contrast, can remember any amount of information easily and forever. 2. Incredibly fast processing of quantitative data. While the person adds two double-digit numbers, the computer will analyze the economic situation and identify a point on the chart, which is best to buy currency. And then he will conclude this transaction and will leave the market in time, leaving his master with a profit. A trader cannot process this amount of information. Let us consider the disadvantages of AI: 1. Artificial intelligence is not yet able to process high-quality information, but it is only a matter of time. 2. Artificial intelligence can still fail, so a person has to monitor it. But in a few decades, AI can learn to see their failures, repair them, and people’s assistance will not be needed. 3. There is a problem of malicious use of AI and machine learning by both governmental and nongovernmental organizations. Artificial intelligence allows attackers to impersonate other people, imitating their handwriting, voice and manner of communication, providing them with an unprecedented power tool that can be used in illegal acts. It should be borne in mind that over time the number of disadvantages of AI will be less and less. What is important is that we can identify the challenges imposed by AI and recognize our responsibility to be sure that we can take full advantage of it and minimize the negative consequences. There are several scenarios for the development of AI: 1. Sooner or later, the AI’s intelligence will be so perfect that it can neither be deceived nor hacked. But it can act in an aggressive manner against a human. As soon as the soulless machine becomes self-aware, it will actually become a man, only much more skillful. And if there is a conflict with this device, the consequences will be very sad. E. Musk, S. Hawking, B. Gates believe that AI is an existential threat to humanity comparable in its scale to the complete extinction of us as a species. 2. Machines will do everything for a man. 3. Humanity can create a machine that will define and solve global problems. And it is possible that after analyzing a lot of data, the robot decides that man himself is the cause of all the problems of civilization. And, naturally, he could decide to destroy the cause, namely, the human race.

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4. The problem of unemployment, which is already gradually becoming more and more severe, not only for conveyor production, but also for quite “smart” professions. Thus, only a couple of traders is sufficient for most of the world’s banks because all the rest of the work on market analysis and even the conclusion of profitable transactions for the purchase or sale of currency or securities is performed by robots. 5. Only those people who serve AI, for example, programmers, will be in demand. But over time, they will become unnecessary, because AI will develop so quickly that even the programmer could not know how to control AI’s code. Artificial intelligence is progressing at full speed and it is possible that the scenarios described here will occur. But there is a gap between strong and weak AI. In order to bridge this divide, it is not enough to increase the computer power, it is necessary to endow them with reason. Scientists still do not see a clear way to do it. The problem of human–AI interaction acquires new features in the context of Internet theory, forms of virtual communities and communication. AI, technosphere in general, global integration processes, InfoSphere are the layers that will reformat the space of everyday human experience (Ioseliani, 2016). Among the problems that humanity will face in the near future, we can note the problem of the need for psychological adaptation of a person to an environment where mass computer literacy is required. These phenomena will inevitably change the spiritual life of society, thinking, way of life (Ioseliani, 2018). There will be different “habitat.” With the new InfoSphere structure, the existing picture of the world will be gradually modified, and a new information model of existence will be formed. Social life can be significantly affected by intellectual systems which are capable of replacing a person in extreme conditions when interacting with the environment. Robotics will be able to work instead of a person wherever there are no conditions in which a person could work, for example, in conditions without an oxygen environment, unacceptable temperature conditions for a person, in spaces without lighting, and so on. Thus, instead of constructing many buildings that complicate the infrastructure of settlements, it is possible to build structures underground. French researcher J. Ellul (1986) is concerned about the destiny of mankind. He writes: We have come to the fork of the historical path, to the place of possible intersection between free socialism and cybernetization of society. The case is not lost yet. The main thing is that the world of informatics, even in the most in-

20    A. D. IOSELIANI and N. V. TSKHADADZE nocent and non-Machiavellian way, will not become an agent of the technical system, culminating its evolution to concentration, to all penetrating control. When this cybernated government “sets” as the icy sludge or concrete sets, that is, strictly speaking, it will be too late. (pp. 148–149)

The technosphere is developing rapidly, accelerating its transformation into a new environment—the InfoSphere, in which the main value is the information transmitted, produced and converted into a product through high technology. At the same time, a new reality is emerging, changing values, norms and priorities of human civilization. According to K. Haefner (1994) and other scientists, it is possible to overcome the above dangers by forming a computerized society in a humane way, where the relationship between AI and humans is well conceived. CONCLUSIONS Artificial intelligence as a social phenomenon, its place and role in social life is treated differently by different scientists, and accordingly various scenarios of its development are constructed. AI is developing rapidly, and, in the future, one of the scenarios described in the chapter may become real. However, to date, the human mind is many times superior to powerful computers. Intellectual systems can influence and radically change social life, but the question is what kind of influence it is and the extent to which a human, as a thinking being, is protected. Technical civilization dictates its values and priorities. One of its main priorities is a new social space, a new type of communication, other conditions of social life, which determines the priorities of competition, rivalry, and benefits. A new social reality, encompassing high technologies, information, globalization, creates a new person, a new type of human activity. This new type of activity integrates pragmatism and utility with professionalism, sense of duty, and responsibility. REFERENCES Averkin, A. N., Haase-Rapoport, M. G., & Pospelov, D. A. (1992). Explanatory dictionary of artificial intelligence. Moscow, Russia: Radio and Communication Research Institute. Bostrom, N. (2016). Artificial intelligence. Stages. Threats. Strategies. Moscow, Russia: Publishing house “Mann, Ivanov, and Ferber.” Diyun, M. S., & Seredinskaya, L. A. (2017). Artificial intelligence and superintelligence: Existential risks. Philosophical Descript, 18(1), 8–12.

Socio-Philosophical Understanding of Artificial Intelligence    21 Ellul, J. (1986). Another revolution. In P. S. Gurevich (Ed.), New technocratic wave in the West (pp. 147–158). Moscow, Russia: Progress. Haefner, K. (1994). Mensch und computer im Jahre 2000 (Man and computer 2000). Boston, MA: Basel-Boston-Stuttgard. Harnad, S. (2008). First scale up to the robotic turing test, then worry about feeling: Artificial intelligence in medicine. Oxford University Press, 44(2), 83–89. Ioseliani, A. D. (2018). The anthropology of man-made world. Perm, Russia: Science. Ioseliani, A. D. (2016). The ontology of modern techno- and sociosphere: Part 2. Gramota, 3(65), 62–64. Kornienko, V. V., & Sorokina, V. V. (2018). Artificial intelligence systems. Student. Postgraduate. Researcher, 5(35), 336–341. Lyushnina, D. G. (2014). Artificial intelligence: A response or a challenge? Bulletin of Medical Internet Conferences, 4(11), 11–21. Penrose, R. (2003). The king’s new mind: About computers, thinking, and the laws of physics. New York, NY: Editorial URSS. Petrunin, Yu. Yu, Ryazanov, M. A., Savelyev, A. V. (2010). The philosophy of artificial intelligence in the concepts of neuroscience. Moscow, Russia: MAKS Press. Trapeznikov, M. V., Bryndza, M. G., & Matasova, M. A. (2017). Artificial intelligence. Youth Scientific Bulletin, 10(23), 97–101. Turing, A. (1950). Computing machinery and intelligence. Mind LIX 236(1), 433–460. Yaroslavtseva, M. M. (2017). The value of the theory of consciousness in the formation of artificial intelligence. Gagarin Readings 2017: Proceedings of abstracts, 1192–1193.

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CHAPTER 3

PHILOSOPHICAL AND EPISTEMOLOGICAL FACTORS OF ARTIFICIAL INTELLIGENCE Tair M. Makhamatov Financial University Timur T. Makhamatov Financial University Saida T. Makhamatova Financial University

ABSTRACT The authors substantiate the thesis that the further development of artificial intelligence is closely connected not only with new discoveries in the field of natural sciences, but also with achievements in the field of philosophy of knowledge and cognitive sciences. The comparative analysis of the research on the neural network as the core of modern artificial intelligence shows that the principle of its functioning corresponds, first, to the principles of sensory cognition, that is, sensualism, studied in detail by Locke (1985), and, secondly, Kant’s (1994) apriorism. Structural analysis of historical and philosophical

Meta-Scientific Study of Artificial Intelligence, pages 23–29 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

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24    T. M. MAKHAMATOV, T. T. MAKHAMATOV, and S. T. MAKHAMATOV studies of the stages of the cognitive process, the system approach, methods of dialectics, genetic method, synthesis, and deduction allowed the authors to come to the following conclusion: the transition of the neural network to a qualitatively new level, determining a breakthrough in the development of artificial intelligence, is possible on the principles of “synthetic unity of apperception,” Kant’s (1994) “I think” and the formation of the ability to create antinomies of intelligence in the neural network. Such properties of the neural network as “evidence of the answer,” “classification of images,” and “reliability of the decision” can be improved on the basis of the theories of knowledge of the above philosophers, as well as T. Hobbes, R. Descartes, B. Spinoza, and G. W. F. Hegel, as well as the results of modern cognitive sciences.

Technological revolutions, which are a determining factor in the qualitative and quantitative growth of modern advanced economies, received its initial impetus from “the emergence and spread of a certain type of thinking—it is a “constructive thinking” based on schemes” (Volovik & Shchedrovitsky, 2018). In our time, the growth of international competition has led to the transition to the wider practical application of artificial intelligence, based on the principles of constructive thinking, which objectively requires the improvement of artificial intelligence. The development and expansion of the scope of artificial intelligence generate positive and negative consequences in various spheres of society, as is evident from the work of such authors as, for example, Florida (2011), Barrat (2015), and others. Taking into account the various social, moral, and ethical consequences, we must take into account the words of Jaspers (1994) that “since technology itself does not set goals, it takes place beyond good and evil or precedes them,” because “always remains a means.” METHODOLOGY The main purpose of artificial intelligence is to facilitate human mental work. The basis of its creation and development are repetitive actions of human mental labor. Recently, however, the development of artificial intelligence has moved to a qualitatively new stage. Its improvement is no longer limited to universal forms of mental activity, the study of the nature of the human brain, but is already based on the features of creative, constructive human thinking (Volovik & Shchedrovitsky, 2018), memory (Hoskins, 2016; Rielf, 2016), philosophical and epistemological abilities (Lectorsky, 2008) and psychology of cognition and thinking (Harré, 1984; Harré & Gillett, 1994). In his fundamental work, Neural Networks: A Complete Course, Simon Khaikin (2006) writes that “the subject area of neural networks lies at the intersection of many sciences. It has its roots in neurobiology, mathematics, statistics, physics, computer science, and engineering” (p. 989).

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In our view, the further development of artificial intelligence, especially its modern core—neural networks—should use the achievements of John Locke’s sensational theory of knowledge, Kant’s (1994) apriorism, which still remain outside of the field of both philosophers and researchers of artificial intelligence. RESULTS John Locke’s Theory of Primary and Secondary Qualities and Current Problems of Neural Networks Studies of the mechanism of functioning of the human brain and neural network as the core of artificial intelligence show that the initial step of the cognitive process is based on the principles of sensationalism. According to Khaikin (2006), Most of the efforts of neural network researchers were focused on the problem of pattern recognition. Considering the practical importance of this problem and its widespread nature, as well as the fact that neural networks are well suited to solve the problem of classification, such a concentration of efforts of scientists was directed to the search for means of correct classification. Developing this direction, it became possible to lay the foundations of adaptive pattern classification. However, we have reached the point where classification systems should be considered in a broader sense if we want to solve the problems of classification of a more complex and intellectual nature. (p. 990)

According to John Locke (1985) the term reflection implies “that observation to which the mind directs its activity . . . resulting in ideas of this activity which arise in the mind” (p. 155). Sensory experience gives the mind knowledge of such qualities of objects as density, length, form, motion or rest, and number, which J. Locke calls primary and real qualities. The ideas of these “qualities of objects are similar to them, and their prototypes really exist in the objects (Locke, 1985, p. 186). Such “qualities as colours, tastes, sounds, etc., which in fact do not play any role in the objects themselves, but represent the forces that cause us different sensations by the primary qualities of things, i.e., the volume, shape, structure and movement of their invisible particles,” he calls secondary qualities (Locke, 1985, p. 184). The ideas caused in the man by these qualities, does not have similarities with the objects. Light, heat, whiteness, or coldness are real in objects, no more than discomfort or pain in manna. Stop these sensations. Let your eyes not see light or flowers, let your ears not hear sounds, let your palate not feel taste, let your nose not smell—and all colours, tastes, smells and sounds as spe-

26    T. M. MAKHAMATOV, T. T. MAKHAMATOV, and S. T. MAKHAMATOV cial ideas will disappear, will cease to exist and will be reduced to their reasons, i.e., to the volume, form and movement of particles. (Locke, 1985, p. 187)

These arguments are very close to the problem, about which Khaikin (2006) writes: In the context of the problem of classification of images, it is possible to develop a neutron network that collects information not only to determine a particular class, but also to increase confidence of the decision. Subsequently, this information can be used to exclude questionable decisions. (p. 35)

The objectives of classification, if we start from Locke’s ideas of primary and secondary qualities, are closely related to the development of mechanisms for detecting the interdependency of these qualities of objects by neural networks of artificial intelligence. Only in this case we can say that “the use of neural networks provides . . . the evidence of the answer“ (Khaikin, 2006, p. 35). Another important problem of artificial intelligence, in our opinion, is the question of its creative constructivism. Such researchers of artificial intelligence and its modern core, neural networks, as Barratt, Jones, Kurzweil, Bernard, and Casesin (Barnard & Casasen, 1991) write about the possibility of creating a self-developing artificial intelligence that will soon surpass man. Only a few scientists, including Simon Kheikin, write about the margins of artificial intelligence capacity, which is determined by the total human intelligence. “It is very important to understand that to create a computer architecture that will be able to imitate the human brain (if this is possible at all), S. Khaikin points out, we still have a long and arduous road ahead” (Khaikin, 2006, p. 33). On the basis of “computer architecture,” which is “able to imitate the human brain,” is, along with the principles of sensationalism, Kant’s (1994) apriorism. Kant’s Epistemological Constructivism and Prospects of Neural Networks Development Software of artificial intelligence, using the language of philosophy, is essentially its aprioristic conceptual structure. Its framework defines the boundaries of all possibilities of artificial intelligence, including the ability to determine the object of operational contact, that is, the ability of epistemological design of the object. “Aprioristic information and invariants should be built into the structure of neural networks, which simplifies the network architecture and learning process” (Khaikin, 2006, p. 33). To perform such a task “it is necessary to understand how to develop a specialized structure

Philosophical and Epistemological Factors of Artificial Intelligence    27

in which aprioristic information is embedded. Unfortunately, there is currently no clear solution to this problem” (Khaikin, 2006, p.  62; see also: Barnard & Casasen, 1991, pp. 9–12). Here, we see the similarity with the process of formation in the mind of the cognizing subject “a phenomenon,” which is the result of the connection of sensory data with aprioristic concepts of mind, as Kant (1994) wrote in his The Critique of Pure Reason. As is well known, in our time, Kant’s theory of knowledge, especially his doctrine of the phenomenon, is reasonably regarded as epistemological constructivism (Rockmore, 2016; Lectorsky, 2008). Kant’s concept of phenomenon from the standpoint of the logic of scientific knowledge “is nothing but a fact of knowledge constructed by means of aprioristic concepts. [. . . ] Natural scientists have understood,” writes Kant (1994), that mind sees only what it creates on its own plan, that it must go ahead with the principles of its judgments, according to constant laws, and force the nature to answer its questions, but not to follow behind it, as otherwise, those observations that were made accidentally, without advance planning, will not be linked by the necessary law, while the mind always looks for such a law and needs it. (p. 16)

Analyzing Kant’s understanding of the philosophy of knowledge, German Neo-Kantian philosopher E. Cassirer (2011), in “Phenomenology of Knowledge” found in the 3rd volume of his work Philosophy of Symbolic Forms, highlights Kant’s epistemological constructivism. Cassirer writes: Knowledge is not described either as a part of reality or as a reflection of it. Nevertheless, its correlation with the reality does not diminish at all, but rather receives its justification from a new point of view. The function of knowledge is the construction and constitution of the object—no longer absolute, but conditioned by this function as a “manifested object.” What we call “objective” reality, the subject of experience, is possible only in the presence of consciousness and its aprioristic unifying functions. (p. 14)

The formation of the neural network learning process should be viewed through the prism of two epistemological goals. The first goal: To program the principle of “I think” in the structure of the neural network, that is, self-consciousness and its connection with perceptions–contemplations of the neural network, which could ensure their unity. Kant (1994) wrote that “all that is manifold in contemplation has, therefore, the necessary relation to [judgment] I think in the very subject in which this manifold is” (p. 100). The principle of “I think” provides the unity and synthesis of all ideas, as well as “the possibility of aprioristic knowledge on the basis of this unity” (Kant, 1994, p. 100).

28    T. M. MAKHAMATOV, T. T. MAKHAMATOV, and S. T. MAKHAMATOV

The second goal: To program in the neural network the ability to think about “things in themselves” and to form antinomies, which means going beyond actions in the space of direct perception and the possibility of creative thinking. The concept of “thing in itself” expresses Kant’s attempt to move to the epistemology of theoretical knowledge and demonstrates the existence of a qualitative difference, a deep border between the sensory and rational levels of cognition. Kant notes that the cumulative accumulation of experimental knowledge cannot take knowledge to the rational level. Therefore, the “thing in itself” acts as a criterion of absolute limitation of our sensory perceptions, experience. CONCLUSIONS The analysis of the achievements and actual problems of neural networks as the core of modern artificial intelligence shows that many problems discussed in this area have long been studied in the theories of cognition. In the present chapter, the authors have touched upon only the closest to the problems of neural networks, aspects of teachings of John Locke and Immanuel Kant. A more detailed and in-depth study of the problems of artificial intelligence in the dialectical relationship with the achievements and problems of gnoseology and epistemology will give fruitful results to both developers of neural networks and philosophers studying the dialectics of the cognitive process. REFERENCES Barratt, J. (2015). The Last invention of mankind: Artificial intelligence and the end of the era of Homo Sapiens (Translation from Russian). Moscow, Russia: Alpina Non-Fiction. Barnard, E., & Casasen, D. (1991). Invariance and neural nets. IEEE Transactions on Neural Networks, 1(2), 498–508. Cassirer, E. (2011). Philosophy of symbolic forms. T. W: The Phenomenology of knowledge (Translation from German). Moscow, Russia: Academic Project. Florida, B. (2011). Creative class: People who change the future. Per. with English. Moscow, Russia: Classics-XXI. Harré, R. (1984). Personal being: A theory for individual psychology. Harvard, MA: Harvard University Press. Harré, R., & Gillett, G. (1994). The discursive mind. London, England: SAGE. Hoskins, A. (2016). Memory ecologies. Memory Studies, 3(1), 348–357. Jaspers, K. (1994). Meaning and purpose of the history, 2nd edition (Translation from German). Moscow, Russia: Republic.

Philosophical and Epistemological Factors of Artificial Intelligence    29 Kant, I. (1994). The critique of pure reason (Translation from German). Moscow, Russia: Thought. Khaikin, S. (2006). Neural networks: A complete course, 2nd edition. Moscow, Russia: I. D. Williams. Lectorsky, V. A. (2008). A team of authors. Cognitive approach. Scientific monograph. Moscow, Russia: Canon+ ROOI Rehabilitation. Locke, J. (1985). An essay concerning human understanding: Works in 3 Volumes, Vol. 1. Moscow, Russia: Mysl’. Rielf, D. (2016). In praise of forgetting: Historical memory and its Ironies. New Haven, CT: Yale University Press. Rockmore, T. (2016). German idealism as constructivism. Chicago, IL: The University of Chicago Press. Volovik, V. V., & Shchedrovitsky, P. G. (2018). Constructive thinking: The unaccounted factor in the development. Retrieved from https://ras.jes.su/vphil/s207987 840000591-9-1

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CHAPTER 4

RESEARCH PHILOSOPHY AS TOOLS OF CROSSDISCIPLINARY FRAMING Alexander M. Starostin Rostov State University of Economics

ABSTRACT Differently directed philosophical discourses (academic, diatribic, and research) are being analyzed in the chapter on the basis of the scientific results achieved in the frame of various case studies. Academic discourse is generally aimed at professionals studying the field and fundamental philosophy development. Diatribic discourse focuses on general learning and popularization of the subject. Research discourse comprises application and development layer in the field of philosophy (philosophical innovation studies), and theoretical layer (philosophical diatropics). Reference to research and philosophical discourse leads to the practical implementation of interdisciplinary scientific platform (framing) based on the integrity of the interdisciplinary studies. The problem of artificial intelligence can be used as an example of a diversified interdisciplinary framing.

Modern methodological and knowledge engineering researches in different spheres of the humanities (political science, law, sociology, psychology,

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32    A. M. STAROSTIN

etc.) reveal the necessity of algorithm and procedure reevaluating, which determines interdisciplinary reflexion. One of advanced research directions is in shifting from multiple research case studies to their generalization and conceptualization in the terms of methodology. We dare to suggest general methodological representation of interdisciplinary research procedure in the frame of the humanities grounded on the achieved results in a number of case studies (political philosophy, global studies, elitology, etc.). For example, in the case study, Political Philosophy, substantial variations in the generalized forms of political representation of the world can be traced in accordance with philosophical representation of analytical tools. METHODOLOGY This aspect of analysis demonstrates a variety of the ways in political and philosophical representation. For instance, structuring the field of political philosophy can be conducted through fundamental philosophy structure overlaid on the theoretically problematic area in politics. Such approach, which is very common, is found in the work Political Philosophy (Vasilenko, 2004), in which the scientist differentiates four sections: political ontology, political anthropology, political praxeology, and political epistemology. Lebedeva (2009) suggests a different concept and states, Political philosophy is a theoretical issue that claims to be a philosophical generalization of politics, which reveals its content, character, forms, political activity regularities, essence of power, political choice; develops the norms of correct political order and optimal organization of political power; defines internal logic of the development of politics, its interconnections with other spheres of public life. (p. 200)

In the works of Alexeeva (2007), as well as Shahai and Yakubovskiy (2011), value- and worldview paradigm (socialism, liberalism, conservatism, etc.) is used as the authors’ starting point for further interpreting the basic politological concepts (state, authority, freedom, equality, justice). Neretina and Ogurtsov (2011) and Makarenko (2011) make attempts to differentiate and operationalize fundamental political concepts via different epistemological discourses. RESULTS We can suggest the following definition: political philosophy is an applicable level of fundamental philosophy dealing with the field of political

Research Philosophy as Tools of Cross-Disciplinary Framing    33

realities, political picture (Danilenko & Danilenko, 2012) and containing information on cognitive processes active while forming this picture. In political philosophy main levels of political reality based on definite cognitive types are represented and a strategy of political activity is also rooted in these types. Let’s deal with some metaphilosophical issues in detail. To characterize the process of modern philosophy development from the metaphilosophical point of view we shouldn’t ignore the fact that it rests on several discourses: academic or fundamental, diatribic, and research. The latter in our view is a synonym to applied researches and scientific developments. Popper (M. Academic Center, 2010, pp. 12–13) was absolutely fair saying that truly philosophical problems always root in other problems beyond the field of philosophy, and philosophy will die if it loses this ground. Outstanding Norwegian philosopher Lars Swendsen (2018) notes absolutely correctly: The process of search for solutions and new views on the issues, which are especially interesting for only philosophers, is very important in modern philosophy; it’s the way to build academic career and gain recognition. Urgency of philosophy for the surrounding reality is of no importance. When philosophy was turned into a career it became more technically advanced, but at the same time very dull and lifeless. (p. 180)

As for the above mentioned diatribic tradition, it implies formation of subcultural philosophical context, which helps to make first steps on the career path in academic philosophy (philosophical propaedeutics in professionalization), as well as, and more than that, to create semantic and content area for comprehending philosophy, to “consume philosophy” by professionals in other areas of expertise. For more exact definitions it is essential to refer to Prof. A. V. Potemkin (2003), who was the first to suggest and develop the problem of philosophical diatribes, and introduced this term in the field of philosophy just in the 1970s, saying: In ancient Greece and Rome the word “diatribe” meant a talk between a teacher and a pupil, a student’s synopsis, and also a country road, dust-laden because of donkeys. Later, over time this word acquired a number of new meanings. (p. 9)

In simpler words it is a scholastic tradition which implies elementary (preprofessional) knowledge. “Starting from now let’s call everything associated with scholastic form of philosophy: personalities, textbooks, reference books, procedures—briefly ‘diatribics,’ and a new tradition in understanding the specific character of philosophy a ‘diatribic tradition.’” (p. 20)

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Diatribic tradition revolutionizes, especially nowadays. In the academic aspect in the end of the 1980s/beginning of the 1990s a gradual distancing from Marxist philosophy in our course books became evident. At first it meant ideological purge from judgmental interpretations of social, economical, and political issues. And then, due to newly introduced norms and standards the syllabus was enriched by numerous ideas from non-Marxian theories, including traditional Russian philosophy. These changes emerged in all the vertical structure of philosophical education: from tertiary education to postgraduate courses. At present, diatribic tradition evolves and tilts towards applied philosophical concepts (political philosophy, philosophy of education, philosophy of machinery, philosophy of computer science, philosophy of management, philosophy of medicine, etc.; Gubin, 2014; Ivin, 2000; Ivin, 2016; Chumakov, 2017), which converges philosophical science with specialized professional training and provides a wider insight into various spheres of public life, methodological and world outlook of a future professional, a beginning scientist, a constructor, a manager. At the same time in the course of this process the idea about multi-explanatory fundamental and philosophical base that makes it possible to use philosophy in different scientific aspects is implicitly and purposefully formed (in philosophy of science, philosophy of life and existentialism, hermeneutics and phenomenology). Nevertheless, such tendencies as well as methodology of philosophical development study are just germinating. Diatribic tradition performs the function which is somehow analogous to communication and intermediation activity of a universal language (e.g., the English language). This diatribic bias is observed both at the academic level and the applicable one. Attempts to schematize and simplify, even to the primitive terms, is also common. We thoroughly observed diatribic tradition in philosophy, including its applicative segments, taking into consideration that in the history of natural sciences (physics, chemistry, biology, astronomy) and the humanities there are quite a lot of examples (cases) of reference performed by outstanding scientists (Einstein, Bohr, Heisenberg, Amburtsamyan, Kedrov, Vernadski, Vygotski, Prigozhin, Vigner, Migdal) to the analysis of the scientific and academic fields of their interest, in which they obtained expertise, in philosophical and methodological terms, that were reconstructed on the basis of diatribic sources. A well-known Russian physicist academician A. B. Migdal published a detailed article on the issue in the Journal Philosophical Issues (Migdal, 1990). Modern diatribic issues show a tendency for the creation of a convergent philosophical concept, which could comprise, reveal, and reflect the most important ideas, principles, debatable topics characteristic to main philosophical systems. In other words, an attempt should be made to develop a

Research Philosophy as Tools of Cross-Disciplinary Framing    35

new philosophical and worldview paradigm based on the variety of philosophical universals. In this regard, it is possible to identify at least three trajectories of innovative development of philosophy: (a) philosophical innovation (studies), (b) philosophical diatropics, and (c) philosophical alternative studies. Briefly about it: Expansion of the field of philosophical applications and developments occurs through comprising philosophical issues and new knowledge, the sources of which are outside the modern system of fundamental Philosophy. This is a well-known. Popper’s postulate, to which we have already referred. Thus, philosophical innovation is the most dynamic part of the system of research Philosophy; it is rapidly expanding new areas of philosophical experience, the generalization of which allows us to reach new frontiers of fundamental philosophical knowledge and ideas. (M. Academic Center, 2010, pp. 12–13)

We adhere to the concept of the possibility of operationalization of philosophy from its fundamental level to the applied one, calling this concept philosophical innovation. Within its framework, a system-comparative representation of philosophical activity is developed. This work has been carried out since 2009 under the guidance of the author in the framework of the program “Applied Philosophy as Philosophical Innovation” (Starostin, 2015, p. 7) implemented in a series of international conferences and round tables and related publications (published more than 20 collections of research papers, 10 monographs. Our established community of authors includes more than 200 authors from different countries; Starostin, 2009, Starostin 2012, Starostin 2014, Starostin, 2015). In modern society, the attitude of philosophers to applied philosophy is ambiguous. On the one hand, the question about this application within such a holistic system of worldview as philosophy is debatable. Such applications are usually the results of knowledge development in specific sciences, coming from a particular picture of the world and guided by philosophical methodology. But, on the other hand, the analysis of philosophers’ “products” shows that more than 90% of them carry out the processes of “generalization” of the data on these specific sciences, or, most often, they represent new interpretations and comments of the fundamental philosophical knowledge already acquired long ago. New fundamental systems and breakthroughs in philosophy are very rare. Meanwhile, the demand for applications of philosophy to various fields of science and practice is growing rapidly. These are philosophy of science, philosophy of politics, philosophy of education, and dozens of other applied philosophies. To concretize the above said, we emphasize that the sphere of intellectual and practical developments of an interdisciplinary nature, carried out

36    A. M. STAROSTIN

with the help of tools of philosophical reflection, should be called philosophical innovation. The need to define this area of activity by the term philosophical innovation is primarily related to the need for more accurate qualification of this activity—it is not the sphere of fundamental philosophy, and not even always its applied part in the form of philosophy of science, philosophy of politics, or philosophy of religion, but the solution of, perhaps even more specific and acutely relevant interdisciplinary problems. On the other hand, it is not actually philosophical (in the sense of fundamental) activity, but a kind of intellectual activity performed by philosophical analytical means. The background to this in Russian philosophy was laid in the 1950s to 1970s of the 20th century in search of innovations, jointly conducted by creative philosophers, teachers and psychologists, scientists, naturalists, designers, engineers: Ilyenkova and Zinoviev, Shchedrovitskiy, and Mamardashvili, Davydov and Galperin, systems analysts, cosmists, synergists, global ecologists, philosophy of science, the human science, which result in a large scale scientific and design complexes of interdisciplinary nature, actively put into practice. And here it is essential to reflect on the algorithms of innovative processes that affect philosophy. All this fits into a nonlinearly developing variety of philosophical systems that evolve on the principle of homological series, forming a diatropic world of philosophy (diatropics—the study on diversity and its laws; Chaikovskiy, 1992). A significant body of literature is devoted to the reflection of this world in the diatropic and innovative perspective, and these works focus on the panorama of the development of modern philosophy. We will not dwell on this topic. We just emphasize that there are certain intersections with philosophical innovation in the aspect that affects the interaction of fundamental and applied knowledge in essentially different philosophical systems. Formation of cross-philosophical images, worldview paradigm, instrumental approaches to the world and the human seem to be the problems of urgent importance. Analogically Philosophical Alternative Studies can be understood with mathematical, physico-theoretical, and cosmological constructs coming from different axiomatics. For example, an analogue of the family of nonEuclidean geometries can be non-geocentric ontologies, the development of which started with the onset of physical and cosmological cognition of micro- and megaobjects in the 20th century. New existential experience required researches in the field of Alternative Ontology, which includes ontological worlds with other than macroterrestrial, attributive characteristics: non-Euclidean space, non-Newtonian time, non-Leibnizian structure (the whole, including parts and vice versa),

Research Philosophy as Tools of Cross-Disciplinary Framing    37

non-Archimedean quantity, and so on (Branskiy, 1973; Kulakov, Vladimirov, & Karnaukov, 1992; Vladimirov, 2007, Yugai, 2007). With regard to the development of researches in the field of artificial intelligence, the intellectual manifestations in the life of animals, as well as the advances in solution of the CETJ and SETJ problems (extraterrestrials, extraterrestrial intelligence, and contact with it) on the basis of an interdisciplinary area of cognitive science, as an element of philosophy, a new school of thought occurred—alternative cognitive science that also develops in the frame of the philosophical alternative science, which is also a kind of research philosophy. It should be emphasized that in recent years with the process of distancing from the monopolistic claims of the social philosophy of Marxism in the frame of domestic science, the development of alternative social philosophy and philosophy of history began to gather quick momentum (Korotaev, 2009; Kradin, 2000; Pereslegin, 2012; Turchin, 2007). As for the alternative axiology, we should note at once that we mean the analysis and generalization of various axiological systems formed within the framework of the modern process of globalization and making active claims to influence and self-assertion in the modern world (Starostin, 2011). CONCLUSIONS Summarizing the above written, we emphasize the importance of the movement according to the scheme: sectoral (disciplinary) case study, generalized multiparameter system of research philosophy, a return to the case study at the level that includes interdisciplinary connections with other case study and alternative options. This allows you to maintain the atmosphere for further research, not giving way to absolute priority given to earlier emerged ideas and concepts, and, especially, to exposing them to ideologizing and even mythologizing. Eventually, it is this algorithmization that allows you to create platforms for interdisciplinary framing. REFERENCES Alexeeva, T. A. (2007). Political philosophy: From concepts to theories. Moscow, Russia: Rosspen. Branskiy, V. P. (1973). Philosophical ground of the problem of synthesis with relativistic and quantum principles. Leningrad, Russia: University of Leningrad. Chaikovskiy, Y. V. (1992). Cognitive models, pluralism, survival. International Philosophical Journal Put, 1(1) 62–108. Chumakov, A. N. (2017). Practical philosophy. Moscow, Russia: Prospect.

38    A. M. STAROSTIN Danilenko, V. P., & Danilenko, L. V. (2012). Evolution in spiritual culture: Prometheus’s light. Moscow, Russia: Krasand. Gubin, V. D. (2014). Philosophy. Moscow, Russia: Prospect. Ivin, A. A. (2000). Philosophy of history. Moscow, Russia: Gardariki. Ivin, A. A. (2016). Philosophical researches in Science. Moscow, Russia: Prospect. Korotaev, A. V. (2009). History and Synergetics: Methodological researches. Moscow, Russia: Librokom. Kradin, N. N. (2000). Alternative ways to civilization. Moscow, Russia: Logos. Kulakov, Y. I., Vladimirov, Y. S., & Karnauhov, A. V. (1992). Introduction into the theory of physical structures and binary geometrophysics. Moscow, Russia: Arhimed. Lebedeva, T. V. (2009). History and philosophy of politics. In D. S. Klementiev (Ed.), History and philosophy of science, Volume 3 (pp. 200–287). Moscow, Russia: Moscow State University. M. Academic Center. (2010). Philosphy of science. Moscow, Russia: Author. Makarenko, V. P. (2011). Political concept studies. Rostov-on-Don, Russia: Southern Federal University. Migdal, A. B. (1990). Physics and philosophy. Journal Philosophical Issues, 1(1), 10. Neretina, S. S., & Ogurtsov, A. P. (2011). Concepts of political structure. Moscow, Russia: IFAN. Pereslegin, S. (2012). Hidden history of WW2: New view of the war between realities. Moscow, Russia: Eksmo. Potemkin, A. V. (2003). Metaphilosophical diatribes on the shores of the Kiziterinka. Rostov-on-Don, Russia: Rosizda. Shahai, A., & Yakubovskiy, M. (2011). Philosophy of politics. Kharkov, Russia: Humanitarian Centre. Starostin, A. M. (2009). Philosophical innovations: Concept and main spheres of manifestation. Rostov-on-Don, Russia: SKAGS. Starostin, A. M. (2011). New subjectivity understanding in different conceptual fields. Rostov-on-Don, Russia: SKAGS. Starostin, A. M. (2012). Philosophical innovations in cognitive and praxeological context. Moscow, Russia: URSS. Starostin, A. M. (2014). Summa philosophiae in applied dimension. Rostov-on-Don, Russia: Donizdat. Starostin, A. M. (2015). Applied philosophy as philosophical innovation. Rostov-on-Don, Russia: SKAGS. Swendsen, L. (2018). Philosophy of philosophy. Moscow, Russia: Progress-Tradition. Turchin, P. V. (2007). Historical dynamics: On the path to theoretical history. Moscow, Russia: LKI. Vasilenko, I. A. (2004). Political philosophy. Moscow, Russia: Gardariki. Vladimirov, Y. S. (2007). Metaphysics: XXI century, Almanac, No. 2. Moscow, Russia: Binom. Yugai, G. A. (2007). Holography of the universe and new universal philosophy: Revival of metaphysics and new revolution in Philosophy. Moscow, Russia: Kraft+.

CHAPTER 5

THE HUMAN CAPITAL IN THE CONDITIONS OF DIGITAL ECONOMY (ASSESSMENT PROBLEM) Lyubov Yu. Arkhangelskaya Financial University Victor N. Prasolov Financial University Marina V. Vachrameeva Financial University

ABSTRACT This chapter is devoted to a problem of assessment of the human capital at the institutional level in the conditions of information society for needs of tactical and strategic management of a modern firm. Assessment of the human capital is a general scientific problem at the level of society in general (national and international levels, territorial aspect), and at the branch and institutional levels as well. The analysis of the existing interpretations of cat-

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40    L. Yu. ARKHANGELSKAYA, V. N. PRASOLOV, and M. V. VACHRAMEEVA egory “human capital” and techniques of determination of its size and quality corresponding to them showed that analysts around the world have now no single methodological approach to its assessment, techniques of accounting of change of cost assessment of the human capital in the conditions of digitalization of economy, the increasing introduction of elements of artificial intelligence in production have not been developed yet. Author’s approach to interpretation of category “human capital” (Becker 1964; Korchagin, 2009; Nesterov, 2010; Schultz, 1968) and methodology of assessment of the amount of the human capital at the institutional level in the conditions of development of digital economy is offered. The algorithm of calculation of cost of the human capital at the institutional level is based on a basis of index model of the mixed type, assumes the use of data of internal accounting, statistical, personnel record, and metadata (official statistical data of the state and departmental statistics) as information base. In the chapter results of approbation of the developed model are given and the directions of its development and practical application are defined.

Understanding the human capital as a set of intellectual abilities, skills, experience, knowledge of each individual separately and all individuals of the community who live in a particular area, allowing its possessor (bearer) to derive material and spiritual benefits from its ownership and use through its application in the labor activity for the production of goods and services (authors’ definition), we believe that in the current conditions of development of the information society in Russia (The Government of the Russian Federation, 2020) the methodological approach to the definition of the category of “human capital” and its monetary measure at the level of individual institutional units will change significantly. The genesis of analysts’ views on the content of the category “human capital” (HC), since the 70s of the 20th century to the present, allowed us to formulate the authors’ understanding of the investigated phenomenon. Based on the presented reproductive structure of human capital, a methodological approach to the monetary estimation of the stock of the organization’s human capital in the digital economy, based on the use of mixed (additive and multiplicative) index models, which will incorporate in the analysis of the method of “chain substitutions” in assessing the impact on the cost of human capital of any organization changes in the elements of current and capital costs, as well as the number of staff, the level of its productivity, the level of staff turnover. The novelty of the study is to generalize and concretize the category of “human capital” in relation to territorial boundaries (national “human capital”), in relation to the individual as a bearer of their own professional and cultural competencies and to the focus on the material and spiritual benefits of the right holder of “human capital” as an asset, which is the

The Human Capital in the Conditions of Digital Economy    41

most important factor in the successful development of institutional units and the economy of the country as a whole. The definition of human capital is increasingly systemic, no longer limited to the level of education and skills of workers (the narrow concept of human capital). It includes at the stage of formation: education, health care, introduction to the cultural heritage, information and communication, investment (extended concept); corporate spirit, creativity, invention, and so on (broad interpretation of the category “human capital”; Krasina, 2007; Nesterov, 2010). At the same time, it is necessary to distinguish human capital as a set of personal and professional properties of a person that bring him income in connection with his professional activity (the realization of human capabilities); and human potential as the accumulated stock of knowledge, psychophysical capabilities of a person that can be used in their activities at a certain time. Thus, the human potential characterizes the ability of a person to survive, to gain an education, access to benefits (e.g., measured by an integral indicator: the human development index [HDI]). This indicator equalizes the countries of the world with each other, without taking into account the mentality of the population of each country, while the assessment of the value of human capital at the national level in all approaches used (Korchagin, 2009) allows to take into account the quality and national characteristics of the human capital of individual countries. METHODOLOGY The authors presented the structure of human capital cost, formed by the management or the owner of the organization (firm, company) to assess the prospects for the development of high-productive activities of a particular organization (Kibanov, 2017). As shown in Figure 5.1 information-logical scheme the cost of human capital (CHC) is formed by three sources: the employer’s cost of labor (CEL), the individual’s cost of self-development a personality and a professional (CID), the cost of government for the formation and reproduction of human capital per capita (CG). The value of costs is given per one average employee of the company (N) , then the enlarged additive-multiplicative index model of the value of human capital of the organization can be represented as follows:

  CHCt = Nt × IQt ×(1 − Ktw )∑CELtj + ∑CIDtl + ∑CGtk  (5.1) l k  j 

42    L. Yu. ARKHANGELSKAYA, V. N. PRASOLOV, and M. V. VACHRAMEEVA The cost structure of human capital on institutional level (CHCt)

Employer’s labor costs (CELt)

Salary fund S*N*IQ*Ktw Social benefits Travel expenses Payments to extrabudgetary funds Expenditures on cultural and household services

Individual’s expenses for self-development (CIDt)

Payments for educational, medical services, services of cultural institutions, sports, tourism; information, management and other services Investments in their own business, cultural heritage, intellectual property, nature

Government expenditure on human capital of the firm (CGt) Financing from the budgets for education, healthcare, culture, housing, including benefits for housing and communal services, science Payments from extra-budgetary funds (benefits, pensions, payment of treatment and rest) Funding from the budgets of environmental measures

Investing in R&D, intangible assets

The cost of training

Figure 5.1  Information and logical scheme of formation of the value of human capital of the organization.

where CHCt is the value of the human capital of the organization in period t (currency units); Nt —the average number of employees of the organization in the period t (person); IQt —the index of the dynamics of the value of production in fractions of a unit (compared with the plan, or the base period) in period t; Ktw —turnover index in the organization in period t in unit shares; CELtj —j th element of the employer’s labor costs in period t (currency unit/person);

The Human Capital in the Conditions of Digital Economy    43

CIDtl —l th element of the cost of the employee (individual costs) for the formation and accumulation of own (individual) human capital on average per one average employee of the organization in the period t (currency unit/person); CGtk—k th element of government spending on the reproduction of human capital per capita in the period t (currency unit/person). The inclusion of IQt in the model allows you to take into account the growth of the organization’s production in period t. It can be identified as an indicator of the productivity growth of the human capital of the organization in period t. The function of this indicator is also to adjust the average number of employees, as well as the employer’s labor costs in period t, in accordance with the real need for it. The turnover index, which characterizes the intensity of excessive staff turnover in the organization in period t, reduces the number (size) of human capital (1 − Ktw ) and reduces its cost. A number of items of expenditure on the reproduction of the human capital of the organization in period t is complex and is formed at the expense of all three of the above sources. Those are, for example, the training costs accounted for in a certain proportion (dmtjkl ) in each component of the organization’s human capital monetary estimation (Equation 5.2):

EPt = ∑dmt j l k × CHCt (5.2) m

EPt —training and retraining costs in period t; dmt j l k —share of training costs from m th source (m = 1, 3) of funding in period t (j, l, k are fixed). Of particular interest in the structure of human capital evaluation is the investment component (capital costs), which should use a discount multiplier to arrive at the current value of expenditures in period t : budget financing of environmental protection measures (long-term programs), investment in R&D; intangible assets (software products, DBMS, etc.): investing in business. As follows from the composition of such investment costs in human capital, they are carried out at the expense of each of the three sources of financing the reproduction of human capital and for their isolation, we will allocate in Equation 5.1 additional items, adjusted for discount factors (reduction) of the value, and present in the form of a ratio (Equation 5.3):

3   CHCe = Nt × IQt ×(1 − Ktw )∑CELtj + ∑ K m × αm ∑CIDtl + ∑CGtk  (5.3) =1 l k  j 

44    L. Yu. ARKHANGELSKAYA, V. N. PRASOLOV, and M. V. VACHRAMEEVA

where K m —capital expenditures (investments; their value is calculated for one average employee of the organization) from the m th source (m = 1, 3) αm —discount multiplier for investment amounts from m th source (m = 1, 3). The proposed factorial index model of a mixed type (additive-multiplicative) makes it possible to implement index factor analysis by the method of “chain substitutions” for the purpose of assessing the impact of various factors on the cost of human capital by the management of companies. Let’s consider the implementation of this methodological approach using a specific example. Table 5.1 presents the initial data for modeling the level of human capital value for two periods t and (t + 1) for the organization “Future.” TABLE 5.1  Initial Data for Modeling Indicator/Period

t

t + 1

The average number (N)

100

120

The performance index of the value of production in fractions of a unit, IQt

1.05

1.03

The turnover index in the organization in fractions of a unit, Ktw

0.038

0.035

Labor costs incurred by the employer in the context of the list of costs, ∑CELtj , total, including            

j

Labor costs (per a single staff member) Social benefits Travel expenses On cultural and consumer services On training and retraining of personnel Payments to extra-budgetary funds

Costs of the employee (individual costs) for the formation and accumulation of own (individual) human capital per a single staff member of the organization,

∑CIDtl , total, including

l

payment for educational, medical services, services of cultural institutions, sports, tourism, information, management and other services

           

           

983,076.3a

1,017,272.3a

750,000.0 2,300.1 1,120.8 2,500.6 2,154.8 225,000.0

774,000.0 3,100.6 2,450.4 2,400.8 3,120.5 232,200.0

                

                

54,881.44a

58,872.5

54,881.44

58,872.5

(continued)

The Human Capital in the Conditions of Digital Economy    45 TABLE 5.1  Initial Data for Modeling (continued) Indicator/Period

t

Government expenditure on the reproduction of human capital per capita, ∑CGtk total, including

k

payments from extra-budgetary funds (benefits, pensions, payment of treatment, and rest) Capital expenditure (investment),

∑CGtk

t + 1

   

             

        16,680.22a

17,744.68

16,680.22

17,744.68

   

   

   

   

k

per a single staff member

81,140.76

90,553.32

9,995.0

12,742.4a

  On intangible assets

a

11,140.2

11,190.6a

 Investments in their own business, cultural heritage, intellectual property, nature

16,020.0a

19,135.0a

 Budget funding of environmental measures

4,0619.43c

4,474.35c

 Budget funding of education, health care, culture, housing, including benefits for housing and communal services, science

39,923.62

43,010.97c

  on R&D

a

b

Source: a b,c

Conditional data author The accounts chamber of the Russian Federation (2016)

RESULTS The author proposes a methodological approach to the valuation of the human capital of an organization in the digital economy, based on the use of mixed (additive and multiplicative) index models, which will allow using the method of “chain substitutions” in the analysis when assessing the impact on the human capital cost of any organization and changes in the elements of the current and capital expenditures, as well as the number of personnel, the level of its productivity, the level of staff turnover. The obtained dependences (Table 5.1) reflect changes in the cost of human capital of the organization as a result of changes in individual elements of expenditure depending on the source of funding, the nature of the costs per a single staff member, the growth of production, changes in the level of wages, investment. According to the results of calculations using additive-multiplicative index model on the example of the organization “Future” defined CHC for two periods. The calculations used both conditional data on the

46    L. Yu. ARKHANGELSKAYA, V. N. PRASOLOV, and M. V. VACHRAMEEVA

organization and actual data obtained from the reports of the Accounts Chamber of the Russian Federation on implementation of the State Budget, budgets of extra-budgetary funds of the Russian Federation. The analysis of changes in the organization’s CHC in the period (t + 1) due to certain factors showed an increase in the CHC as a whole by 23.94 million rubles, with the following indicators being the main growth factors: an increase of 20 staff members, which led to the increase in CHC to $ 21.3 million rubles; an increase of employer’s unit costs on 34,196 rubles, which caused the increase in CHC to 4.08 million rubles; an increase of individual’s unit costs on the reproduction of HC on 3,991 rubles, which led to the increase in CHC 0.41 million and, finally, an increase of government unit cost in HC taking into account the discount on 18,579 rubles, which caused a rise in the CHC by 4.3 million. Factors that reduced the CHC were the indicators of staff turnover (the growth of the indicator causes a decrease in the real list number of personnel of the organization)—by 3.9 million rubles and the index of dynamics of the value of production—it decreased by 2%, which led to a decrease in the CHC by 2.4 million rubles. CONCLUSIONS The presented author’s development can be used by the management of enterprises based on various forms of ownership engaged in various types of economic activity. The development is of an applied nature, but contains elements of scientific novelty in terms of indicators (factors) included in the model. The advantages of the development are: available implementation tools (Microsoft Excel); available primary data sources (www.gks.ru; www.audit. gov.ru); ease of implementation of deterministic factor model and availability of “chain substitutions” method; versatility (for all types of organizations); can be used as an add-on in automated management accounting systems of organizations, which is particularly promising in the digital economy. REFERENCES Becker, G. S. (1964). Human Capital. New York, NY: Columbia University Press. Kibanov, A. Ya. (2017). Human resource management: Theory and practice. Assessment of economic and social effective management of personnel of the organization: An educational and practical grant. Moscow, Russia: Avenue. Korchagin, Yu. (2009a). Broad concept of human capital. Retrieved from http://www .lerc.ru/?part=articles&art=3&page=22 Korchagin, Yu. (2009b). Efficiency and quality of national human capital of the world. Retrieved from http://www.lerc.ru/?part=bulletin&art=38&page=1

The Human Capital in the Conditions of Digital Economy    47 Korchagin, Yu. A. (2009c). Interrelation of information and human capital. Retrieved from http://lerc.012345.ru/informatics/0001/0006/ Schultz, T. (1968). Human Capital in the International Encyclopedia of the Social Sciences. New York, NY: Science. The Accounts Chamber of the Russian Federation. (2016). Report of 2016. Retrieved from http://audit.gov.ru/upload/iblock/999/99941bef8cfcd18e7e1 0d5e8630f70bb.pdf The Government of the Russian Federation. (2020). The Resolution of the Government of the Russian Federation of 15.04.2014 N 313 (An edition of 25.09.2018) “About the approval of the state program of the Russian Federation “Information society (2011–2020)” the Official Internet portal of legal information. Retrieved from http://www.pravo.gov.ru

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CHAPTER 6

FEATURES AND MODELS OF HUMAN CAPITAL REPRODUCTION IN THE CONDITIONS OF ARTIFICIAL INTELLIGENCE DEVELOPMENT Valentina V. Gorbunova Stavropol State Medical University Lilianna Yu. Grazhdankina Stavropol State Medical University Natalya K. Mayatskaya Stavropol State Medical University Marina M. Shulga North-Caucasus Federal University Nina Yu. Zhelnakova North-Caucasus Federal University

Meta-Scientific Study of Artificial Intelligence, pages 49–58 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

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ABSTRACT The chapter analyzes the role and features of human capital reproduction amid modern scientific and technological advances, robotization and development of artificial intelligence (AI). The study conducted a systematic analysis of theoretical and applied research in the field of changes in the direction of human capital (HC) reproduction, employment structure and labor market needs in the development of AI. Three main models of HC reproduction are described: individual, corporate, national. It is concluded that investments in the development of HC should be focused on the requirements for the occupations of the future through the competencies that depend on the demand from the changing markets. An important condition for ensuring the development of HC is the modernization of the education system, its transformation towards lifelong learning based on a combination of public and corporate learning tools. In order to offset the negative consequences of the unevenness in digitalization and robotization of the economy in the regional, sectoral, and professional aspects, it is necessary to actively develop programs of comprehensive socioeconomic support of the population.

Human capital (HC) in the digital economy is a key factor in the development of society and scientific and technological progress, the introduction of technological innovations. The state and quality of HC determine the nature of interaction with modern technological innovations, including production systems based on artificial intelligence (AI). However, the modern dynamics of global innovation development, intensive processes of AI technologies introduction underscore the need for creation of favorable conditions for breakthrough scientific, technological and socioeconomic development of the country in Russia, in which the quality of HC plays a key role. Improving the quality of HC and changing the direction of investment in its reproduction in the modern digital economy are essential conditions for sustainable economic growth. METHODOLOGY The theoretical and methodological basis of the research are the works of scientists in the field of the main factors and mechanisms of HC reproduction in modern socioeconomic conditions. Highlighting the features and main trends of HC development in the development of AI was carried out on the basis of a systematic approach and analysis of the current situation in the labor market and employment. The empirical base of the research is the statistical data of Russia. The use of systematic and structural–functional scientific approaches allowed considering HC as a dynamic system that ensures the implementation of scientific and technological progress and innovative development of the Russian economy.

Features and Models of Human Capital Reproduction    51

RESULTS The processes of HC reproduction in the conditions of AI development will receive a priority for the modern Russian society (Varlamov, 2018). Development patterns of modern scientific and technological progress directly affect the reproduction of HC and labor relations. The modern information revolution is expressed in the swift development of information technologies, intensive implementation of information networks in the general spheres of society. Major contradiction of global scientific and technological progress is the growing technological gap between developed and lagging countries, different social strata and groups, as well as the problem of employment in situations of widespread production systems based on AI and faster population growth in low-income countries (Gokhberg et al., 2017). Accelerating rates of scientific and technical progress lead to reduction of periods of change of technological tenors, generations, and models of equipment. If in the mid-twentieth century for a generation there was one change of technological tenors and two or three generations of technology, by 2050, during the period of a person’s active working life there will be two technological tenors and three or four generations of technology. These trends require constant adaptation to changing technological working and living conditions, increasing the level of innovative activity of people, periodic change of accumulated professional skills (Rylko, 2015). The current generation is not ready to be an engine of a radical and innovative revitalization of society (Bobylev & Grigorev, 2018). In this regard, in the near future in order to ensure the effective reproduction of HC in the conditions of development of AI it is necessary to solve the following problems: • increase of fundamental character of education, its leading character with the purpose of readiness formation for radical changes in lifestyle and labor activity. It is necessary to change the content of education, the level of training/retraining of personnel, focus on increasing creative orientation of professional education, the readiness of new generation to implement innovations in all areas of activities; • formation of a continuous education system covering the entire period of the human life cycle and providing an opportunity to improve their professional potential and willingness to work in the new socioeconomic and technological conditions; • humanization of education and all social spheres of society with the purpose of adaptation to the radical changes taking place in society in the transition from industrial to the integral humanistic world civilization based on artificial intelligence technology, the formation of

52    V. V. GORBUNOVA et al.

the world, established on dialogue and partnership of civilizations and states, social strata, and generations (Abrahamyan, 2017). The active development of AI technologies will lead to the intellectualization of labor, the growth of the share of employees in the service sector and the development of new forms of employment based on distance technologies. In the context of emerging trends in Russian society (decline in the working-age, growing pressure in the labor market, mostly structural nature of unemployment associated with the imbalance of the labor market and the market of vocational education, the high share of the informal sector in total employment, relatively low wages in the cost of production), employment problems will be further exacerbated (Sizova & Khusyainov, 2017). The processes of robotics and the introduction of AI technologies will lead to the displacement of man from the production. According to a study by The Future of Jobs, recently published by the World Economic Forum, 2 million jobs will be added to the global labour market by 2020, but 7.1 million will disappear. Jobs will appear in the intellectual and high-tech sectors, and will be reduced in the real sector of the economy . . . and the sphere of administrative work. (Laikam, 2017, p. 105)

A number of futurists predict that by 2030–2035 robotization will lead to replacement of almost half of the currently existing professions (“Millions of Jobs in the World Will Disappear Because of New Technologies,” 2016). Investments in the reproduction and development of HC throughout an individual’s active lifetime are becoming an increasingly urgent problem. Modern information society creates the conditions, when the skill requirements for employees are rising and constantly changing, the flexibilization of employment is growing. A situation is emerging where there is no permanent employer interested in maintaining long-term employment relationships in the conditions of transforming nature of employment, so the bulk of responsibility associated with advanced training/retraining falls on the person himself. In the system of reproduction of HC in the development of AI three models can be identified: individual, corporate, national (Savchenko, 2010). These three main models of HC reproduction make it possible to carry out a comparative description of investments and factors that determine its level and quality. Individual Model of HC Reproduction The family acts as a basic factor in the formation and reproduction of HC, accumulating and developing the abilities of the individual, which in

Features and Models of Human Capital Reproduction    53

the future are manifested in the form of the HC implementation in the socioeconomic system and society. At the level of family and home education, the basic characteristics of modern HC are laid: creative thinking, the need for continuous training and self-development, the ability to effectively adapt to the rapidly changing conditions of the economic, social, political environment, effective interaction with information, and the use of information technology. Individual factors of human potential reproduction at the micro level are: • biological capital: providing a set of inclinations and talents at a genetic level; • financial capital: determining the material possibilities and investments volume of the family in the reproduction of HC; • HC of parents and the immediate environment: which determines the direction of the reproduction of HC on the basis of variables such as the level of education of parents and their social and professional status; • social capital: social resources transferred by parents to their children; and • cultural capital: is a system of values, norms, stereotypes of behavior, culture of leisure and life activities assimilated by children through parents. The following conditions are formed at the micro level to determine the possibility of developing and improving the quality of HC: the formation of the need for continuing education and self-development; a certain set of abilities, knowledge, professional skills and motivations; interest in research and development activities; the possibility of compensation for lost earnings due to the need for continuing education (see Figure 6.1). The main functions of the family in the formation of HC are: 1. Ensuring the satisfaction of the needs and interests of family members on the basis of formed family financial capital. 2. The formation of family property, its accumulation and provision of inheritance. 3. Socioeconomic activities for the formation and use of the family budget. 4. Formation of readiness for effective interaction in the modern information environment, digital literacy, entrepreneurship, and innovation (Savchenko & Popova, 2012). 5. Integration of all economic and social functions of the family in order to obtain a result—a “capitalized” person.

54    V. V. GORBUNOVA et al.

Biological capital

Financial capital

The possibility of compensation for lost earnings

Work motivation and economic culture

Parents’ human capital

Social capital

Mobility and social activity

Interest in research and development activities

Social status and availability of social resources

Values, moral guidelines, and norms of behavior

A certain stock of abilities, knowledge, professional skills, and motivations

Quality and duration of training

Ability, aptitude, the direction of the personality

Degree of development of individual and creative abilities

Formation of the need for continuous education and selfdevelopment

Strategies for self-preservation behavior and healthy lifestyle

Individual HC

Cultural capital

Individual potential of the micro level of human potential reproduction

Figure 6.1  Model of HC reproduction at the micro level (individual model of HC reproduction).

The following activities are needed to achieve this goal: • normal conditions for the production of human life; • investments for the formation of natural elements of HC; and • participation of each family member in social development. Corporate Model of HC Reproduction The current socioeconomic situation determines the need for constant reproduction and development of personnel at the organizations level. Human capital at the enterprise level is generally understood to be “the combination of skills, abilities, qualifications and intellectual potential of profit-making personnel” (Savchenko, 2010. p. 178). Schematically, the model of HC reproduction in the system of postgraduate education at the meso level is shown in Figure 6.2.

Features and Models of Human Capital Reproduction    55 The purpose of the enterprise: maximum profit at minimum cost

Increase of competitiveness of the enterprise in the Russian and international market

Improving the image, social status and prestige of the enterprise or organization

Growth of production profitability

Improvement and optimization of production processes

Optimization of trade and sales network of the enterprise

Development and improvement of human potential of enterprises, institutions

Planning

Acquisition

Maintenance

Development

Preservation and retention

Retraining and obtaining a new qualification or specialization

Traineeship

Postgraduate education

Advanced training

Re-training and obtaining the second higher education

Postgraduate study, doctoral, and postgraduate studies

Figure 6.2  Model of HC reproduction in the system of post-graduate education at the meso level (corporate model of HC reproduction).

There are five main activities of enterprises and institutions in the development and improvement of HC at the meso level: • planning, which involves the cost of career guidance and training of future professionals; • acquisition includes the costs of search, recruitment, and personnel financial costs during the adaptation period; • maintenance involves investments in personnel during the period of growth potential accumulation, the focus of which is wages, financing of social programs related to the improvement of motivation and stimulation of labor; • development involves investments in retraining, advanced training, and additional education; and • preservation or retention involves making the maximum possible profit from the use of the staff HC.

56    V. V. GORBUNOVA et al.

The reproduction of HC at the meso level involves at least three types of costs: • direct money expenses (payment of trainers, handbooks, manuals, guidelines, etc.); • indirect additional costs, the use of experienced workers not for productive work, but for tutor training; and • indirect costs, part of the time goes to training. The National System of Reproduction of HC The state is the main factor in the reproduction and development of HC. The current socioeconomic situation of the development of Russia is characterized by the gradual replacement of the paternalistic model by the model of subsidiary state, transferring a major share of all expenditure on investment in HC from the state to organizations and the population. In Russia, the technology of socially responsible business has not yet been widely developed. The main objective of the state in the reproduction of HC is to develop the competitiveness of the Russian economy (Figure 6.3). The purpose of the state: development of competitiveness of the Russian economy

Normative-legal regulation

Monitoring and forecasting of the needs for human resources of the economy

Improving the level and quality of life of the population

Development of effective employment policy at the federal and regional levels

Financing of research and innovation

The main directions of postgraduate education

Financing R&D

Development of the state order for the training of specialists in demand in the labor market

Development of federal programs of professional development

Reforming the system of higher and postgraduate education

Integration of Russian education into the world educational space

Definition of strategic directions of Russian science development

Figure 6.3  Model of HC reproduction at the macro level (national model of HC reproduction).

Features and Models of Human Capital Reproduction    57

The main means of achieving these goals are: improving the level and quality of life of the population; regulatory and legal regulation of socioeconomic processes; monitoring and forecasting the needs for human resources of the economy; development of effective employment policy at the federal and regional levels; and financing of research and innovation. CONCLUSIONS Amid the intensive development of digital economy, robotics and AI technologies, the problems of reproduction and qualitative development of HC, capable of active labor and social activity, becomes particularly relevant. The intensive development of digital economy and AI technologies in the next 5–7 years will have an inevitable impact on the HC reproduction, focusing it on the preparation for effective interaction with the achievements of scientific and technological progress, the formation of new competencies that will ensure the leadership of our country in certain areas of science and technology and the formation of a comprehensive national innovation system. The leading promising areas of HC development in Russia are: • purposeful and systematic development and improvement of HC through various reproduction models; • orientation of HC innovations for the competencies required of the professions of the future that depend on the demand from the changing markets and form a dynamic portfolio; • ensuring the development of HC on the basis of modernization of the education system, with its transformation towards lifelong learning and a combination of public and corporate learning tools; and • implementation of programs of complex social and economic support of the population in order to overcome the uneven digitalization and robotization of the economy in the regional, sectoral, and professional aspects. REFERENCES Abrahamyan, A. A. (2017). Scientific researches in the field of pedagogy and psychology: Convergence and genesis of knowledge. In V. A. Kurina & O. A. Podkopaeva (Eds.), Monograph (p. 299). Samara, Samara Oblast: Povolzhskiy Scientific Corporation. Bobylev, S., & Grigoriev, L. M. (Eds.). (2018). The human development report in the Russian Federation in the Year 2018. Moscow, Russia: Analytical Center Under the Government of the Russian Federation.

58    V. V. GORBUNOVA et al. Gokhberg, L. M., Sokolov, V. A., Chulok, A. A., Radomirova, Ya.Ya., Kuznetsova, T. E., Dranev, Yu.Ya., . . . Mayorova O. A. (2017). Global trends and prospects of scientific and technological development of the Russian Federation. Moscow, Russia: Higher school of Economics. Laikam, K. E. (2017). Labour and employment in Russia. In 2017: Statistical Bulletin (p. 261). Moscow, Russia: Rosstat. Millions of Jobs Will Disappear in the World Because of New Technologies. Statements. (2016). Retrieved from https://www.vedomosti.ru/management/ articles/2016/01/27/625618-ischeznut-rabochih-mest Rylko, E. D. (2015). How competent are adult Russians today. The results of the Programme for the International Assessment of Adult Competencies in the Russian Federation. Moscow, Russia: Higher School of Economics. Retrieved from http://piaac .ru/wp-content/uploads/2015/05/Report_PIAAC_RUS ahhh! Savchenko, V. V. (2010). Models of human capital reproduction in modern Russian society. Bulletin of the North Caucasus State Technical University, 1, 175–181. Savchenko, V. V., & Popova, M. V. (2012). Features and strategies of youth behavior in the modern labour market (on the example of Stavropol region). Regional Economy: Theory and Practice, 29, 28–36. Sizova, I. L., & Khusyainov, T. M. (2017). Work and employment in the digital economy: The problems of the Russian labour market. Vestnik St. Petersburg University, 10(4), 376–396. https://doi.org/10.21638/11701/spbu12.2017.401 Varlamov, K. (2018). Digital economy without human capital is untenable. Retrieved from https://dspace.spbu.ru/bitstream/11701/9064/1/01-Sizova.pdf

CHAPTER 7

METHODOLOGY FOR MEASURING THE QUALITY OF LIFE OF THE POPULATION ON THE BASIS OF REGIONAL DIFFERENTIATIONS Vera I. Menshchikova Tambov State Technical University Natalia V. Zlobina Tambov State Technical University Dmitry D. Logvin Tambov Branch of the Russian Presidential Academy Mkhran Khavashki Tambov State Technical University

Meta-Scientific Study of Artificial Intelligence, pages 59–67 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

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ABSTRACT The purpose of the chapter is to summarize and systematize the methodology for measuring the quality of life of the population, currently being used in different countries of the world, and to work out recommendations for its improvement on the basis of regional differentiations that should be taken into account when developing the social policy of the country. The research methodology is based on the extensive use of logical methods, methods of functional, systemic, and comparative analysis, as well as methods of collecting and processing analytical and statistical information to achieve the research objectives. The authors developed a hypothesis that when measuring the quality of life of the population, it is necessary to apply a methodology that takes into account regional peculiarities of human life. The authors’ definition of the quality of life as an integral category is given; it combines objective and subjective assessment of various conditions of human life within the framework of available resources, through which an individual seeks to achieve well-being. The necessity of taking into account regional peculiarities in assessing the quality of life of the population caused by sharp differentiation of socioeconomic development between regions is substantiated. We propose a method for assessing the quality of life of the population based on the integration of indicators into groups that characterize certain aspects of the phenomenon under study. The results of the calculations were used to allocate several groups of regions of the Russian Federation, differing in the quality of life of the population. The first group includes the regions with the highest level of the quality of life (Moscow and St. Petersburg); the second group—the regions suffering from problems in the quality of life of the population (Republic of Karelia, Republic of Karelia Komi, Republic of Karelia Altai, Republic of Karelia Buryatia, Republic of Karelia Tyva, Republic of Karelia Khakassia, Republic of Karelia Sakha (Yakutia), Murmansk Oblast, Tyumen Oblast, Irkutsk Oblast, Kemerovo Oblast, Novosibirsk Oblast, Tomsk Oblast, Amur Oblast, Magadan Oblast, Sakhalin Oblast, Trans-Baikal Krai, Krasnoyarsk Krai, Kamchatka Krai and Primorsky Krai, and Chukotka Autonomous Okrug); the third group(s)/regions with living standards typical of the country as a whole (the remaining 59 constituent entities of the Russian Federation).

The growing interest in the problem of quality of life was associated with the transition to the postindustrial stage of development and the raising awareness of society of the global problems. In the late 1950s and early 1960s, the contradictions of the existing type of social development were exacerbated. This resulted in strengthening both positive consequences (a sharp increase in productive forces, an improvement in the financial situation) and negative ones (social polarization, an increase in the number of stressful situations, “technological dehumanization,” environmental degradation, etc.; Spiridonov, Nizhegorodov, & Gerasimov, 2010). The quality of life has become a new indicator of social welfare, a “new humanistic

Methodology for Measuring the Quality of Life of the Population    61

philosophy,” and its research has become one of the most dynamically developing branches of scientific knowledge. We can compare different social units by this indicator in time and space, finally design target criteria for public welfare, the conditional optimization of which (with different types of climatic, political, and resource constraints) makes it possible to determine the optimal path of socioeconomic, ecological, and demographic development (Ayvazyan, 2001). METHODOLOGY The research methodology covers logical methods, methods of functional, systemic, and comparative analysis, as well as methods of collecting and processing analytical and statistical information. The research is based on the assumption that measuring the quality of life of the population requires using a methodology that takes into account regional peculiarities of human life. RESULTS The category of quality of life and the problems related to the directions of economic development are in the center of scientific research. The range of approaches to the definition of this category is quite wide; it has been explored in philosophy, medicine, economics, sociology, geography, and ecology. Philosophy considers the quality of life as a complex and multidimensional, social phenomenon. The geographers and ecologists greatly expanded the concept and criteria related to the above concept. From the ecological perspective, such environmental factors as the level of anthropogenic environmental pollution, changes in climatic characteristics, and their impact on health status and perception of living conditions by the population are put at the forefront (Kogut & Rokhchin, 1994). Most researchers agree that today there is neither unified definition of the category of quality of life, nor generally accepted methods for evaluating this category (Sobol, 2018; Kislitsyna, 2016). Some researchers believe that the quality of life covers all spheres of an individual’s life and includes evaluation of the individual’s living conditions, resources, through which they seek to satisfy their needs (Zhadko, 2008). In our opinion, the quality of life of the population is an integral category that combines an objective and subjective assessment of various conditions of an individual’s life using available resources through which individuals strive to achieve well-being and satisfy their needs (Degil, 2012). The theory of quality of life of the population consists of the terms forming a system that can be represented as a diagram, where standard terms

62    V. I. MENSHCHIKOVA et al. Institutional shell Key terms Core terms Core

Quality of life Life quality management system Resources Reserves

Quality Life Quality system Quality management

Identification Factor Need Indicator Integral indicator Integrated management system Indicator planning

Life quality management reserves Individual reserves (M1) Policy and strategy reserves (M2) Regional resource reserves (M3) Authorities reserves (M4) Metrological reserve (M5) Environmental reserves (M6)

Figure 7.1  Block diagram of life quality management.

describing the category “quality” form the core of the system. The core is formed on the basis of the terms of the standards series ISO 9000, ISO 14000, OHSAS 18000, SA 8000 (AA1000), GRT. The formation and development of the term system refers to the system of management. The developed system of terms will improve the efficiency and effectiveness of activities enhancing the quality of life of the population at all stages of the management process (Figure 7.1). The key terms characterizing the quality of life of the population are as follows: • quality is the degree of compliance of the set of inherent characteristics with the requirements (GOST R ISO 9000: 2008); • quality system is a set of organizational structure, methods, processes and resources necessary for the implementation of general quality management (GOST R ISO 9000: 2008); • quality management is a coordinated activity in directing and managing an organization with regard to quality (Rebrin, 2004); • reserve (Fr. reserve [stock], Lat. reservare [to save]) is either a reserve of something in case of need, or a source from which additional new forces are drawn (Lekhina, Lokshinoy, Petrova, & Shaumyana, 1964, p. 784);

Methodology for Measuring the Quality of Life of the Population    63

• need is lack of anything necessary to support the vital activity of the organism, the human person, the social group, the community as a whole (Yadov, 1975); • indicator is a device, or element, showing the course of the process or the state of the object under observation, its qualitative or quantitative characteristics in a form that is convenient for perception (Osipov, 2005). The proposed system of terms allows systematizing the terminology related to life quality management, as well as expanding the scope of its application in the processes of improving the quality of life of the population. The life quality management system represents a complex structure of interrelated elements: the quality of health care, education, the living standards, the quality of the environment, spiritual values, and so on. The impact on one of the allocated areas provokes a complex change in the entire system, which reflects the complexity of the relationships in it. The quality of life is understood as a complex indicator of all aspects of life, the system of the quality of life should cover all aspects of life. In order to analyze the structural shell of the quality of life, it is expedient to build on the quality of the individual’s needs (Figure 7.2). We consider it expedient to single out three key spheres: the natural and biological sphere involves the satisfaction of the needs at the lowest physiological level; the social sphere refers to the satisfaction of social needs);

Health Environment

Demography etc.

Natural & Biological sphere Leisure & Recreation

Living standard Healthcare

Spiritual sphere

Quality of Life

Social sphere

Cultural Heritage

Family Employment Social Security

Value Orientation

Figure 7.2  Life quality management system.

Education Labor life

64    V. I. MENSHCHIKOVA et al.

spiritual and cultural sphere covers the process of satisfaction of spiritual and cultural needs. A generally accepted assessment system is that of measuring the Human Development Index (HDI) proposed by the United Nations (UN). It is calculated every year, and the UN report combines the calculation of a complex indicator with a large analytical report and statistical information from various international institutions and national organizations. The HDI is an integral indicator of the level of human development in a particular country. It is often used as a synonym for the “quality of life”/“living standard” categories, measuring the country’s achievements from the perspective of health, education, and real income. Along with the integral indices developed by the UN at different times, there was a huge amount of such complex integral indicators at the national levels. Such indicators were created in the United States, France, Great Britain, Germany, and other countries. The integral indicators are used to rank the countries and determine their ranking. Modern authors include various sets of characteristics that are significantly different from each other into the integral indicator. They can be divided into three groups (Martyshenko, 2014): the first group of authors uses strictly objective or statistically recorded indicators to determine the integral indicator; the second group, on the contrary, considers only subjective assessments, that is, data from surveys and sociological surveys; the third group of authors combines the above two approaches. It is necessary to determine the purposes for which we use indicators of the quality of life of the population. If their main function is to rank regions or localities within a region, then indicators should be as objective and widely available as possible (official statistics). At the same time, from the perspective of management information, some subjectivity should be inherent in the indicators of quality of life (using the expert method), so as to take into account the most problematic aspects of the region’s development. From the perspective of the management action, the most effective is the impact at the regional level, since it is the region that possesses the necessary levers of influence. At the same time, the regions have a fairly large set of resources, and, if necessary, they can obtain the necessary resources from the federal center through a wide range of federal programs. At the same time, local officials can respond quickly to changes. In such a situation, the main point is the qualification of the managerial staff in the region, its commitment in the struggle for quality, and the effectiveness of the life quality management system as a whole (Sheveleva, 2010). The regional specifics in assessing the quality of life of the population is extremely important in modern economic conditions, both within one country and when comparing countries of one region of the world. According to Rosstat, in the regional context, industrial production in Russia

Methodology for Measuring the Quality of Life of the Population    65

in 2017 was characterized by a high degree of unevenness: the range of industrial production index values varies from 141.9% in the Jewish Autonomous Region to 89.2% in Sevastopol (www.gks.ru). While in the country as a whole there is a slowdown in industrial growth, the number of regions with a positive dynamics of the indicator compared with 2016 has increased. At the end of 2017, the industrial production index grew in 73 regions of Russia. In terms of the absolute volume of investments in fixed capital in the regions of Russia, there was a global gap: the total investments in 2017 attributable to the city of Moscow, the Yamalo-Nenets Autonomous Okrug and the Khanty-Mansi Autonomous Okrug-Yugra amounted to almost 4 trillion rubles, or 25% of the total Russian volume. The smallest contribution to the total investment was made by the Republic of Tyva, the Republic of Kalmykia, the Jewish Autonomous Oblast, the Chukotka Autonomous Okrug, the Republic of Altai, the Republic of Ingushetia and the KarachayCherkess Republic. These seven regions accounted for 0.5% of total Russian investments in fixed assets, while investments in each of these regions did not exceed 20 billion rubles (www.gks.ru). The average Russian Gini coefficient was at a relatively high level (0.37), which was not due to inequality within regions, but to inequality among different regions. In general, the Gini coefficient of wage inequality in Russia was approximately at the level of Japan or England. At the same time, the Gini coefficient for the salary ratio in Russia was noticeably lower than calculated for income (0.41). The indisputable leader in Russia on the minimum value of the Gini coefficient is the Belgorod Oblast. The Gini coefficient in this region was only 0.27, which indicated a high degree of wage equality in the region. The highest Gini ratio for wages was observed in Moscow and the Sakhalin Oblast (www.gks.ru). The results of the calculations allowed allocating several groups of regions of Russia, differing in the population quality of life: the first group (the regions with the highest level of quality of life) Moscow and St. Petersburg; the second group (regions with some problem areas in the quality of life of the population) Republic of Karelia, Republic of Karelia Komi, Republic of Karelia Altai, Republic of Karelia Buryatia, Republic of Karelia Tyva, Republic of Karelia Khakassia, Republic of Karelia Sakha (Yakutia), Murmansk Oblast, Tyumen Oblast, Irkutsk Oblast, Kemerovo Oblast, Novosibirsk Oblast, Tomsk Oblast, Amur Oblast, Magadan Oblast, Sakhalin Oblast, Trans-Baikal Krai, Krasnoyarsk Krai, Kamchatka Krai and Primorsky Krai, and Chukotka Autonomous Okrug; and the third group (regions with living standards typical of the country as a whole) these are the remaining 59 constituent entities of Russia.

66    V. I. MENSHCHIKOVA et al.

CONCLUSIONS The assessment of the quality of life of the population in the regions of the Russian Federation showed that factors affecting the increasing income differentiation in society, the increase in the proportion of the population with incomes below the subsistence minimum have a negative effect. This indicator is closely related to the unemployment rate. The low standard of living of the population affects the demographic indicators, which are characterized by a decrease in the birth rate and an increase in the morbidity rate, which causes a natural decline in the population, despite many measures of social support from the government. On the whole, the hypothesis put forward was proven: when measuring the quality of life of the population, it is necessary to apply a methodology that takes into account regional characteristics of human life. This will increase the effectiveness of measures of government social policy. ACKNOWLEDGMENTS The chapter was supported by the Russian Fundamental research Foundation and the Tambov Oblast in the framework of a research project 18-410680010 p_a REFERENCES Ayvazyan, S. A. (2001). Comparative analysis of the integral characteristics of the quality of life of the population of the constituent entities of the Russian Federation. Moscow, CEHMI RAN, 64. Degil, O. V. (2012). The method of determining the quality of life of the population of the region based on a comprehensive indicator of the quality of life. Global Scientific Potential, 11(20), 132–138. GOST R ISO 9000. (2008). Quality management systems. Fundamentals and glossary (put into effect by order of the Federal Agency for Technical Regulation and Metrology of December 22, 2011 N 1574-st). Retrieved from http://www.consultant .ru/document/cons_doc_LAW_195013 Human Development Report 2017. (2017). Retrieved from http://hdr.undp.org Kislitsyna, O. A. (2016). Measuring quality of life. Well-being: International experience. Moscow, Russia: Institute of Economics, RAS. Kogut, A. E., & Rokhchin, V. E. (1994). Regional monitoring: Quality of life of the population. SPb., ISEPN RAS. Lekhina, I. V., Lokshinoy, S. M., Petrova, F. N., & Shaumyana, L. S. (Eds.). (1964). Dictionary of Foreign Terms. Moscow, Russia: Soviet Encyclopedia.

Methodology for Measuring the Quality of Life of the Population    67 Martyshenko, S. N. (2014). Conceptual models of quality of life management. Analytical review. Regional Economics and Management: Electronic Scientific Journal, 2(38), 80–92. Osipov, Yu. S. (Ed.). (2005). Great Russian Encyclopedia (Vol. 11). Moscow, Russia: Great Russian Encyclopedia. Rebrin, Y. I. (2004). Quality control. Taganrog, Russia: Publishing House of TSURE. Sheveleva, R. N. (2010). On the issue of assessing the quality of life of the population. Regional Economics: Theory and Practice, 14, 67–76. Sobol, T. S. (2018). The modern level and quality of life of the population of Russia. Bulletin of the Moscow University named after S. Yu. Witte, Series 1: Economics and Management, 2(25), 7–14. Spiridonov, S. P., Nizhegorodov, E. V., & Gerasimov, B. I. (2010). Institutional indicators of quality of life. Tambov, Russia: TSTU. Yadov, V. A. (1975). Need. In Great Soviet Encyclopedia (Vol. 20; p. 439). Moscow, Russia: The Great Soviet Encyclopedia. Zhadko, Yu. V. (2008). Conceptualization of the concept of “social resources.” Scientific potential of students—the future of Russia. Materials of the II International Scientific Student Conference, Social Sciences. Stavropol, North Caucasus State Technical University, 2, 166.

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CHAPTER 8

LIFE IS GREY, BUT THE TREE OF THEORY IS EVERGREEN Ladislav Zhak INSOL Europe Member, Prague Czech Republic

ABSTRACT The chapter concerns the issue of potential systemic, objective, and subjective errors that could be possibly made by modern science, especially by the branch of computer science called artificial intelligence (AI). The chapter warns against a very peculiar approach to simulation and to experimental certification of results in virtual cyberspace. It emphasizes the importance of direct human communication and its development to insure that the future of society belongs to human society and the chapter also warns against the ongoing dehumanization and dumbification of man and society.

The name of our conference is Metascientific Study of AI. The title of my speech is “Life Is Grey but Forever Green Is the Tree of Theory” (Adams, 2002). It goes without saying that all of you understood that it is a play on words of the famous quote from Faust by Johann Wolfgang von Goethe (2003), that is, “All theory is gray, my friend, but forever green is the tree of life” uttered by Mephistopheles, one of the notorious devils of world literature. I would like to attract your attention to a serious problem facing us while conducting scientific research in artificial intelligence (AI). This is Meta-Scientific Study of Artificial Intelligence, pages 69–73 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

69

70    L. ZHAK

a problem of interrelation of life and mechanics, interdependence of man and machines, a problem of relationship between life and science. METHODOLOGY If the facts and reality are not consistent with the theory then ”Woe to the facts!” (Alpidovskaya, 2018) and ”Woe to the reality!” We all know that, we have heard that, and the elderly used to hear that almost every day, when they correlated the theory of the so-called scientific communism with their life experience. It was true not only in our youth, and, unfortunately, it remains true in scientific discourse even nowadays. It is necessary to touch upon the topic of science. Although science is only one of many tools to perceive our world, it has gradually filled the privileged social niche, which in the Middle Ages was occupied by churches, particularly in Western Europe by the Catholic Church (Alpidovskaya et al., 2018). We used to be concerned about acting in compliance with the church doctrine, but at the present time we are mostly concerned to remain compliant with scientific knowledge and expert opinion. We might even wonder if there is any difference between the purchase of indulgences in the olden days and that of expert conclusions at present. The matter is that they both are expected to protect us against possible consequences of certain violations of the existing rules. RESULTS Nowadays science is too closely intertwined with power similar as was the case with the church in the past. Many scientists are happy about that, which is especially used by nondemocratic governments to demonstrate the infallibility of their regime. Thirst for power is the greatest sin of modern science generally added by excessive craving of some scientists for personal popularity. The Real World Science creates models transferring us into the virtual “otherworld” (Andersen, 1956) and works with them over there on the basis of the hypothesis developed by the scientists with the help of various tools, mostly mathematical and laboratory ones. The modified and transformed models are transferred by science back into the real world, where it tries through experiments to make them again operate as an integral part of the reality of nature. In case all that succeeds, the transformation process in the virtual otherworld is confirmed. The hypothesis used by the scientists becomes an integral part of the scientific theory awaiting its disproof by some theoretical or practical methods. If this is not achieved, the hypothesis of

Life Is Grey, but the Tree of Theory Is Evergreen     71

possible model transformation is immediately disproved. Therefore, science has three main areas where and how errors can be made. The problem of the science of AI is that it all too often takes into account the so-called cyberspace or digital world or data world (Bateson, 2006). In spite of the fact that it surely has the characteristics of true reality, it is actually a human construction being partially virtual. Therefore it is not as independent of science as nature is. In this case it is possible for a child of science and technology to forgive its parents’ mistakes. Both the original creation of models and the final experiment take place in a “more friendly” virtual atmosphere than in wildlife. If we are not careful enough while creating models for our work, especially for the experimental validation of the results achieved in the course of the development of AI, we might entertain dangerous illusions entailing rather grim consequences both for a human being as an individual and for a group of people and the whole society. I always tell a story from the book The Hitchhiker’s Guide to the Galaxy. The answer to “The Ultimate Question of Life, the Universe, and Everything” (Adams, 2002) was supposed to solve all the problems of the universe. All rational beings were looking forward to the answer. The answer was given after seven and a half million years of ceaseless calculations made by a special supercomputer named Deep Thought, which was created to carry out that particular task. The computer stated that the correctness of the answer had been checked a few times but it might be disappointing for everybody. The answer turned out to be: 42. The aforementioned does not concern only literary works warning about the problem. Until quite recently it seemed that chess algorithms would be able to help raise the level of chess of the players using these algorithms while analyzing chess games and positions. It was assumed that chess algorithms are more powerful and accessible than a group of consultants, who could be used only by a few top chess players in the world. The ELO rating system, which is the most widely used chess rating system, has clearly shown that performance degradation took place right there where growth was expected with high probability. The matter is that during a chess game the human brain works differently than the AI algorithms, and they rather hamper the development of creative thinking of certain chess players. The issue of chess algorithms was covered in a recent comprehensive interview with the world chess champion Magnus Karlsen (Flegr, 2015). Such phenomena may be observed regarding the results of the influence of AI upon the quality of results in other spheres of human activities, although, unfortunately, they cannot be as successfully measured as in chess competitions. We can say that it is a matter of trust and credulity. In case we believe artificial intellect blindly, it is very likely that they will bend us to their will. These days the Western world begins to understand that the communication systems of the Chinese company Huawei are practically

72    L. ZHAK

uncontrolled. It is an open secret that any communication system and also other systems controlled by AI at a similar level are, black boxes. Only with some degree of professional certainty can we only guess what kind of processes are going on in there. In the end we can find out that everything is 42. In view of the fact that all that is science and it has taken a lot of money and effort, there will start a strong and effective campaign proclaiming that everything is really 42. The best brains and big money will throw their weight behind their child “Deep Thought” and its calculations. The voices of common sense crying out like one character of the famous fairy tales by Hans Christian Andersen (1956) that “the King is bare!” will be just the biblical miserable “voice in the wilderness” (Bible, Czech Biblical Society, 2015). CONCLUSIONS AND RECOMMENDATIONS It could easily happen that, unlike Andersen’s fairy tale characters, bare and pompous Deep Thought kings will become the rulers of the world. Instead of being riciculous, dumb, and artificial bareness will become a highly respected common standard. In this way the use of artificial intellect will definitely complete the process of total dumbification of an individual and society as a whole. If we wish to have our future society as a human society with the presence of artificial intellect, there is no other option than to refuse from the increase of interpersonal compatibility through the formalization and ritualization of communication replacing it by global intercultural understanding and tolerance. The prerequisite for intercultural understanding is the recognition of human differences, awareness of communication with a representative of my species having something common with me, which I would call humankind (Hrubec, 2011). The other individual might have other interests, ideals, education, upbringing, religion or skin color, but it is a human being. Human communication in future must be based on human naturalness and the ability to reach agreements and fulfill them (Neubauer, 2009), at least, as long as agreements are beneficial for the parties to them. Understanding, recognition and respect of human naturalness, that is, ordinary humanness, can in the light of the theory of evolution help find such a way of communication, which could lead to achieving a number of social goals and define appropriate methods and expenses (Wiener, 1963). That would, first and foremost, filter out most of the useless dreams and ideals about the bright future and the better man that claimed many millions of lives on our planet.

Life Is Grey, but the Tree of Theory Is Evergreen     73

People as living beings are images who depict themselves. We communicate with each other, interpret ourselves, represent ourselves within our milieu, being an integral part of it, which is the only judge in this big show under the Sun. As opposed to the machines communicating on the level of signals and codes, we communicate with the help of symbols and signs. This ability and relevant expertise developed in the course of evolution must be supported and further developed not only for the superiority over the machines in communication. The sad truth of the present day is that, on the one hand, the scientific and technological elites move the machines closer to the people, and, on the other hand, the social and political elites move the people closer to the machines (Alpidovskaya et al., 2018). That is all that I meant to say about the studies of artificial intellect and science in general in view of the metascientific nature of our conference. Unfortunately, all that is not only about modern science (Alpidovskaya, 2018). REFERENCES Adams, D., (2002). The hitchhiker’s guide to the Galaxy. Moscow, Russia: OZONE.RU. Alpidovskaya, M. L. (2018). Integrity as an imperative of the expanded reproduction of the national economy. The Problems, 43, 12–17. Alpidovskaya, M. L., Gryaznova, A. G., & Sokolov, D. P. (2018). Regress economy vs progress economy: “Alternatives of senses.” In E. Popkova (Ed.), The impact of information on modern humans (Vol. 622, pp. 638–646). Cham, Switzerland: Springer International. Andersen, H. Ch. (1956). Fairy tales. Prague, Czech Republic: SNKLHU. Bateson, G. (2006). Mind and nature, necessary unity. Prague, Czech Republic: Malvern. Bible, Czech Biblical Society. (2015). People or machines. Andrology 2016/2017. Flegr, J. (2015). Evolutionary melting. Prague, Czech Republic: Academia. Goethe, I. V. (2003). Faust (N. Kholodkovsky, Trans.). Retrieved from http://lib.ru/ POEZIQ/GETE/faust_holod.txt Harari, Y. N. (2017). Homo deus. Prague, Czech Republic: LEDA. Hrubec, M. (2011). From disgrace to justice. Czechia, Czech Republic: Institute of Philosophy, AV CR. Neubauer, Z. (2009). What is science about. Prague, Czech Republic: Malvern. Wiener, N. (1963). Cybernetics and society. Prague, Czech Republic: CAV.

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CHAPTER 9

FORECAST FOR THE DEVELOPMENT OF HUMAN CAPITAL IN THE AGRICULTURAL SECTOR AT THE REGIONAL LEVEL Alexander W. Turyanskiy Belgorod State Agricultural University Andrei F. Dorofeev Belgorod State Agricultural University Alina I. Dobrunova Belgorod State Agricultural University Tatiana V. Kasaeva Pyatigorsk State University

Meta-Scientific Study of Artificial Intelligence, pages 75–84 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

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76    A. W. TURYANSKLY et al.

ABSTRACT In this research a technique of economic and mathematical modeling of forecasting human capital (HC) of the agricultural sector of the economy in the Belgorod region, business-as-usual and innovative scenarios that improve the economic efficiency of agriculture in the region is proposed. A special direction in the development of estimation tools for predictive parameters of the rural population of different age cohorts, the number of students in universities and colleges in the field of agriculture, the parameters of the development of agricultural production as a basic sector, is the use of methods and means of artificial intelligence to expand the functionality of the study. On the basis of the analysis of the optimization model it was found that the proposed changes in production structure of the industry will improve the level of production efficiency in agriculture as a whole (the level of profitability under the business-as-usual scenario will be 21.2%; the base scenario, 55.7%; and the innovation one, 82.8%), and for certain categories of management, as well. In making the calculation, not only the total population living in rural areas, but also the population makeup according to sex and age were predicted. At the same time, the calculation of the male and female population was made separately. In addition, in the process of building the forecast model the indicators of fertility, mortality, and migration of the population were taken into account, as they are not constant during the forecast period. The use of the proposed method of forecasting the rural population shows that in the future there will be a steady downward trend for both the total rural population and the people of working age.

State policy in the field of innovative development of human capital (HC) in the agricultural sector, as one of the main directions of socioeconomic development of the country, is a set of goals and measures aimed at sustainable development of agriculture. The implementation of this agricultural policy involves not only a comprehensive analysis of the current state and forecast parameters of the development of qualitative and quantitative components of HC development in rural areas, but also the development of productive forces and industrial relations in the agricultural sector of the country’s economy. When assessing the ambition level of development of socioeconomic relations in the agricultural sector, it is necessary to conduct a comprehensive analysis of the level of compliance of the state of development of agricultural production with the subsystem of HC reproduction. For complex and effective state regulation of the reproduction of human capital in the agricultural sector forecasting subsystem is particularly important because the development of predictive parameters allows to determine the forward looking indicators of the state of supply and demand for labor in the village and the level of organization of the reproduction of HC of the rural population in the Belgorod region.

Forecast for the Development of Human Capital in the Agricultural Sector    77

METHODOLOGY Based on the study of existing methodological approaches to the development of HC development strategy, the stages of forecasting its strategic parameters were proposed: Phase I: diagnosis of the functioning of HC. Stage II: preparation of baseline information for the development of economic and mathematical models. Stage III: development of parameters Stage IV: implementation of scenario options for the optimal plan of HC development. The study was conducted on the basis of systematized and generalized experience of functioning of economic entities of the agricultural sector of the Belgorod region. The prediction of changes of demographic component in the development of human capital is an integral part of the reproductive process, the development of agriculture (Dorofeev, 2015). To make this prediction we used the technique of aging, or the component method. RESULTS Forecast calculations showed that in the analyzed period (up to 2030) there is a tendency of growth of the birth rate in all age categories, a slight decrease in the birth rate is observed only in the categories of 15–19 and 20–24 age groups (the actual birth rate was analyzed; Statistical Yearbook, 2016). The increase in the birth rate is associated with the implementation of the state policy aimed at strengthening family values, the orientation of young people to have a large family, improving social and engineering infrastructure, increasing the level of income of the population, the payment of maternity capital, the implementation of state targeted programs aimed at supporting the young family, and a set of measures that allow families to fully realize their reproductive plans. It is obvious that in the forecast period (2018–2030) the demographic situation will be influenced by the policy implemented by the state aimed at increasing the birth rate and reducing the mortality, in particular, measures to implement the presidential decrees: 1. About the approval of the concept of demographic policy of Russia for the period till 2025 (The Decree of the President of the Russian Federation No. 1351 of 9 October 2007).

78    A. W. TURYANSKLY et al.

2. About measures for implementation of demographic policy of Russia (The Decree of the President of the Russian Federation No. 606 of 7 May 2012). 3. On improving state policy in healthcare (The Decree of the President of the Russian Federation No. 598 of 7 May 2012). Developed forecast (Figure 9.1) based on the increase in the migration balance due to the fact that the attractiveness of the Belgorod region as one of the centers of internal and external migration will grow. The main prerequisites for this will be a growing deficit in the labor market, the overall growth of socioeconomic development of the region, improving the standard of living, and so on. At the same time, one of the tools of migration policy will be the implementation of the concept of migration policy of the Belgorod region for the period up to 2025 (Bogdanovsky, 2010), the implementation of the main provisions of which is aimed at increasing the level of immigration to the region. Analysis of the forecast parameters confirms the current trend of growth of the total population size on average in Russia, but at the same time there is a decrease in the rural population size. In the Belgorod region there is a similar situation, in particular, there is an increase in the total population size by 23,318 people or 1.5%, but the rate of decline in the rural population is lower than the average for the Russian Federation, so in the forecast period there was a decrease of 3.0% or 15,478 people (Figure 9.2). The decrease in the population of rural areas is determined by the decrease in the prestige of agricultural professions, the drop in the living standards in the countryside, the outflow of the most active part of the working population to the urban area (Dobrunova, 2012).

148.4

Population size (in millions)

146.5

148.0 148.2

147.2

147.0 147.4

145.5

148.1

147.9

145.3

148.9

148.2

146.2

144.9

Total population Rural population

37.9

37.7 37.8

2016

2017

37.2 37.5

2018

2019

36.4 36.8

2020

2021

35.2 35.7

2022

2023

34.9

2024

2025

2026

Figure 9.1  Population forecast in the Russian Federation.

34.6

34.9 35.1

35.0

2027

34.6

2028

2029

2030

Forecast for the Development of Human Capital in the Agricultural Sector    79

Population size (in thousands)

1,574.1

1,570.4

1,566.5

1,562.7

1,559.7

1,556.4

1,552.5

1,575.8

1,572.2

1,568.5

1,564.5

1,561.2

1,558.1

1,554.6

1,550.1

Total population Rural population

510.5

505.3 507.8

2016

2017

500.7 502.9

2018

2019

496.9 498.7

2020

2021

494.5 495.5

2022

2023

493.3 493.8

2024

2025

492.7 492.9

2026

2027

492.4 492.5

2028

2029

2030

Figure 9.2  Population forecast in the Belgorod region.

There is an increase in the total number of people of retirement age and the rural population in the country as a whole, for the analyzed period it will be 14.8%. In addition, by 2030, on average, the share of the people older than the working age of the total population in Russia will be 27.8%, which is higher than the level of 2016 by 3.2 p.p. At the same time, the proportion of the population over the working age in rural areas is growing at a faster pace, so, by 2030 their share will be 31.4% or increase by 6.4 p.p. There is a steady downward trend in the population of working age. In Russia, during the projected period there will be a decrease of 7.4% in the total population, 17.5% in the rural population, and their shares will be 52.4% and 49.8%, respectively, by 2030. In the Belgorod region, the total population and rural population will decrease by 4.8% and 9.4% or 2.9 p.p. In the forecast period, the population younger the working age will experience an increase. The next stage of the study was to predict the number of students. In 2015, 100 educational institutions accounted for 110 pupils in urban areas and 97 in rural areas. Additional 29 units of educational institutions need to be constructed and brought into use to ensure the standard fill rate. As the number of pupils increases, the lack of educational institutions will be more than 38 units. Children’s preschool education coverage, as a percentage of the number of children of the appropriate age in urban and rural areas by 2030 should be 78.8 and 66.8%, respectively (Table 9.1). By 2030, in urban areas there will be an increase in the number of students in general education institutions by 37.6%, in rural areas by 12.9%, which in turn will increase the pressures on each educational institution in urban areas by 65.8%, in rural areas by 22.7%.

Indicators

2016

694

312 382

The number of institutions necessary to provide the projected number of places for children in pre-school educational institutions (with the standard fill rate; total)

  cities and urban-type settlements

  rural areas

29 –20

  cities and urban-type settlements

  rural areas

Lack of institutions (total)

76.3

69.0

53.5

17,295

  rural areas

Coverage of children in pre-school education, as a percentage of the number of children of the appropriate age (total)

  cities and urban-type settlements

17,495

53,074

  cities and urban-type settlements

  rural areas

53,174

70,369

The number of pupils in the institutions of preschool education and childcare facilities (total, p)

7,657

–15

29

386

312

698

52.3

75.3

67.9

70,669

33,447

69,716 33,320

  cities and urban-type settlements

104,104

2015 103,036

  rural areas

Number of children aged 1 to 6 (total)

Assessment

Fact 2017

–11

30

391

313

704

52.9

74.4

67.6

17,695

53,274

70,969

33,426

71,584

105,010

2020 73,550

2

32

404

315

719

55.,7

72.8

67.5

18,295

53,574

71,869

32,859

24

35

426

318

744

62.6

75.4

71.6

19,295

54,074

73,369

30,809

71,714

102,523

2025

Forecast 106,409

Years

45

38

448

321

769

66.8

78.8

75.1

20,295

54,574

74,869

30,385

69,253

99,638

2030

TABLE 9.1  Forecast of the Number of Students in Preschool Educational Institutions of the Belgorod Region, people

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Forecast for the Development of Human Capital in the Agricultural Sector    81

This increase is due not only to the population growth between the ages of 7 and 17, but also to a downward trend in the number of general education institutions, both in rural and urban areas. In the future, the growth in the workload may lead to a deterioration in the quality of educational services, and, consequently, to a deterioration in the competitiveness of human capital of the rural population (Table 9.2). In the future, there will be a tendency to reduce the number of students enrolled in training programs to provide a skilled workforce, by 2030 there will be a decrease of 1,786 people or 31.9%. At the same time, there is a steady trend towards an increase in the number of students enrolled in training programs to provide middle-level specialists (there is an increase of 5,255 people or 24.1%), and their number will increase by 30.8 people per 10,000 of the population and will amount to 171.7 people compared to 2015 (140.9 people; Table 9.3). TABLE 9.2  Forecast of the Number of Students in Educational Institutions of the Belgorod Region, people Years Fact Indicators

Firecast

2015/2016 2016/2017 2020/2021 2025/2026 2030/2031

Population aged 7 to 17  Total

153,673

157,982

176,257

195,292

198,541

 cities and urban-type settlements

101,324

104,575

119,648

134,990

139,451

52,349

53,407

56,609

60,302

59,090

  rural areas

Number of students in educational institutions (not including evening [shift] educational organizations), th. p  Total

147,700

151,837

169,401

187,695

190,818

 cities and urban-type settlements

97,386

100,508

114,994

129,739

134,027

  rural areas

50,314

51,329

54,407

57,956

56,791

147,000

151,137

168,701

186,995

190,118

 cities and urban-type settlements

96,924

100,044

114,519

129,255

133,535

  rural areas

50,076

5, 093

54,182

57,740

56,583

Including in state and municipal  Total

The number of students per 1 educational institution (not including evening [shift] educational organizations), p  cities and urban-type settlements

550

574

689

826

912

  rural areas

128

132

142

156

157

82    A. W. TURYANSKLY et al. TABLE 9.3  Forecast of the Number of Students in Professional Educational Institutions of the Belgorod Region Years Fact Indicators

Firecast

2015/2016 2016/2017 2020/2021 2025/2026 2030/2031

The number of students enrolled in training programs to provide a skilled workforce, p

5,600

5,487

5,017

4,418

3,814

The number of students per 1 organization engaged in the training of skilled workers, p

1,400

1,372

1,272

1,147

1,022

The number of students enrolled in training programs of skilled workers, employees per 10,000 p

36.2

35.4

32.2

28.2

24.2

The number of students in the professional educational organizations enrolled in training programs to provide middle-level specialists

21,800

22,155

23,555

25,305

27,055

The number of students per 1 organization engaged in training programs to provide middle-level specialists, pers.

623

633

673

723

773

The number of students engaged in training programs to provide middlelevel specialists, per 10,000 p

140.9

142.9

151.2

161.5

171.7

There will be a tendency to increase the number of students enrolled in bachelor’s, specialist’s, and master’s programs in higher educational institutions, so by 2030 there will be an increase of 13,650 people or 25.7%, while the growth of the number of students per 10,000 of the population will be 23.8% (Table 9.4.). The analysis of the forecast showed that the trends of decrease in the total rural population of the Belgorod region in the future. CONCLUSIONS On the basis of the analysis of the optimization model it was found that the proposed changes in production structure of the industry will improve the

Forecast for the Development of Human Capital in the Agricultural Sector    83 TABLE 9.4  Forecast of the Number of Students in Higher Educational Institutions of the Belgorod Region, people Years Fact Indicators

Firecast

2015/2016 2016/2017 2020/2021 2025/2026 2030/2031

The number of students enrolled in bachelor’s, specialist’s, and master’s programs, thousand people.

53,100

54,010

57,650

62,200

66,750

The number of students per 1 organization

8,850

9,002

9,608

10,367

11,125

The number of students enrolled in bachelor’s, specialist’s, and master’s programs, per 10,000 of population

342.2

348.4

370.0

397.1

423.6

level of production efficiency in agriculture as a whole (the level of profitability under the business-as-usual scenario will be 21.2%; the base scenario, 55.7%; and the innovation one, 82.8%), and for certain categories of management, as well. In the process of developing the forecast, it was found that there is a steady trend of population aging, which is confirmed both on average in Russia, and in the Belgorod region. There is an increase in the total number of people of retirement age and the rural population of Russia; older than the working age of the total population in Russia will be 27.8%, which is higher than the level of 2016 by 3.2 p.p. At the same time, the proportion of the population over the working age in rural areas is growing at a faster pace, so, by 2030 their share will be 31.4% or increase by 6.4 p.p. The forecast parameters of the rural population size obtained as a result of the study indicate that there will be a downward trend not only in the total population size, but also in the number of workers employed in production. Thus, according to the base scenario, there will be a decrease in the number of employees of agricultural organizations by 2,910 people or by 4.9%, according to the innovative scenario, by 3,154 people (4.7%), and the business-as-usual scenario by 3,507 people (4.4%). At the same time, there will be a decrease of the total rural population by more than 4.4 % and the working-age population by 2.3 %.

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REFERENCES Bogdanovsky, V. A. (2010). Employment in agriculture of Russia: A difficult way to the market. Economy, labour, Management in Agriculture. 1(2), 48–53. Dobrunova, A. I. (2012). Problems of management training for the agrarian and industrial complex of the Belgorod region. In Innovative ways of agricultural development at the present stage. Proceedings of the XVI International scientific and production conference. 286. Belgorod, Russia: Belgorod State Agrarian University. Dorofeev, A. F. (2015). The cluster approach to development of rural areas. Bulletin of Orel State Agrarian University, 2(53), 94–100. Statistical Yearbook. (2016). Statistical bulletin, Belgorod, Russia: Belgorodstat. The Decree of the President of the Russian Federation No. 598 of 7 May 2012 “On the improvement of state policy in the field of health.” http://base.garant .ru/70170948/ The Decree of the President of the Russian Federation No. 606 of 7 May 2012. “About measures for implementation of demographic policy of the Russian Federation.” Retrieved from http://base.garant.ru/70170932/ The Decree of the President of the Russian Federation No. 1351 of 9 October 2007 (edition of 01.07.2014). “About the approval of the Concept of demographic policy of the Russian Federation for the period till 2025.” Retrieved from http://www.consultant.ru/document/cons_doc_LAW_71673/

CHAPTER 10

A MODERN MAN The Dialectic of His Place and Role in the Modern Digital Society Marina L. Alpidovskaya Financial University

ABSTRACT The inexorable logic of modern times in terms of globalization of the global economy and fight for the remaining markets re-puts on an agenda the issue of necessary regard with the balance of forces. Russia as a great power can’t allow losing its place in the sun. A new feature of socioeconomic progress is both a hardly solvable problem and a particular chance for modern Russia that must surpass in catching a mobilization tool. And what place will be given to man in this confrontation and fight?

At the turn of the 20th and the 21st centuries, humanity faced two paths. The choice was very limited: either a path towards the ultimate globalization of all socioeconomic processes where the role of a man won’t be clear or a path to human revival with a primary emphasis on the development of a man as a thinking and creative body, implementation of human capabilities and satisfaction of needs that don’t concern their material essence (Alpidovskaya, Gryaznova, & Sokolov, 2018, p. 639). Meta-Scientific Study of Artificial Intelligence, pages 85–90 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

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As it was found out, the modern economy is far from development in favor of man as an individual. The economy turned from a system of endless opportunities and abundances into a society of privations, losses, and exigencies due to permanent economic crises destroying the economy as well as the socioeconomic system in general (Semenov, 2013, p. 591). Each state has a demand to implement its own big strategy, the idea and aim hereof consists not only in the integration of knowledge, art, and cultural beliefs, but also in integration of forces (including military ones). The purpose of this process is to determine the world’s effectiveness. However, the issue arises . . . What capacity will Russia as a power and civilization have in this new world? For what purposes? And by what means and by what bodies? What place will be given to a man in this confrontation? METHODOLOGY To resolve the set-up issue on the role of Russia as a civilizational foundation and the essence of the global economic system, its qualitative content as well as the place of a man and his dialectic image of creator, producer, and implementer of opportunities in a new digital society we offer to use a political and economic analysis of socioeconomic relations developing within the production, distribution, exchange, and consumption of material goods and services, economic categories, and laws. Also, we studied the relations of ownership and alienation in a market economy, reproduction laws at the national and international levels, including public role herein. RESULTS From the viewpoint of the world history, Russia is unconditionally described by a mobilization prescription: When Russia nears the downfall, it replaces its clock-like daily rate with a mobilization outpacing economy that is essentially comparable to the most advanced ones in the West. This is tough and rude; this is dictated by the country’s position in the world. (Kornyakov & Alpidovskaya, 2015)

And when Russia gains its crucial socioeconomic advantages that consist in achieving a high level of socioeconomic development (sometimes by full immersion into forced autarky) possible only for the developed countries of the Western economy, some demands of the global world immediately disappear. What does this socio-economic system look like today? According to Galbraith (1969), another overpromoted topic of all possible and inconceivable

A Modern Man    87

Figure 10.1  The growth rate of bifactorial (labor plus capital) productivity in the United States of 1948–2015. Source: Gurova, 2017.

scientific events became digital (previously information) economy with its displaced source of power from capital to organized knowledge: “The source of power at the industrial enterprise will be replaced once again: this time from capital to organized knowledge” (p. 97). Actually, the digital economy is a future one . . . However, why are the abilities to growth of productivity and quality, efficiency, flexible response to changes in the economic situation not yet visible (Figure 10.1). In accordance with the already established views, the acceleration of economic growth over the last 200–250 years of human existence was invoked by three successive scientific and technical revolutions (STR). The first STR (1750–1830s) brought the invention of the steam engine and railway construction. The second STR (1870–1900s) presented an internal combustion engine and electricity. Then appeared “telecommunication, cinema, house water supply, elevator, household electrical appliances and cars, airplanes, highways. Inventions of the second revolution had lasted until the ’70s of the 20th century (TVs, air conditioners, etc.)” (“The World is Waiting,” 2012, para. 6). The third STR (from 1960 till present times) developed the first computers. The first industrial robots were introduced in 1961 by General Motors. By the end of the 20th century, mass access to the Internet appeared. Nowadays, fundamentally new inventions don’t appear. All innovations are limited to modernization, improvement, and updating (upgrading) of the previously invented things. It is practically impossible to uncover

88    M. L. ALPIDOVSKAYA

the relation between the increasing rate of investments into information technology and productivity trend in economic industries applying these technologies and creating their own saleable and paid product. All this is accompanied by a world-scale economic downturn. In 2000, Robert Merton Solow, the Nobel Prize winner in Economics of 1987 for fundamental research in the theory of economic growth raised a question: “How did the introduction of information technologies influence the growth of labor productivity in different industries?” As a result, the American scientist found out that it increased only in the industry of computer production: The growth of the US economy in the next century will achieve an initial level, i.e., 0.2 per cent per year. At the same time, the US won’t become a global periphery and will remain the largest economy of the planet and the driver of its growth. Of very weak growth, he says. (“The World is Waiting,” 2012, para. 9)

What will be the product of the future economy? The benefits created by modern economy are the same benefits and services of the previous stage of economic development (late industrialism). The new product of the digital economy won’t be able to replace and fill the entire list of social production goods. A man is not a cyber machine and doesn’t correspond to electronic-mechanical automata. He is not able to satisfy his direct physiological needs of nutrition by a digital (information technology) product and use it as clothes or means of transportation, and so on. It can’t be in any other way . . . The new computerized economy won’t be able to eliminate the reproduction framework of its previous configuration (related to industrialization). “The former industrial economy will continue to act as the main user and consumer of the information product manufactured” (Partsvaniya, 2018, p. 245). And owing to this fact, a man will play a significant and crucial role herein. The imposition of a consumer lifestyle occurs successively and systematically. The society states that a man as a developed and smart individual striving for endless self-improvement will be able to express himself without labor (Peccei, 1985, p. 161). Actually, the consumer function of the administration (no way the labor one) is the embodiment of life ideals, aspirations, ambitions, social significance, and status weight (Alpidovskaya, 2018, p. 16). Unwillingness to be involved in labor (to study, to learn new information, to master abilities and skills) is a new substance and role of a modern man. A modern individual consumer avoids labor. And this is manifested in the underestimation of labor significance, unwillingness to work by profession or vocation, to improve knowledge, abilities, and skills, to continue professional learning (Elmeev, 2007, p. 419). Modern postindustrial and information-digital patterns of explaining reality push and alienate insofar not labor as a substance and a creator of

A Modern Man    89

value as much as a man, “It seems that the center of the whole society is not labor, but exchange and consumption; it does nothing except change and consumption” (Elmeev, 1999, p. 19). Over the past 400 years, capitalism has acquired and restructured all free non-capitalist zones, pursuing a similar expansion policy. However, capital has always existed, it exists today and will exist in the future . . . Actually, for the purpose of self-preservation today, capitalism begins to remove everything that previously created the institutional pillars. These institutions interfere with the capital. The state in itself is dying for the benefit of transnational corporatocracy (A Super Corporation Rules the World, 2018, para. 8), civil society is contracting, policy has become a show business, mass education has certainly been broken up. While the political elite is still unable to understand that all its activities are managed and controlled by the money markets, “countries that had enjoyed the prosperity till the moment are now devouring the social component of their framework even faster than destroying the environment” (Martin & Schumann, 2001, p. 154). In turn, the alienation of man from labor and the subsequent alienation of man from man in itself (out of conscience) is one of the final stages of the modern era. CONCLUSIONS The modern state and the society under governance hereof has a very unclear future. The elite is able to resolve disputes accumulated in the socioeconomic system. However, when fulfilling first and foremost its own economic interests, which of the three possible options will it choose? The first of them is the implementation of fundamentally new innovations able to raise labor productivity and, consequently, the population’s standard of living? But this will require heavy investments. The second one is to launch wide-scale military activities, throwing the majority of the population of the planet into archaic? But this again will require big financial investments, and the implementation of the archaization project of the socioeconomic system can be carried out without attracting money schemes to the military industry. And the third option is the most true-to-life: to simulate activities, while having got an additional opportunity of budget funds use. It will be an imitation of everything: innovation, social and economic life. In fact, the so-called digital economy of the future is related to the abovementioned where the place of a man is occupied by a new being for whom creative labor demanding a remarkable energy, patience, and perseverance in overcoming the difficulties arising becomes unattainable.

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ACKNOWLEDGMENTS This chapter is published with the support of the Russian Foundation for Basic Research (RFBR) grant, project No. 18 010 00877 A “The Issues of the 21st Century Global Economy Configuration: The Idea of Socio-Economic Progress and Possible Interpretations.” REFERENCES A Super Corporation Rules the World. (n.d.). Retrieved from http://aktiv.com.ua/ archives/5912http://www.finmarket.ru/main/article/3072313 Alpidovskaya, M. L. (2018). Integrity as an imperative of the expanded reproduction of the national economy: The problems of modern fundamental economic science. Theoretical Economics, 1(43), 12–17. Alpidovskaya, M. L., Gryaznova, A. G., & Sokolov, D. P. (2018). Regress economy vs progress economy: “Alternatives of senses.” In E. Popkova (Ed.), The impact of information on modern humans (pp. 638–646). Cham, Switzerland: Springer International. Elmeev, V. Ya. (1999). Towards a new paradigm of socio-economic development and social cognition. St. Petersburg, Russia: St. Petersburg University. Elmeev, V. Ya. (2007). The social economy of labor: General foundations of the political economics. St. Petersburg, Russia: House of St. Petersburg University. Galbraith, J. (1969). New industrial society. Moscow, Russia: Progress. Gurova, T. (2017). Brief dictionary for elections. Expert. 17(1027). Retrieved from https://expert.ru/expert/2017/17/kratkij-slovar-dlya-vyiborov/ Kornyakov, V. I., & Alpidovskaya, M. L. (2015). On the Russian plan of actions and its place in the world economy: Overcoming the obstacles. Bulletin of Tver State University, Economics, and Management Series. 1(1), 57–68. Martin, G.-P., & Schumann, H. (2001). Trap of globalization: Attack on prosperity and democracy. Moscow, Russia: ALPINA. Partsvaniya, V. V. (2018). Genealogy of alienation: From an abstract person to a particular one. Retrieved from http://anthropology.ru/ru/text/parcvaniya-vv/genealogiya -otchuzhdeniya-ot-cheloveka-abstraktnogo-k-cheloveku-konkretnomu Peccei, A. (1985). Human qualities. Moscow, Russia: Progress. Semenov, Yu. I. (2013). Philosophy of history: General theory of the historical process. Moscow, Russia: Triksta. The World is Waiting for a Thousand-Year Decline. (2012, October 3). Retrieved from http://www.finmarket.ru/main/article/3072313

CHAPTER 11

ARTIFICIAL INTELLIGENCE IN JOURNALISM Prospects, Challenges, and Problems Svetlana N. Gikis Pyatigorsk State University

ABSTRACT The chapter discusses the theory and practice of applying artificial intelligence (AI) algorithms in journalism as a whole, as well as in the activities of individual editors (the experience of The Associated Press). The advantages of AI in working with big data and in routine tasks performance, the problems of technological, legal, and ethical nature limiting the use of AI in journalism are analyzed. In the process of identifying the prospects, challenges, and problems of the use of artificial intelligence in journalism the author identified three most promising areas of application of AI in the work of mass media (optimization of work processes, processing of big data, reducing the time for routine work); four technological and three sociopolitical challenges, as well as two key problems—the problem of sharing responsibility between journalists and programmers and the problem of copyright protection when secondary content created by AI.

Meta-Scientific Study of Artificial Intelligence, pages 91–97 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

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The spread of information technologies in the late 20th–early 21st centuries had a significant impact on all spheres of human activity, including journalism, where the effect of their application is characterized as “the great revolution in journalism” (Salazar, 2018, p. 21). At the same time, the journalistic community has faced the professional challenge in the form of the need to structure the information chaos, data verification, and ensuring the accuracy of the news. As a means of improving the efficiency of journalistic activities, an increasing number of editors are turning to artificial intelligence (AI) technologies introducing its algorithms into work for the content production and management. Thus, in the newspaper The Los Angeles Times there is AI “Bot Quake” engaged in the publication of news about earthquakes in Los Angeles without staff participation. The Associated Press news agency uses AI technology “Automated Insights” to create reports on any issues: from the publication of income information of state-owned companies to the baseball league team performance, however, by admission of the agency’s management, AI allows journalists to save up to 20% of working time (Salazar, 2018). The introduction of AI in journalism creates not only advantages, but also problems of technological, legal, ethical nature, the analysis of which an increasing number of scientists around the world is engaged. In this regard, this chapter discusses the practice of AI in journalism and identifies the advantages, challenges, and problems faced by journalists with the implementation of AI algorithms in the work of mass media editors. METHODOLOGY Content analysis of documents and publications devoted to the use of artificial intelligence in journalism is chosen as the main method of research. In this case, scientific works, reports on the problems of artificial intelligence in journalism made at international conferences of the last 3 years by experts in this field were analyzed. All key ideas are systematized and divided into thematic blocks corresponding to the topic of the chapter: first, the main prospects, then technological and sociopolitical challenges, and in the final part, two key ethical and legal problems of the use of AI in journalism are consistently identified and characterized. Particular attention is paid to the analysis of texts and videos of interviews with representatives of the journalistic community, in which reporters and editors spoke about the practice of using AI for publishing separate columns. The case-study method was also used in the preparation of the first part of the chapter: it analyzes the experience of implementation of artificial

Artificial Intelligence in Journalism     93

intelligence algorithms in The Associated Press (advanced international agency in this area), mentions the experience of other editorial offices, which are already using AI technologies as an experiment. RESULTS Artificial intelligence in the form of intelligent algorithms and automated processes penetrates and in all spheres of society, including information. The concept of AI involves the use of machines for activities previously performed by people, including journalism. In the work of mass media AI algorithms are already used at all stages of media text creation, including data collection, their analysis to identify new trends and ideas, combining the revealed facts into a logically coherent text. The spread of AI technologies has led to the formation of a new scientific field—social physics, the main postulate of which is the idea that each type of human activity leaves a digital footprint, like physicists who learn the world through the study of atoms, human nature can be learned through the study of actions and ideas recorded in the digital world (Latar, 2018). The algorithms developed by programmers are able to analyze the accumulated information about people and their participation in certain events, divide it into text, photo, video, sound, synthesize media content from fragments. Innovations in the field of AI can ensure the appearance of the first fully automated newsrooms, in which robots would take on the key functions of journalists—the chief expert in the field of forecasting R. Kurzweil (of Google) and Nobel prize winner in economics, D. Kahneman, have complete faith in it (Latar, 2018). The question of whether robots will be able to completely replace journalists is related to the technological limitations of AI and how important they are for journalistic practice. The use of AI in the work of journalistic newsrooms from the very beginning provided three main directions: • optimization of working processes, • processing of big data, and • reduction of time for routine work. The example of The Associated Press deserves special consideration, as this agency is a world leader in the introduction of AI in news production. Basically, they are of a business nature and related to the work of American enterprises. Articles are written and edited by a special platform “Wordsmith” using special templates. The templates are not yet dynamic, the system is used for statistical reporting and gradually gets rid of the errors: in 2014, when the “Wordsmith” was only launched, the editors had to read

94    S. N. GIKIS

each prepared article, while already in 2018, fewer errors were found in its articles than in the texts written by professional journalists. Technically, the system “Wordsmith” is able to generate 2,000 articles per second. It should be noted that The Associated Press includes personalized content among its priorities—in the form of numerous versions of the article for different people. Senior Vice President of Strategy of The Associated Press D. Kennedy, the first to propose the use of AI as an auxiliary technology, has repeatedly noted three unconditional advantages of AI in the newspaper and publishing business: AI is able to create new content for which journalists do not have time, but readers are interested in receiving it; generated by AI content is virtually indistinguishable from written by humans; AI writes articles in a shorter time than people (“Case Study,” n.d.). D. Kennedy also intends to use AI as a system that gives each reader a detailed answer to the question, and this answer will be collected from the previously published articles of The Associated Press in the form of a new text. Another tool of AI applied in The Associated Press is a system of monitoring called “NewsWhip.” It makes it possible to monitor trends on the Internet and social networks, on the basis of which journalists then prepare articles. According to the editors of The Associated Press, E. Carvin and L. Gibbs, The Associated Press News Department uses “NewsWhip” to better understand what stories and topics attract people’s attention during specified periods (Marconi, 2018). In most European newsrooms, AI performs simple tasks, but in the British Reuters and Norwegian NTB the algorithms are already able to compare new information with historical data and interpret them. The second characteristic feature is the publication of automated content without revision of text by editors (Reuters generates 950 warnings and 400 articles per day without human intervention). Journalism using AI is now being separated into a particular field and is called robotic, automated, algorithmic, or AI journalism. Technologically, it has two bases: • computer software that extracts new knowledge from data warehouses using the concept of “social physics” and • algorithms that automatically convert this knowledge into humanreadable stories (Latar, 2015). The art of storytelling itself is also becoming a science using AI algorithms, a promising area of research for software developers. Their algorithms are already able to adjust the tone and narrative structure of stories according to the characteristics of the audience. The advantages of using AI in journalism are quite wide and numerous. The report of The Associated Press noted: “AI can give journalists the

Artificial Intelligence in Journalism     95

opportunity to analyze data, identify patterns, trends and the viable ideas in different sources; see what is invisible to the majority; turn data and spoken words into a text, a text—into audio and video; understand feelings; analyze scenes to find specific objects, faces, and more” (Schmidt, 2017, para.  7). From Hall’s point of view, AI changes traditional journalism in three directions: 1. Automation of routine reporting: Generation of articles on topics that are important to the audience, but are not interesting to journalists due to the reporting nature; 2. Providing rapid visualization of the data: AI is able to respond to changing data and integrate it into the structure of infographics; and 3. Closing the gap between local and federal journalists: AI reduces the human factor in writing history (Hall, 2018). At the same time, along with the advantages, AI creates challenges: • limitation of data availability: AI will be effective only if there are no barriers in obtaining the data necessary for the preparation of the article; • limitations in the interpretation of unstructured data: Tabular results of sport games or company income statements can be easily translated into articles using algorithms, but it will need to use unstructured data that makes up most of the available data to disseminate AI in the social sphere; • limiting self-awareness: AI cannot explain its conclusion: why it was written and what it was exactly done, while it may be important for the journalist and for readers; and • limitation in the verification of truth: most AI is not able to distinguish whether the information obtained is true or false. In addition to technology, there are also social and political challenges for AI: • the need to review the copyright standards; • the need to increase corporate responsibility. Since AI cannot be held responsible, companies should enforce their responsibility; and • the possibility of “The AI race” in the field of licensing information, to which small companies for the sake of survival and earnings will have to resort.

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The introduction of AI in mass media leads to a reduction in the importance of the journalist as a person, but the significance of ethical algorithms is growing. As a result, the problem of division of responsibility between journalists and programmers setting up AI is becoming more urgent, and in the future it promises to serve as a catalyst for the emergence of ethical codes for robotic journalism (Derr & Hollnbuchner, 2017). The most significant legal challenge for AI in journalism is the ability of AI to create a content of the libellous nature. The main difficulty for legal institutions during the court proceedings in cases of this kind is the inability to prove the intent of the offender convicted of slander for the publication of a text generated by AI, since the accused formally should know that the information published by him is knowingly false, and have the intention to defame someone’s name and business reputation, while it is obvious that AI does not have either this knowledge or intention because of the inability to subjective judgments (Lewis, Sanders, & Carmody, 2018). Another legal challenge in the field of AI application is the problem of intellectual property in news production. In this case, it is not clear who is considered the author of the content generated from several objects of copyright, and who should make a profit. A related problem may be the lack of permission from the author of the original source to use it or even a direct ban on it. From the point of view of the Executive Director of NYC Media Lab D. Hendrix, AI has made a fundamental change in the functioning of media. Its application changes the way content is produced, distributed, consumed, and monetized. At the same time, in his opinion, “where there are opportunities, there are also problems, and the main among them are organizational problems of attracting new talents who will be able to promote and apply these technologies. There are others, from ‘fake news’ to ‘news bubbles,’ from privacy issues to cybersecurity” (Marconi, 2018, p. 20). CONCLUSIONS Summarizing the study, we note that the three most promising areas of the use of AI in journalism should be considered: getting rid of routine work for journalists, expanding their capabilities in the field of information and work with big data, and improving the quality of interaction with the audience. As the main problems associated with automated journalism, it is possible to note (a) the risks inherent in the uncontrolled generation of algorithmic news, (b) the possibility of disruption in the workflow, and (c) the growing gap in the set of skills needed to manage this area and the amount of skills that modern journalists really have.

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Thus, creating new opportunities, AI is able to help the development of journalism, but not to replace it completely. It is a useful tool, but the emergence of new tools does not mean the abolition of general professional rules and ethical standards. In these circumstances, it is recommended for the newsrooms which use the AI algorithms to think about strengthening the ethical control over the outcome of the work of AI, to prevent the proliferation of publications with false data and information of slanderous character. REFERENCES Case Study: Why Artificial Intelligence is a Big Part of AP’s Future. (n.d.). Retrieved from https://www.tcs.com/perspectives/articles/why-artificial-intelligence-is -a-big-part-of-APs-Future Dörr, K., & Hollnbuchner, K. (2017). Ethical challenges of algorithmic journalism. Digital Journalism, 5(4), 404–419. Hall, S. (2018, January 15). Can you tell if this was written by a robot? 7 challenges for AI in journalism. Retrieved from https://www.weforum.org/agenda/ 2018/01/can-you-tell-if-this-article-was-written-by-a-robot-7-challenges-for-ai -in-journalism/ Latar, N. (2015). The robot journalist in the age of social physics: The end of human journalism? In G. Einav (Ed.), The new world of transitioned media: Digital realignment and industry transformation (pp. 65–80). Cham, Switzerland: Springer International. Latar, N. (2018). Robot journalism: Can human journalism survive? Singapore: World Scientific. Lewis, S., Sanders, A., & Carmody, C. (2018). Libel by algorithm? Automated journalism and the threat of legal liability. Journalism & Mass Communication Quarterly, 1–22. Marconi, F. (2018). The future of augmented journalism: A guide for newsrooms in the age of smart machines. Retrieved from https://insights.ap.org/uploads/images/ the-future-of-augmented-journalism_ap-report.pdf Salazar, I. (2018). Robots and artificial intelligence: New challenges of journalism. Doxa Comunicación, 27, 21. Schmidt, T. (2017, May 16). Smarter journalism: Artificial intelligence in the newsroom. Retrieved from https://en.ejo.ch/ethics-quality/smarter-journalism-artificial -intelligence-newsroom

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CHAPTER 12

ARTIFICIAL INTELLIGENCE AS A SPACE OF SPEECH REFLECTION Viacheslav I. Shulzhenko Pyatigorsk State University Leokadiya V. Vitkovskaya Pyatigorsk State University Igor F. Golovchenko Pyatigorsk State University Tatiana D. Savchenko Pyatigorsk State University Marina Yu. Sumskaya Plekhanov Russian University of Economics in Pyatigorsk

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ABSTRACT The chapter deals with the theoretical and practical basis of the functioning of artificial intelligence (AI) as a communicative substance. For this purpose, a major revision was made of the existing ideas about the nature and principles of neorhetoric, which are largely relevant to the goals of the described phenomenon. The relevance of the use in the modern methodology of the well-known scheme of the communicative act of R. O. Jacobson, which confirmed its status as an effective research algorithm in the context of the topic under consideration, is proved. For the first time the theory of dialogue and M. M. Bakhtin’s theory of speech genres are connected with its description, forming the linguo cognitive basis for the creation of optimal communication systems. Moreover, the orientation to the anthropological paradigm of communication remains dominant for the authors, taking into account the specifics of the mentality of the dialogue participants, the mechanisms of speech generation, the language preferences of speakers, and so on. The promising ways of inclusion of AI parameters in general education programs for training specialists at universities are proposed. The chapter outlines measures that can in practice ensure adaptation to the harmonious coexistence of human beings and AI in the form of the thesis.

The positioning of the phenomenon of artificial intelligence (AI) in the public consciousness of Russia and leading European countries is somewhat different, which is to some extent connected with the translation into Russian of the existing English phrase “artificial intelligence,” which does not have in this language the anthropomorphic color, which it acquires in the traditional Russian translation. In this combination, the word intelligence is referred rather to “the ability to think reasonably” than “intellect.” The wide dissemination of AI gave rise to a long-term discussion about the potential threat posed by the replacement of people by machines which might be inevitable in the future. The most unusual conclusions of such controversy are the fruit of the efforts of large audit firms and research centers, whose regular reports are very convincing for the public. Such a strategy never implies neutrality or habitual altruism. In addition to purely information, advisory, and prognostic functions, it has a pragmatic goal—to increase the funding of their research, grants for laboratory development. To date, very opposite views have emerged not only on the current role of AI, but also on the prospects for its development. It is argued that by 2045, machine intelligence will surpass human intelligence, although at the same time there are loud statements that there is no reason to insist on any distinct similarities between the machine and man, which have been identified in recent decades. In this regard, it is often possible to hear calls for the need for a larger level, than there is today, of human generosity, sensitive care, and even “mercy” towards AI. The view that AI is still more fiction than a fundamental object finds many supporters, and its economic

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dominance in the global perspective in a number of statements of major experts is not interpreted correctly. At the same time, everyone who writes about AI, mainly considers it within the framework of a close professional activity, which leads to inevitable elements of subjective judgement and fragmentation, so characteristic of our thinking. Another situation is in Russia, where in the public discussion the rhetorical figures about AI, which can be considered as relevant to ones of the mentioned European authors, are practically not found. At best, Russian colleagues of European specialists become marginal participants of various media projects, which can be considered as the main cognitive and communicative environment for discussing AI problems. It is easily seen that the discussion of AI problems is directly related to speech practices, the importance of which should not be underestimated when we turn to the study of the ways of AI multidisciplinary presentations in the modern world (Avsharov & Shulzhenko, 2016). We are deeply convinced that “the idea of language as a complex cultural and political economic resource” (“Russian Language and New Technologies,” 2014, p. 5) will allow AI to integrate more harmoniously and less painfully into the ongoing global post industrial revolution. METHODOLOGY One of the methodological impetus that has prompted us to study the speech strategies of AI, were the obvious signs of the revival in the last 50 years of rhetoric, or rather, its modern version—neorhetoric. We fully recognize that the surge of interest in neorhetoric around the world is associated with the emergence of the phenomenon of AI in the human consciousness. Let us state very succinctly that in the neorhetoric is extremely important from the point of view of the methodology. First, it has an obvious metascientific character, that is, it develops through logic, genre studies, legal hermeneutics, poetics, and other disciplines, without which it is impossible to build communication technologies. Secondly, this interdisciplinary approach allows for effective research within the framework of the anthropological paradigm, which highlights the linguistic personality. Third, neorhetoric by its very nature is able to formulate as fully as possible the tasks facing AI as a communicative institution, and, moreover, become the “key” to forward-looking decisions in this area. Another methodological principle, which largely determined the concept of our study, is associated with the teaching of M. M. Bakhtin on dialogue and the theory of speech genres, which is actively developed by many Russian scientists. Along with the lexical and stylistic approaches, we focus on the speech one as well. It is due to the latter that the model of the speech

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genre is built. We are deeply convinced that the improvement of AI will take place in the direction of using AI in a wide range of speech patterns. Another principle of the proposed work, we would call it “methodological and pragmatic,” we consider as one of the most interesting discoveries of applied linguistics of the 20th century—the scheme of the communicative act of R.O. Jacobson. RESULTS We will start the research part of the chapter with R. O. Jacobson, “the iceberg of formal theory.” It is worth recalling that according to this Russian-Czech-American scientist his “scheme” was designated for a purely linguistic sphere, however, it is widely used in the classroom for such a complex discipline as “Introduction to Literary Study,” which is in the constant process of development. This scheme is also very effective during the study of key topics on such disciplines as “Linguo Cognitive Basis of Marketing Communications,” “Advertising Discourse,” “Integrated Communications in Public Relations,” “Fundamentals of the Concept of Positioning,” and so on. But R. O. Jacobson’s scheme was especially useful in the procedures of discourse analysis, which, in our view, is becoming so popular thanks to the dissemination of AI and the gaining of new communication niches. AI quite naturally coincided with modern polemic battles, thanks, first of all, successfully formed harmony between the signifier and the signified, directly pointing to the basic meanings of the term “discourse,” which is so important for the study of AI, denoting both methodically disciplined speech, and the language action itself, closely related to the speech genres. That is, in other words, all the complexity of AI as a communicative phenomenon is reflected in this term: on the one hand, the clear desire “to the inferential, rational, institutionalized knowledge [. . .], to the spontaneous knowledge, connected with the live unfinished speech, contrasted with the completed written text, on the other hand” (Kasavin, n.d., para. 1). Discourse, thanks to P. Bourdieu and M. Foucault, is perceived as a social practice, and not as certain statements, texts or speech situations, between which the thematic connection is obvious. Hence, the shift of focus from the “products” or “tools” of communication, which determined the previous choice of linguistic structuralist approaches, to which we have attached so much importance until recently, to the process of “production of texts, meanings and knowledge in digital communication” (Kasavin, n.d., para. 1), what is declared almost in the first line of the dictionary article on discourse analysis. It means that we should consider digital communication activity not as an absolutely new type of social practice, but as a private,

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contextual, situationally dependent type of interaction (Kozhemyakin, n.d., para. 9), where digital technologies, or rather their use, determine the social significance of the event. Moreover, European postgraduate students at Pyatigorsk University, conducting research on Russian literature, widely use the works of J. Jeanette, who found in the Jacobson’s concept the grounds for the differentiation of two types of literature—constitutive and conditional (Jeannette, 1998), which made it possible to speak, firstly, about the “literature on the circumstances” and, secondly, to recognize as literary even not relevant texts. It seems essential to us to highlight that Jeanette’s doctrine of the nonpermanent feature of the essence of art, including the idea of conditional literature, was in the greatest demand for AI, from which since its emergence has been expected to create poetic statements. There were samples of such works, however, they were immediately rejected by, shall we say, responsible subjects of speech. The latter, who still retain their authoritative, but not so dominant, let us be objective, positions in public opinion, continue to insist that there is and will always be an impassable border between rational and irrational sources of meaning. “So far,” T. V. Chernigovskaya would certainly add, looking at “victorious march of AI through the planet” with pessimism, if she were our interlocutor at this moment. Jacobson’s context, which before and now refers to the sum, the continuum of cognitive knowledge, providing, in principle, the very meaning of the dialogue, is no less important. Today, it includes intertextual and interdiscursive connections that contribute not only to the description of the meaning of the statement, but also to the understanding of “the processes of reproduction and construction of social relations and communities” (Kozhemyakin, n.d., para. 1). In our days, the conative (imperative) function inherent in addressee returns in many genres of oral speech communication, especially in those that are not alien to AI; namely, advertising messages, sermons, regrets, forgiveness, wishes, flirting, and so on. Without the fatal function, the existence of AI as a communication system is difficult to imagine. For example, in the story of E. Hera “Tales on the Phone,” (Ger, 1999) in principle, all humanistic projects of psychological support of people in trouble would be meaningless if the vocabulary, meaning human sympathy, compassion, kindness would be eliminated from the thesaurus of “rescuer.” The metalanguage function is connected with the code, because any understanding includes its opposition—misunderstanding, which manifests itself in different ways in different communicative acts, which is clearly demonstrated by the increasingly unsuccessful international meetings of Russian and Western politicians. There is a feeling that in any situation everyone understands only his own, while the use of the mentioned function

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is reduced to zero, and here, let us allow ourselves to be sarcastic, whether AI would not be more effective with its ability to search for a compromise endlessly. A few words about the most common and, we believe, the most important function for AI, aimed at the context. It is called a referential one, and refers to the reality that we are talking about, which leads to the increase of our knowledge of the world, and this is where many optimists see the acceptable future of the investigated phenomenon. The creators of the AI, we believe, even intuitively, but already in the early stages recognized the validity of Bakhtin’s provisions for diversity of speech genres and the difficulty of identifying the “general nature of the utterance” associated with them. “Speech genre,” the researcher wrote, “is not a form of language [. . .]. Genres correspond to typical situations of speech communication, typical themes” (Bakhtin, 1979, p. 268). As it is impossible to imagine the social interaction of people without the diversity of genres of communication, and it is impossible to think about the communicative pragmatics of AI without this factor. In order to illustrate all the above, let us turn to A. Tsvetkov’s (2002) story “TV for Terrorists,” written in the early 2000s, when the issues that we are discussing are fait accompli, to date, but they seemed somewhat naïve then, but from the point of view of the development of computer technology everything was guessed correctly. The protagonist of “TV for Terrorists,” the creator of the new television, declares the public need for an alternative television, the effectiveness of which is possible while maintaining strict secrecy of its practice, which can be provided by closed trials, certain chemicals and physical rays, plus the necessary laws that would keep terrorists in prisons separately from other criminals. Here, just note, is one of the aspects of the extremely acute problem of relations between law and AI. The main “feature” of this TV-broadcasting is the ability to cover any spatial object, in our case—the kindergarten captured by terrorists. Of course, the fulfillment of all demands can only be demonstrated in the virtual mode. Television, in this situation, is intended only for them, the state creates the appearance of communication through it, and the criminals themselves do not notice the mass media deception. The project employs a group of actors (today perfectly identical simulacra), playing the role of the most important government officials with whom terrorists often agree to contact. As it was in the case of capture of the detachment of Basayev in Budennovsk in 1995, when the then Prime Minister of the Russian government led negotiations which were extremely humiliating for a self-respecting government with the then “terrorist No. 1” in Russia (Shulzhenko, 2003). In addition, the project had the maximum opportunity to create all sorts of “backdrops” (what we call today “virtual world”)—scenery designed to convince the recipients-criminals that their demands are strictly met. Scenery, by the way, seems to be much more reliable means

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than computer graphics, which can suddenly go out or noticeably ripple, revealing its digital nature (of course, we do not mean today’s omnipotent “digit”). At this time the hero of the story mounts “news” (analog of the start page of the current Google), which 10 minutes later was on the air, displaying a generally positive reaction of society to the demands of terrorists (Shulzhenko, 2011; Shulzhenko & Golovchenko, 2013; Shulzhenko, 2013). Thus, Tsvetkov (2002) creates in his story the original technology of future ways to counter terrorism with the help—if you take into account the nature of such TV—the very speech acts, the algorithm of which we are condemned to teach AI. Among them are not only the standard formulas in speech acts of greeting, apology, advice, persuasion, but also the techniques of language play in the genre structure of flirting, business negotiations, and more of what social life consists, including numerous conflicts, to which it gives rise, along with the need to solve and discuss them. Does not the most attractive educational function of the Internet, which is impossible without grammatical-lexical and intonation-accentual constructions of persuasion, irreplaceable in the transfer of knowledge to new generations, need such a diverse use of speech genres? (Shulzhenko & Sumskaya, 2015, 2017). CONCLUSIONS We, linguists, who came into the sphere of AI with the idea of “humanization” of speech acts, do not always feel calm and confident in it. We seem to have a double responsibility: on the one hand, we are dealing with an inanimate object, the attitude to which cannot be full of with all the variety of emotional sensuality as it would not be quite natural, on the other hand, there is full realization that avoiding emotional contact with him can lead to defects of knowledge. We have to admit that maintaining relationship with AI at the subject-object level, we are at risk to go through our own lives without knowing the complexity of the studied and studying. But only the subject-subject approach is able to open and provide this, which we strive to implement through speech reflection in our studies (Vitkovskaya, Golovchenko, Savchenko, & Shulzhendo, 2019). At the same time, we are fully aware of the danger of the “general case,” which, as we believe, is fatally hanging over the communication strategies of AI. We see our task in defending the uniqueness of each speech utterance because it is addressed to a living person, the personality, and at the same time, we will not give up the prerogatives of the “general case.” We just need to take into account an order of magnitude, more parameters in the algorithm of any prospective communication, as we have already learned to do, working with the text, which for us is not a fragment of the objectified

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world, but, let us face it, often a personality who contributes to the development of the person who studying it. This is the meaning and purpose of our future work. REFERENCES Avsharov, A. G., & Shulzhenko, V. I. (2016). Aesthetic metamorphoses of advertising text. Aesthetics and pragmatics of advertising: Materials of the I International Symposium. Pyatigorsk, 5-10. Bakhtin, M. M. (1979). The problem of speech genres. In Aesthetics of verbal creativity (pp. 237–280). Moscow, Iskusstvo. Ger, E. (1999). Gift of the word. Tales on the phone. Retrieved from https://znamlit.ru/ publication.php?id=672. Jeanette, J. (1998). Figures: Works on poetics. Moscow, Russia: Sabashnikov. Kasavin, I.T. (n.d.). Discourse analysis: Encyclopedia of epistemology and philosophy of science. Retrieved from https://terme.ru/termin/diskurs-analiz.html Kozhemyakin, E. (n.d.). Discourse analysis in the digital age: Capacity-building. Retrieved from https://www.nlobooks.ru/magazines/novoe_literaturnoe_obozrenie/ 138_nlo_2_2016/article/11886 Russian language and new technologies. (2014). Collective monograph. M. V. Akhmetova, V. I. Belikova (Eds). Moscow, Novoye literaturnoye obozreniye. Shulzhenko, V. I. (2003). Budennovsk-95: The experience of deconstruction of the collective unconscious. In Language and text in the space of culture (pp. 357–361). Stavropol, Russia: Stavropol State University. Shulzhenko, V. I. (2011). Poetics of the terrorist attack in the “caucasian text” of Russian literature. Vestnik Pyatigorsk State Linguistic University, 4, 271–276. Shulzhenko, V. I. (2013). The shahid: A profil.. Social Sciences, 44(4), 49–55. Shulzhenko, V. I., & Golovchenko, I. F. (2013). Modern terrorist narrative as palimpsest (art and media discourses). Bulletin of Pyatigorsk State linguistic University, 3, 156–159. Shulzhenko, V. I., & Sumskaya, M.Yu. (2015). Lermontov in modern school: Problems of reading and perception. Russian language and literature in the space of world culture. In Proceedings of the XIII Congress of MAPRYAL, Vol. 15, 139– 144. Saint Petersburg, MAPRYAL. Shulzhenko, V. I., & Sumskaya, M. Yu. (2017). Modes of artistry and types of reading in the algorithm of literary education of schoolchildren. Actual problems of teaching Humanities: theoretical and applied aspects. Collection of scientific materials on the results of the international scientific-practical conference. 204–206. Tsvetkov, A. (2002). TV for terrorists. Saint Petersburg, Russia: Amfora. Vitkovskaya L. V., Golovchenko I. F., Savchenko T. D., & Shulzhenko V. I. (2019). Polydiscursiveness of the “caucasian text” as the basis of literary brand in the economic strategy of the caucasian mineral waters region. In E. Popkova & V. Ostrovskaya (Eds.), Perspectives on the use of new information and communication technology (ICT) in the modern economy (pp. 846–862). Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-319-90835-9_96

CHAPTER 13

COGNITIVE ASPECTS OF ARTIFICIAL INTELLIGENCE SEMIOLINGUISTICS Signs, Concepts, Discourse Andrey V. Olyanich Adyge State University Zaineta R. Khachmafova Adyge State University Susanna R. Makerova Adyge State University Marjet P. Akhidzhakova Adyge State University Tatiana A. Ostrovskaya Adyge State University

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ABSTRACT The chapter covers the problems of reality cognitive development by a phenomenon named artificial intelligence (AI). The chapter purpose is to review the current state of the technology of functioning devices with AI problems, as well as description of such functioning semiotic models. The achievements of modern technologies allowed us to bring the degree of AI cognitive development to capabilities of man, which allows built-in AI behavioral models to realize the processing of information about signs/concepts of the world and, accordingly, actualize it in relevant discourse types. The chapter uses the methodology of semiotic analysis/modeling of processes described. The basic problem for AI researchers is the problem of teaching the machine an emotional perception of reality, a global assessment of actions and events, everything that only Homo sapiens can do today. The task is to teach artificial systems to act, plan, and set goals for themselves, focusing on the constantly changing situation with the help of natural languages semiolinguistic constructions. Proposed is to undertake the development of semio-logical methodology of AI based on principles of logical pluralism and relativism, coherence, fuzzy semantics, diversity of semiotic systems and models, algebraic representations of semiotic matrices.

What is artificial intelligence (AI)? Does it represent a digital cast of natural intelligence, of a thinking biological personality, or, is it something (somebody?!) totally different in nature? Here are a few definitions: 1. “AI is a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation” (Kaplan & Haenlein 2018, p. 17). 2. The AI can be strictly interpreted as a special sign system. Culture was treated as a machine that creates rationality only in semiotic studies, and it was found that human activity is completely dependent on the work of such a machine. So, if the AI is considered as a special kind of culture, then it is possible to find methods of producing bridges between traditional and digital humanities (Kulikov, 2015). 3. “AI is a digital reproduction of processes of conscious activity of a person and society as a whole in terms of creative processing and reasoning on the basis of non-trivially formalized information under the conditions of time and resource constraints of uncertainty and incompleteness of the initial data, creating cybernetic objects capable of independently setting goals and achieving them with with a quality not lower than an average specialist, capable in the

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long term of replacing existing activities and professions” (Gusarova, 2018, p. 8). So, from the definitions, it is clear that speaking of AI, we mean the transfer of human cognitive abilities to a machine, so that the machine learns to cognitively understand the surrounding reality, form judgments and opinions about it, accumulate knowledge and use them for constructive purposes without harming the person. What does cognitively master reality? This means the actualization of several functions: 1. clustering of signs (discrimination, systematization, and typology of semiotic elements of reality); 2. conceptualization of reality (highlighting concepts, combining concepts into concept spheres); 3. categorization of being in general; and 4. discursive actualization of cognitively mastered reality. The chapter proposed to scientific society covers the problems of such cognition mastering of reality based on cognitive and semiotic principles. METHODOLOGY The authors appealed to several methods when analyzing the problem of teaching AI devices to think: • semiotic modelling to build the semiotic structures that might have an impact on AI-device’s actions and behavior; • matrix representation of information data to present consistent patterns in AI’s processing, evaluating, and classification of information; • cognitive analysis to research the ways AI is able to understand, evaluate, and react to received information; • behavioristic analysis to structure patterns of AI’s behavior; • mind mapping to represent different processes in AI’s “mind” (Olyanitch, Khachmafova, Ostrovskaya, & Makerova, 2017); • comparative linguistic method to analyze how natural languages affect the AI’s ability to “speak”; • semiolinguistic analysis to determine the scope of signs AI is able to discriminate and use while functioning; and • concept-restructuring analysis to figure out the elementary structures of concepts surrounding AI devices.

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RESULTS The human being as a system possessing consciousness interacts with the outside world through transformation “external world → image → sign → meaning and back,” and the meaning is that common (semantic invariant) that is contained in all images of this phenomenon of reality. The process of model information interaction (with the participation of sign systems) can be expanded into an algorithm: 1. The birth of the message, endowed with meaning (in the mental environment). 2. The correlation of the future message with the context (knowledge), the choice of an adequate deductive (sign) system, the formulation of the message in the appropriate language (mentally). 3. Bookmark the message in the signal, generation and modulation of the signal (for example, the muscles of the larynx). 4. Signal transmission through the transmission channel (for example, acoustic). 5. Reception of a signal (selection from interferences, demodulation, and decoding). 6. Prediction of the deductive (sign) system, the establishment of its adequacy (based on the analysis of the current context and existing knowledge). 7. Interpretation of the message, comprehension of its meaning (in the psychic environment). 8. Semiotic modeling. The main property of AI is to perform functions that are traditionally considered the prerogative of Man. The modeling of the sign system for designing, planning, and solving problems had been going on since the 1990s, when applied semiotics appeared. Some components of the sign are well recreated. Thus, the value can be represented in the form of a role structure or plot, which in artificial intelligence has learned to model in the form of a network of frames or semantic networks. The image is well modeled: In an artificial system, the neural network performs the recognition function and reports exactly which object is present in the image. Not everything is so simple with the modeling of personal meaning: It is closely connected with the emotional sphere, but there is not a single mathematical model that can imitate the attitude to the real experience. Generally speaking, the emotions of robots should be skeptical, because the real reaction to emotions is not expressed in the rules “raise the corner of the mouth” or “send a smiley.” To empathize, the

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artificial system itself must have an emotional sphere, but this is unlikely. In existing systems, there are no prospects for modeling emotions. Today, it is not bad at all to simulate the operational component of AI, and this problem is solved by classical symbolic methods. You can think in advance and save in the memory of the machine a sequence of steps that must be taken to implement the action. The action scheme consists of commands that are applied to different parts of a physical device and set in motion a system that is controlled by AI. The operational structure of the action of the scheme is formed in real conditions—the system responds to the situation and selects the most appropriate action program for this, developed in advance. The basic features of intelligence are defined as follows: intelligence means: (a) the ability to understand and learn by experience, the ability to acquire and retain knowledge, mental ability; (b) the ability to quickly and correctly respond to a new situation, the ability to reason when solving problems, choosing type of action, and so on; (c) a measure of the success of the use of the abilities of the individual in solving, performing specific tasks. Intelligence is defined as the ability of thinking, the manifestation of rational knowledge. Intellect in the broad sense means the totality of all the cognitive functions of an individual: from sensations and perceptions to thinking and imagination; in a narrower sense, intelligence is equated with thinking. D. Wexler and D. Gilles (2012) defined intelligence as the generalized global ability of the individual to rational behavior, rational thinking, and effective interaction with the environment. Intelligence is also the ability to optimally update life experience (memory) in order to minimize the time to build a plan and the way to solve a specific task, taking into account probabilistic changes in the external environment. Human intelligence is the highest form of thinking, the ability to abstract thinking, abstraction, self-analysis, to anticipate events, the results of their own actions, to analyze the situation for subsequent decision-making, to formulate, shape goals, based on their knowledge and worldview. The development of the intellect as well as its most important attribute, consciousness, is closely connected with the development of memory, the speed of information received, its systematization. Human knowledge is organized in the form of a conceptual system— systems of categories and relationship between them. This system is structured radially: there are central (basal calling) and off-center categories and truths. Central truths are characterized in terms of directly perceived concepts, relevant preconceptual structure experience. These include categories of basic level of the material area and the scheme repentant from everyday and professional experience. Basic knowledge is acquired during interaction with the environment through perception and manipulation

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of material objects. Other types of knowledge are obtained either directly socially (emotional and social knowledge), or indirectly (knowledge gained by others and transmitted through books, etc.; Kuznetsov, 1995). All people have the ability to conceptualize, that is, to build conceptual systems. Our ability to conceptualize includes: • the ability to form basic symbols, lyrical and figurative schematic structures correlated with preconceptual structures (Sowa, 1984); • ability to metaphorical projection structures of material areas on structures abstract areas, due to the structure correlation between them; and • the ability to form complex categories using structural images and figurative schemes. However, the same conceptual abilities and customizations can generate different con-chained systems. Conceptual systems’ differences in opposed cultures stem mainly from the differences in preconceptual experience— both material and social. There exist individual concepts: the same systems of people of the same culture also may differ quite strongly, however these differences are usually closer to the periphery. Differences in conceptual systems, written in different languages A and B, lead to the difficulties of translating from another language B to language A. Comparing the abilities of artificial and natural intelligence, Migurenko (2010) draws attention to the fact that “a technical device imitates, simulates human intelligence only in some competencies,” since “there are such abilities in humans that cannot be imitated or simulated,” in particular, the specificity of individual mental life, [. . .] the uniqueness of natural language as a sphere of manifestation of individual consciousness; the variability of the structure of human motivations, value systems [. . .] a variety of forms of verbal and non-verbal interpersonal communication; [. . .] competences related to creativity. (p. 89)

Therefore, “information that is possessed by natural intelligence is subjectively loaded,” and “information that is operated by artificial computing systems is personally neutral” (Migurenko 2010, p. 88). CONCLUSIONS AND RECOMMENDATIONS Summarizing the cognitive approach to solving the problems of AI we can distinguish the following ideas of concept that might be used when structuring the AI’s ability to cognate are that it seems necessary

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1. to develop the idea of gestalt as the central place of the concept; 2. to teach the machine discriminate categories of the base level, socalled nonstandard category structures; 3. to develop the AI’s ability to preconceptual structuring and basic figurative and schematic structures; and 4. to solve the problem of constructing AI’s ability to understand based on basic categories and schemes. The presented concept allows taking a fresh look at some of the problems of AI. First of all, two important areas of AI should be distinguished: the organization of knowledge and the formalization of reasoning. For the organization of knowledge and, in particular, the construction of ontologies, ideas related to categorization and various types of categories are of considerable interest. As for the formalization of reasoning, it is important to keep in mind here that a person does not reason according to the laws of formal logic. Such reasoning is not realizable in real time due to the huge number of consecutive elementary steps. Even rigorous mathematical proofs are many orders of magnitude shorter than the arguments formalized in the style of predicate logic. The human ability to reason quickly is based on the use of figurative schemes, the role of which was noted by J. Lakoff (1987) earlier. You can outline the following areas of research in line with the ideas presented: 1. the formalization of typologies of cognitive categories and the organization of knowledge based on them; 2. research and formalization of the problem of gestalt and the connection of gestalts with the concepts of the basic level; the neural network model is focused on solving this problem; 3. formalization of quick reasoning on the basis of figurative and schematic structures; 4. formalization of the concept of cognitive complexity; and 5. implementation of formalized models at the micro level, in particular, at the level of various types of neural network. REFERENCES Gusarova, N. F. (2018). Introduction to the theory of artificial intelligence. St. Petersburg: ITMO University. Kaplan, A., & Haenlein, M. (2018). Siri, siri in my hand, who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15–25.

114    A. V. OLYANICH et al. Kulikov, S. (2015). Artificial intelligence and semiotics, or methods for production of bridges between traditional and digital humanities. Retrieved at https://hal.archives -ouvertes.fr/hal-01179990v1/document Kuznetsov, O. P. (1995). Non-classical paradigms of artificial intelligence. Theory and Control Systems, 5, 3–23. Lakoff, J. (1987). Women, fire, and dangerous things: What categories reveal about the mind. Chicago, IL: University of Chicago Press. Migurenko, R. A. (2010). Human competence and artificial intelligence. Retrieved at http://www.cyberleninka.ru/article/n/chelovecheskie-kompetentsiii-iskusst vennyy-intellekt Olyanitch A., Khachmafova Z., Ostrovskaya T., & Makerova S. (2017). Engineering an elite in social networks through semiolinguistics’ data mapping: A fantasy or reality? In A. Kravets, M. Shcherbakov, M. Kultsova, & P. Groumpos (Eds.), Creativity in intelligent technologies and data science (pp. 671–682). Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-319-65551-2_48 Sowa, J. (1984). Conceptual structures: Information processing in mind and machines. Boston, MA: Addison-Wesley. Wexler, D., & Gilles, D. (2012). Intelligent brush strokes. Siggraph Talks. https://doi .org/10.1145/2343045.2343112

PART II FEATURES OF SUCCESSFUL DEVELOPMENT OF THE INFORMATION ECONOMY UNDER THE CONDITIONS OF TECHNOLOGICAL PROGRESS BASED ON ARTIFICIAL INTELLIGENCE

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CHAPTER 14

DIGITALIZATION OF KEY SECTORS OF RUSSIA Main Problems and How They Could Be Overcome Olga V. Danilova Financial University Irina Yu. Belayeva Financial University Galina V. Kolodnyaya Financial University Alexey Yu. Zhdanov Financial University

ABSTRACT The purpose of the chapter is to discuss the prospects of development of intelligent systems in key sectors of the Russian economy. Reliability, safety, quality, and environmental impact remain key issues in the functioning of the Meta-Scientific Study of Artificial Intelligence, pages 117–125 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

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118    O. V. DANILOVA et al. Russian economy. The development of digital technologies in the infrastructure complex is associated with the need for technological development of energy. The transition of the energy sector to intelligent systems is the only possible way to improve the quality and reliability of electricity supply and solve financial and economic problems of the energy supply sector without increasing tariffs and additional burden on consumers. Intelligent network is a set of connected to the generating sources and electrical consumers of software and hardware and information-analytical and control systems that provide reliable and high-quality transmission of electrical energy from the source to the receiver at the right time and in the right amount. The positive effect of the introduction of digital technologies requires careful calculations of economic and technological efficiency, a comprehensive analysis of costs and benefits.

The rapid transformations taking place in the modern world have been called the fourth industrial revolution, or Industry 4.0. They are associated with the active spread of the Internet, information and computer technologies, cloud technologies, the creation of digital platforms, the formation of stable communication channels, the spread of sensors, the use of artificial intelligence (AI), machine learning, and robotics. A distinctive feature of these changes is the scale of the changes and the rapid speed of their implementation (Schwab, 2016). Artificial intelligence is an integral part of Industry 4.0. Artificial intelligence is understood as technological solutions embedded in technological chains based on machine and deep learning algorithms that create faster and better results compared to humans. Despite the fact that the background of AI has a 60-year period of development (Schwab & Davis, 2018), the active use of achievements in the field of AI around the world has been observed in the last decade. The choice in favor of AI technologies in comparison with human ones is dictated by a number of advantages: • automation of processes, • high speed processing of huge amounts of information measured in terabytes, • using multiple variables in analysis, and • quick selection and making the most effective decision. All this leads to increased use of AI by most manufacturers. METHODOLOGY Significant transformations in recent years have been associated with an increase in the efficiency of interaction between economic entities. Due

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to digitalization, there was a decrease in transaction costs—the cost of interaction between different groups of counterparties. This was due to the exclusion of individual economic agents from production chains, reducing market barriers, building optimal logistics, expanding sales markets, and increasing the transparency of the process of forming market prices. Eventually, we all see the availability of goods for buyers and the activation of competitive processes in the markets (Belayeva, 2014). The use of AI is not limited to marketing and logistics. Artificial intelligence and machine learning technologies have become widely used by businesses to improve the efficiency of internal processes. Artificial intelligence technologies in the modern world can be considered as the most important source of increasing labor productivity and reducing production costs. This is confirmed by numerous studies. According to McKinsey experts, the use of artificial intelligence technologies can increase the real value added in the U.S. manufacturing industry by more than $500 billion per year (McKinsey Global Institute, 2017). The popularity of using digital twins as an integral part of AI on the part of business is caused by the possibility of significantly increasing the efficiency of work. It should be recalled that digital twins are software analogs of physical devices that allow you to simulate the internal processes, technical characteristics, and behavior of a real object in the conditions of disturbances and environmental impact. The use of digital twins marked a new stage in the development of predictive analysis, or predictive analytics. Manufacturers using digital twins get a number of advantages: accident risks are lowered, the service life of objects is extended, and the costs associated with equipment maintenance are reduced. All this allows achieving significant savings in production. Annual maintenance costs for companies using predictive diagnostics of industrial equipment are reduced by 10%, downtime is shortened by 20%, and monitoring costs are decreased by 25% (McKinsey Global Institute, 2017). Robots are increasingly being used in the production process. Investment in the robotics industry is projected to exceed $135 billion in 2019, which is twice as much as similar investments in 2015 (Vanian, 2016). The use of robots to perform individual operations that replace human labor is practiced in many industries. The transition from analog to digital level provides a great potential for economic development. Russia is among the world leaders in terms of the volume of new technologies being introduced in the banking and financial sector. One of the reasons for this is the relative youth of the industry, which makes it possible to easily implement hightech solutions, using the accumulated world scientific and technical experience to achieve the desired economic results in a short time.

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RESULTS The most active in the use of AI technologies with regard to robotics can be considered the automotive industry. Compared to other industries, the automotive industry is a leader in the number of robots purchased and used in the production (Figure 14.1). In the modern world, the automotive industry is the most advanced not only in terms of the use of robotics per employee, but also in the field of creating unmanned vehicles controlled by AI. The use of AI systems to pilot drones brings a noticeable economic effect to business (in the list of expenses for the operation of traditional motor vehicles controlled by a driver, the costs are distributed as follows: 33.3%, cost of drivers’ wages; 33.3%, cost of fuel; 33.3%, operating costs). The introduction of AI technologies allows to reduce the cost of drivers’ wages and achieve significant savings on fuel costs. In the future, with the increase in production of UAVs and the decrease in their production cost due to economies of scale, the use of AI technologies in the control of unmanned transport will grow dramatically around the world. At present, when there is a large potential for reaching the analog and digital level of economic development, the construction of an intelligent accounting system faces a number of serious difficulties (Table 14.1). The transition to digital energy involves deep interaction of sales organizations with consumers and with new market actors: microgeneration, Number of multipurpose industrial robots (all types) per 10,000 employees in the automotive and in all other industries 2014 1,600 Automotive All other

1,400 1,200

Units

1,000 800 600 400 200 0

Japan

Germany

United States

Republic of Korea

France

Italy

Slovokia

Spain

Figure 14.1  Robots purchased and used in the production by industry sectors. Source: Pittman, 2016.

Limited Absent

The security and protection of data

Absent, more than 300 modifications

The compatibility of the metering devices

The access of market participants to accounting data

More than 70 million owners of metering devices

absent

9%

153%

Current Status

Corresponds to the information society development strategy (decree of the president of the Russian Federation of 09/05/2017, No. 203)

Non-discriminated access

100% compatibility

Network organization

Network organization

100%

4%

The Target

The Russian Federation

A single accounting operator

Availability of a single database, standards, and data collection center

Availability of intelligent metering devices

Level of power losses in distribution networks

Indicators

TABLE 14.1  Current Status and Targets

100%

4%

The Target

Meet European standards

Non-discriminated access

compatibility

100%

Network organization

Network organization

50%

6%

Current Status

Experience of Developed Countries

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prosumers, aggregators, storage devices, and so on, including taking into account the possibilities of providing load dispatch services: price-dependent consumption in retail electricity markets (Danilova, 2014). Digitalization of interaction with consumers can create the necessary basis for the transition to the formation of price-elastic demand for electricity, that is, create the necessary competitive pressure on prices, impetus to the development of related markets based on information about the nature of energy consumption, the composition of equipment. On average, the share of the network component in the price of electricity for consumers reaches 50%, which is significantly higher than the world practice. In the regions, the situation is even worse: the cost of electricity for consumers due to the network component in the wholesale market prices increases from 1.5 to 3 times. Considering that the cost of gas, which is used by more than 60% of thermal power plants in Russia, is lower than the world level, this price structure indicates an extremely low efficiency of the entire electric industry. These negative changes led to an increase in the costs of all economic agents to pay for electricity. According to Russian experts, the amount of financial resources diverted from the financial turnover of real sector enterprises as a result of the increase in electricity tariffs amounted to at least 550 billion rubles per year, including by almost 300 billion rubles only by cross-subsidization of the population and equivalent consumer groups (Kutovoy, 2015). Currently, the total maximum capacity of consumers with a maximum capacity of at least 670 kW, connected to the electric networks of distribution subsidiaries of PJSC “Rosseti,” is 87 GW, and is used by consumers at about 44%. This inefficient capacity utilization comes against the backdrop of a chronic lack of investment in the power grid complex, and significant physical and technological wear and tear of power grids. The average technical level of installed equipment in electric distribution networks corresponds to equipment which was used in developed countries 25–30 years ago in a number of parameters. In fact, 50% of electric distribution networks have used up their service life, and 75% have used up two service lives. The total depreciation of distribution electric networks reached 70%, and main electric networks, about 50%, which is significantly higher than in other countries with a similar territory, where the depreciation rate is 27–44%. In the investment program of JSC “FGC UES,” for the period 2016–2020, 25% of the funds will be aimed at technological connection of consumers, 29% on development of electric networks, and 46% at modernizing capital assets. Investment programs for grid facilities of subsidiaries of PJSC “FGC UES” provide for the modernization (renovation) of power grid facilities for 2017–2026 in the amount of 495.7 billion rubles, and for 2021–2026, 329.5 billion rubles. The main source of funding for these programs should be their own funds (depreciation and profit), 64%; raised

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funds, 15%; payment for technological connection, 9%; budget funding, additional share issue funds, 2%; and other sources, 10%. The construction and maintenance of excess capacity requires appropriate operating and investment costs, for which network organizations allocate funds intended for the modernization and renovation of electric networks. The introduction of innovative technologies in the energy sector is necessary to create new markets in which consumers will be given the opportunity to quickly adjust their needs. The energy complex, in turn, must ensure the reliability and availability of energy supply, reduce network losses, and adapt to any energy sources and new market participants. The solution of these tasks is possible on the basis of creating a reliable and full-fledged system of accounting for consumed energy resources, which allows objectively determining the amount of mutual obligations to pay for delivered energy resources, making a reliable balance of their production and consumption, and ensuring transparency of the activities of natural monopolies. The positive effect caused by the penetration of digital technologies into all sectors of the economy is not in doubt. Smart accounting, by itself, is not a self-sufficient technology. Outside of the operation of the “smart power system,” such accounting does not provide additional advantages in comparison with the usual and remote transmission of indicators of metering devices and implementation of individual elements of management. The introduction of intelligent power system management technologies (smart power system) is ultimately designed to solve the following problems: • Integration of centralized and distributed generation, including generating facilities of consumers when a certain share in the energy balance is reached. The strategy of large-scale development of renewable energy generation is typical for energy-deficient countries and regions. The most active “digitalization” of the power grid infrastructure is taking place in Europe, where the share of RSE in electricity production is about 15%, and according to the European Commission estimates it should reach 27% by 2030 (Digital Technologies in the Network Complex, 2019). • smoothing peak power demand in the power system (reducing power consumption peaks and raising/leveling dips) to reduce the overall demand for generating capacity and the corresponding to its network infrastructure, as well as network losses per peak load. CONCLUSIONS The organization of intelligent accounting is a high-tech, but also extremely expensive project. The introduction of an intelligent energy system across

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the country requires careful calculations of economic and technological efficiency, and a comprehensive cost–benefit analysis. National support programs for the development of smart networks are being implemented in EU countries. In Germany, the United Kingdom, Denmark, France, Austria, Sweden, Slovenia, and Ireland, roadmaps and strategies for implementing smart networks were adopted between 2011 and 2014, implying government support, including financial support. Implementation of smart grid projects requires expensive equipment (microprocessor-based measuring devices), creation and maintenance of information transfer technologies, processing centers for new information arrays (data centers), a software package, organization of access for electricity market participants to the created IT system, installation and maintenance of load limiting devices for all consumers as part of their intelligent accounting system. There are three other important problems: • The first problem is the need to integrate “intelligent” and conventional accounting systems. • The second problem is the rapid obsolescence of IT technologies, including data transfer and compatibility of devices of different generations. • The third problem is changing the basic functionality of conventional accounting systems because in addition to measuring the amount of electrical energy, definitions and data storage on such indicator as capacity (hourly measurements of the value of electric energy) operated metering devices shall be provided to measure other parameters (power quality) and provide for information exchange (two-way communication). Analyzing the risks generated by the use of AI technologies, it is necessary first of all to name the increase in the level of structural (technological) unemployment. According to some estimates, about half of the world’s jobs can be automated by 2025–2035 (Brynjolfsson & McAfee, 2014). The introduction of AI can lead to a significant reduction in traditional jobs—with a large number of employed low-skilled personnel, which creates social risks. In our opinion, the state should play a decisive role in solving this problem by implementing a policy of retraining and retraining of personnel. Conflict resolution issues remain unresolved. There are no established frameworks or recommendations for resolving conflicts arising from the use of AI technologies.

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REFERENCES Belayeva, I. (2014). “Socially responsible activities of the state and business” in multidisciplinary scientific conferences on social sciences and arts (SGEM 2014). In Proceedings of the International Conference in Albena, Bulgary, 357–363. Brynjolfsson, E., & McAfee A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. New York, NY: Norton. Danilova, O. (2014). “Substainable development of territories of presence big business” in Multidisciplinary Scientific Conferences on Social Sciences and Arts (SGEM 2014). In Proceedings of the International Conference in Albena, Bulgary, 373–380. Digital Technologies in the Network Complex. (2017). Energy bulletin of the analytical center under the government of the Russian Federation. Retrieved from: ac.gov.ru/ files/publication/a/14737.pdf Kutovoy, G. P. (2015). Formation of forms and methods of state regulation of electric power industry during reforms of economic relations and privatization. Analytical review, Annex to the magazine “Energetik,” 12, 13–22. McKinsey Global Institute. (2017). Making it in America: Revitalizing U.S. manufacturing. McKinsey, November, 8, 10. Pittman, K. (2016, March 24). The automotive sector buys half of all industrial robots. Retrieved from https://www.engineering.com/BIM/ArticleID/11761/ The-Automotive-Sector-Buys-Half-of-All-Industrial-Robots.aspx Schwab, K. (2016). The fourth industrial revolution. Cologny, Switzerland: World Economic Forum. Schwab, K., & Davis N. (2018). Shaping the fourth industrial revolution. Cologny, Switzerland: World Economic Forum. Vanian, J. (2016, February 24). The multi-billion dollar robotics market is about to boom. Fortune, 24. Retrieved from https://fortune.com/2016/02/24/ robotics-market-multi-billion-boom/

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CHAPTER 15

THE USE OF PROBABILISTIC MODEL OF REMOTE CYPTOGRAPHIC TRANSFORMATION IN MULTILATERAL TREATIES ONLINE Gennady A. Vorobyev Pyatigorsk State University Vladimir A. Kozlov Pyatigorsk State University Victoria A. Ryndyuk Pyatigorsk State University Irina I. Pavlenko Pyatigorsk State University Igor V. Kaliberda Pyatigorsk State University

Meta-Scientific Study of Artificial Intelligence, pages 127–135 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

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ABSTRACT The chapter is devoted to the issues related to the possibilities and certain nuances arising from the multilateral agreements concluded online in electronic format in the conditions of the use of insecure channels in the process of their negotiation, including the use of artificial intelligence technologies. In the process of formation and negotiation of the electronic contract reliable information security of the contents of the electronic document (ED) is provided when it is transmitted through open communication channels by the use of probabilistic model of cryptographic transformations hybrid for its encryption (Kozlov, Chernyshev, Ryndyuk, & Bondarenko, 2015; Vorobyev, Kozlov, & Rynkyuk, 2019; Vorobyev, Ryndyuk, Kozlov, & Makarov, 2016). The authenticity of the ED (the final version of the contract) is ensured by electronic digital signatures of all parties to the contract. It is recommended to use a probabilistic model of electronic digital signature (EDS) for its creation (Kozlov et al., 2015; Kozlov & Ryndyuk, 2015). In order to validate the ED, it is confirmed by a special certificate of the certificate authority (CA), which guarantees confirmation of the authenticity of the public and private keys used at the last stage of the formation of the EDS by certified algorithms.

The legal basis for the use of electronic digital signature (EDS). Back in 2002, Russia adopted a special legislative act on the rules of application of EDS. It is Federal Law No. 63-F3 on EDS, which served as a legislative legal basis of the documentation procedures with the EDS throughout the entire territory of the Russian Federation. In accordance with this Federal Law, it is allowed to use three types of EDS: simple, enhanced, and qualified EDS. A simple EDS ensures that the electronic document (ED) has passed the authentication process, therefore, it is known for certain that it is sent by the particular person or organization. Simple digital signature is used for signing EDs or communications sent to an official or a governmental agency. The enhanced EDS not only identifies the sender, but also guarantees the immutability and integrity of the document from the moment of its signing with an electronic signature. A document or communication signed with an electronic signature, in accordance with Federal Law No. 63-F3, is equivalent to a paper document certified by a seal and a handwritten signature. A qualified electronic signature, unlike an enhanced one, is additionally confirmed by a special certificate of the certificate authority (CA). Its mandatory application is required for the creation of legally significant EDs. The main task of the CA is to create and confirm the authenticity of public and private keys used to create EDS certified algorithms for the formation of EDS. From the legal point of view, the presence of a digital electronic signature on an ED is a necessary condition that guarantees the legal force of the

Probabilistic Model of Remote Cyptographic Transformation    129

ED, since it can be successfully used as a strong argument, for example, in various legal proceedings. METHODOLOGY The procedure for the conclusion of multilateral contracts with full legal force, which is carried out online through the Internet, has a number of specific features. First, partners (contracting parties) are usually at a considerable distance from each other, and their communication is realized through the use of modern channels of communication, such as the Internet. As a result, there is a problem with mutual authentication of partners communicating with each other through insecure communication channels. The solution to this problem is described in the works of Kozlov, Avdonina, Vorobyev, and Ryndyuk (2017) and Ivanko, Tumanov, Kozlov, and Ryndyuk (2017). Secondly, the information included in the text of the contract may have different degrees of confidentiality or even secrecy. And this requires the use of special measures to protect it, because it can be transmitted, including through insecure communication channels. The most effective way to protect confidential information transmitted over insecure communication channels is a probabilistic hybrid model of cryptographic transformations of the source text (Kozlov, Chernyshev, Ryndyuk et al., 2015; Vorobyev et al., 2016; Vorobyev et al., 2019). RESULTS Probabilistic model of crypto transformations. The hybrid probabilistic model involves the use of a one-time session key of symmetric cryptographic transformations to encrypt the agreed source text of a particular fragment of the contract. In addition, the hybrid probabilistic model has the following remarkable property: with multiple encryption of the same source text each time we get completely different cipher texts. And this is very important, because the same fragments of the contract with minimal changes are repeatedly sent through open communication channels in the process of negotiating the contract. Figure 15.1 presents a generalized block diagram of the segment encryption of the source text. In the Figure 15.2, gamming cipher of IV is the gamming cipher of the initialization vector. The hybrid probabilistic model of cryptosystem transformations includes both symmetric and asymmetric cryptographic models (Kozlov, Chernyshev, Kaliberda et al., 2015; Kozlov et al., 2017; Vorobyev et al., 2016; Vorobyev et al., 2019).

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Figure 15.1  A simplified block diagram of a segment encryption of hybrid probabilistic models of cryptographic transformations.

Symmetric cryptographic transformations provide encryption of the ED. A gamma cipher of the initialization vector is used as a one-time encryption key, obtained by using a random gamma generator. The result is an ED cipher obtained with the help of a symmetric cryptosystem model. The asymmetric cryptographic transformations which are a part of the hybrid model system are used to encrypt gamma-cipher initialization vectors. The recipient’s public key is used as the encryption key. The recipient, having received a bundle of two ciphers (gamma-cipher IV and the cipher of the source text of the document) through an open communication channel, first decrypts the gamma-cipher IV with his secret asymmetric key, and then uses it to decrypt the ED. Hash function of ED compression: The compression algorithm itself is a kind of hash function that has the property of irreversibility. Irreversibility of the

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Figure 15.2  General scheme of the SHA-512 algorithm.

hash function means that it is not possible to restore the original version of the source document after its compression (called a hash). The input of the hash function is the text of the source document of variable length, often called a prototype. First of all, the text of the source document is divided into blocks of fixed length (512 or 1024 bits). The last block, if necessary, is brought to the standard size by filling it with a pad character. The first block is processed using the initialization vector (In.Vect) which is similar to the key of symmetric cryptographic transformations. The size of the initialization vector (In.Vect) strictly coincides with the size of the standard block. All successive blocks use the result of the transformation of the previous block as the initialization vector. Figure 15.2 presents a simplified scheme of the standard SHA-512 cryptographic hash algorithm. In Vect is the initialization vector of the hash transformation key of the first block of the source document. The hash of the last block of the source document is used in the algorithm of asymmetric cryptosystem transformations as the source text for encryption. The hash function should have a very useful property of the so-called avalanche effect which can be seen in the slightest changes in any of the blocks of the source ED. Electronic digital signature algorithm: The procedure for computing the digital signature is the encryption of the last block hash using an algorithm of asymmetric cryptographic transformations, such as the RSA algorithm. The cryptographic stability of the RSA algorithm is based on the computational complexity of factoring sufficiently large numbers, as well as the practical impossibility to calculate the secret key, knowing the public one, and vice

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versa. The cryptographic stability of the RSA algorithm depends entirely on the length of the numbers decomposed into factors. A sufficient level of cryptographic stability is provided when they are more than 1024 bits. Figure 15.3 shows a diagram of the algorithm for obtaining an EDS of an ED using the private RSA key (the left segment of the diagram), and verifying the electronic signature by applying the public key pair of the RSA algorithm is the right segment. The procedure for determining the EDS is carried out by encrypting the hash value of the last block of the ED with an asymmetric algorithm of cryptographic transformations and the private key of the sender. Description of the main algorithms of EDS is given in the works of Kozlov, Ryndyuk et al. (2017). Within the framework of the presented diagram Figure 15.4 at the very first stage, the ED is compressed by means of the hash function. The result is a digest of the last (n th) block of the document. The resulting digest, which has a standard size (512 bits), is processed by the EDS algorithm using the public key of the first party of the contract. Thus, we get EDS1, which at the next stage is also processed by the EDS algorithm, but with the use of the public key of the second party to the contract, and so on. At the last stage, there is a similar processing of EDSn-1 with the public key of the n th party to the contract. The n th party to the contract is the CA, the authenticity of the keys of which is confirmed, and the EDS has

Figure 15.3  The diagram of the installation and verification procedures of EDS.

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Figure 15.4  Diagram of the system of conclusion of multilateral contracts in the online mode.

the status of a qualified EDS, which guarantees the legal validity of the electronic contract. CONCLUSIONS The procedure for the conclusion of multilateral contracts with full legal force, which is carried out online through the Internet, has a number of specific features. First, partners (contracting parties) are usually at a considerable distance from each other, and their communication is realized through the use of modern communication channels, including such as the Internet. As a result, there is a problem with mutual authentication of partners communicating with each other through insecure communication channels. The solution to this problem is described in the works of Kozlov, Avdonina et al. (2017).

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Secondly, the information included in the text of the contract may have different degrees of confidentiality or even secrecy. And this requires the use of special measures to protect it, as it can be transmitted, including through insecure communication channels. Thirdly, the procedure for agreement of all items of the contract, designed in the form of an ED, should include at least two stages. At the first stage, the integrity and immutability of the ED received through an insecure communication channel is checked; at the second stage, an acceptable version of the text of the contract is developed and sent to the partner for approval. The use of the algorithm of the installation and verification procedures of EDS of the document, shown in Figure 15.4, ensures 100% detection of violations of the integrity and immutability of EDs received via the communication channels. Fourth, the main condition that guarantees the legal force of the electronic contract in various legal proceedings is the presence of a qualified EDS, confirmed by a special certificate of the CA. At the same time, if they so desired, the participants of the contract could choose the so-called enhanced EDS, which do not require confirmation by a special certificate of the CA. REFERENCES Ivanko, V. V., Tumanov, V. S., Kozlov, V. A., & Ryndyuk, V. A. (2017). Algorithm of mutual authentication of partners on the basis of a probabilistic model of cryptographic transformations. In New science as a result of innovative development of society (pp. 129–135). International Scientific-Practical Conference: In 17 Parts. Kozlov, V. A., Avdonina A. I., Vorobyev, G. A., & Ryndyuk. V. A. (2017). Two-Factor authentication scheme and information protection in automated banking system. In University of Reading–2017: Materials of the scientific-methodical readings, 147–152. Kozlov, V. A., Chernyshev, A. B., Kaliberda, I. V., & Orshansky, A. Yu. (2015). Probabilistic model of the system of asymmetric cryptographic transformations. Scientific Review, 7, 261–266. Kozlov, V. A., Chernyshev, A. B., Ryndyuk, V. A., & Bondarenko, K. O. (2015). Probabilistic model of electronic digital signature. Scientific Review, 11, 141–146. Kozlov, V. A., & Ryndyuk, V. A. (2015). The System of management of Depositary Bank cells based on the probabilistic model of electronic digital signature. In Proceedings of International, Scientific Practice, Transport Conference, Rostov-on-don, Russia. Technical Science, 66–69. Kozlov, V. A., Ryndyuk, V. A., Vorobyev, G. A., & Chernyshev A. B. (2017). Models and methods of protection against attacks “man in the middle” (MITM). Modern Fundamental and Applied Research, 1(24), 27–35.

Probabilistic Model of Remote Cyptographic Transformation    135 Vorobyev, G. A., Kozlov, V. A., & Ryndyuk, V. A. (2019). Peculiarities of cryptographic model of the system of wireless remote control. Advances in Intelligent Systems and Computing, 726, 684–692. Vorobyev, G. A., Ryndyuk, V. A., Kozlov, V. A., & Makarov, A. M. (2016). “Probabilistic models of cryptographic systems and their applications.” IEEE Electronic Publication Agreement Receipt. The Third International Conference on Digital Information Processing, Data Mining, and Wireless Communications, July 06–08, 2016, Moscow, Russia, 160–163.

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CHAPTER 16

MODELING OF KNOWLEDGE BASED BY MEANS OF PRE-FRACTAL GRAPHS Ilyas Z. Batchaev Pyatigorsk State University Aleksandra V. Ryzhuk Pyatigorsk State University Irina V. Sklyarova Pyatigorsk State University Olga V. Timchenko Pyatigorsk State University Irina I. Pavlenko Pyatigorsk State University

Meta-Scientific Study of Artificial Intelligence, pages 137–144 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

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ABSTRACT The effectiveness of the artificial intelligence system is largely determined by the quality of the knowledge base that underlies it. Improving the quality of the knowledge base and methods of interaction with it is possible by various mathematical methods in the design of knowledge systems. This study focuses on the modeling method for frame-based knowledge bases through pre-fractal graphs. The use of pre-fractal graphs in some cases allows to model hierarchically arranged objects more effectively and, consequently, to solve practical problems within the framework of the obtained models. The main purpose of the study is to develop a theoretical frame model of the knowledge base through the mathematical apparatus of pre-fractal and fractal graphs, to identify the limits and advantages of the method. We want to admit that modeling of knowledge bases by means of graph theory is not a novelty. However, usage of pre-fractal and fractal graphs makes it possible to solve a number of problems more effectively, which is also proved in the work. The study shows that the obtained models are useful for finding information, building logical circuits consisting of elements of the knowledge system. The application of algorithms, developed in the framework of the theory of fractal and pre-fractal graphs, allows to formulate and to solve a number of practical-oriented tasks, thus extending the functionality and efficiency of artificial intelligence systems.

Modern society places new demands on artificial intelligence systems, which involves increasing the speed of information processing and improving the quality of the result. In this regard, there is an objective need to develop new methods of mathematical modeling of knowledge systems, and, consequently, new methods of information processing in the framework of the obtained models (Zhdanov, 2009). One of the most common models underlying knowledge systems is the frame model, which is based on the idea of Marvin Minskiy, professor of the Massachusetts Institute of Technology. Frame model is a systematic psychological model of human memory and consciousness (Minskiy, 1979). It’s a common knowledge, a frame is a data structure that characterizes arbitrary conceptual objects and represents a set of slots. Each slot can be either a terminal (hierarchy sheet) or a lower-level frame. Frames form a hierarchical sequence, which generates a single multi-level structure that describes the object (presume the slots characterize the properties of the object), situation or process (slots define the names of procedures, inherent in the frame and run during its operation; Borisov & Dyakonov, 2007). Each frame is defined by an arbitrary number of slots, some of which are automatically determined by the artificial intelligence system to perform special functions, the rest are set by the engineer (Batchaev, 2009). The main elements of a frame are as follows: the name of the frame; the pointer of inheritance (only for hierarchical models, showing what information about the attributes of the slots in the frame of top-level is inherited by the

Modeling of Knowledge Based by Means of Pre-Fractal Graphs     139

slots with the same names in the frame of lower level); a pointer attributes (a pointer to the data type of the slot); slot value (the value corresponding to the slot data type and satisfying the inheritance conditions); daemon (a procedure that is automatically started when some condition is performed). Formally, a frame is a data type of the form: F = N , S1 , S 2 , S 3  , where N is the frame name; S 1 is a set of slots containing facts that determine the declarative semantics of the frame; S 2 is a set of slots that provide connections with other frames (causal, semantic, etc.); S 3 is a set of slots that provide transformations that determine the procedural semantics of the frame. The following types of frames can be distinguished: frame-example (slots take specific values and, accordingly, the current state of the subject area is described); frame-sample (slots are represented as variables, that is, the frame is a template that characterizes the class of objects or possible facts of the subject area); frame-class (top-level frame to represent a set of framesamples; Zhdanov, 2009). The composition of frames and slots for an arbitrary specific knowledge model may differ from other models, but within a single knowledge system, a single view is preferable to eliminate unnecessary complexity. We emphasize that the frame model allows the representation of arbitrary properties of declarative and procedural knowledge, while the depth of the hierarchy of slots in the frame is determined by the subject area and the language that implements the model. METHODOLOGY In the framework of this mathematical model, assume that all frames have the same structure and consist of the same number of slots. Let’s form a primer of pre-fractal graph (Batchaev, 2007). To do this, each frame element is mapped to the primer vertex. The logical relationships between the frame elements within the primer are represented by edges. For certainty, you can connect the vertex corresponding to the frame name with the edges to all other vertices. In addition, the vertices assigned to the slots can be logically connected in a chain. In some cases, it is possible to connect all vertices with edges and obtain a complete primer or graph (Emelichev, Melnikov, Sarvanov, & Tyshkevich, 1990). Further, in accordance with the hierarchy of the frame knowledge base, there is a generating process of a pre-fractal graph (or a set of pre-fractal graphs). At the first stage, the frames-classes are modeled, then the framessamples, from which the frames-examples occur. We emphasize that if at the next iteration of the construction of a pre-fractal graph, a vertex corresponding to some variable with a finite set of specific values is obtained, then the next step will determine for this vertex a primer consisting

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exclusively of these values, and this primer will be a complete graph. In subsequent iterations, vertices corresponding to specific values are fixed. For certainty, the old edges will intersect, although without breaking the generality, we can assume that they will connect logically related objects. A number of such iterations will result in a pre-fractal graph generated by one or more primers, depending on the rules of graph formation. Thus, there is the possibility of modeling of frame-based knowledge systems by means of mathematical apparatus of fractal and pre-fractal graphs (Batchaev, 2004). RESULTS An important type of practice-oriented problems are multi-objective optimization problems on pre-fractal graphs (Batchaev, 2007; Batchaev & Kochkarov, 2004). We show that in the framework of pre-fractal graphs many problems are solved more efficiently than in the classical graph theory (Batchaev & Kochkarov, 2003). Thus, it proves that the study of artificial intelligence systems by means of pre-fractal graphs is relevant and significant. Let’s consider a given arbitrary pre-fractal, weighted (n, L)-graph G L = (V L , E L ), H = (W , Q ) is a generator. A valid solution is a certain spanning subgraph x = (V L , E Lx ) whose all connected components are stars. For an arbitrary x = (V L , E Lx ) there is set of criteria: F1(x ) = x is the number of elements (stars) of coverage;

{

F2(x ) = s K 1,l s ∈ x ,l = 1,2,, L

}

is the number of ranking types of stars cover; F3(x ) =

∑ ∑

p(e )

K 1,s ∈x e ∈ K 1,s

is coating weight (Batchaev, 2007; Batchaev & Kochkarov, 2003). On the set of feasible solutions X the vector-objective function F (x )= ( F1(x ), F2(x ), F3(x )) is determined with minimizing criteria Fi (x ) → min,(i = 1,2,3) . The search for a solution implies the selection of some set of alternatives, which is often Pareto set X (Mikhalevich & Volkovich, 1981). The most common method of forming Pareto set is the algorithm of linear convolution, which involves the construction of the objective function

Fλ(x ) = ∑ λi Fi (x ), (16.1) i

Modeling of Knowledge Based by Means of Pre-Fractal Graphs     141

where λi ≥ 0, ∑ λi = 1 i

(Batchev, 2004). We note that, if x ∈ X minimizes function (16.1), that x ∈ X is Paretooptimal solution of the problem. We select Pareto-optimal solutions of the problem under study, which cannot be obtained by any linear convolution of the criteria. Let us first consider a simplified sum with a vector-target function F (x ) = ( F1(x ), F3(x )) . We set a 5-vertex connected primer H = (W , Q ). Let’s form a pre-fractal (5, 2)-graph G 2 = (V 2 , E 2 ), with primer H = (W , Q ). G 2 = (V 2 , E 2 ) consisting of H i = (V i , E i ),(i = 1, 5) (new primer) where V i = {i1, i 2, i 3, i 4, i 5}, E i = {e i21 , e i22 , e i23 , e i24 } . e i21 = (i1, i 2), e i22 = (i 2, i 3), e i23 = (i 3, i 4) , e i24(i 3, i 5), p(e i21 ) = p(e i24 ) = 1, p(e i22 ) = 0.5, p(e i23 ) = 0 and the old edges E1 = {e 11 , e 21 , e 31 , e 41 } , where e 11 = (13,23), e 21 = (21,31), e 31 = (32,41), e 41 = (32, 51), p(e 11 ) = p(e 21 ) = 3 , p(e 31 ) = p(e 41 ) = 1. Pareto set of alternatives X o to this sum contains three elements x i = (V 2 , E 2xi ) , (i = 1,2,3), where 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 E 2x 1 = {e 11 , e 13 , e 14 , e 21 , e 23 , e 24 , e 31 , e 33 , e 34 , e 41 , e 43 , e 44 , e 51 , e 53 , e 54 } 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 E 2x 2 = {e 11 , e 13 , e 14 , e 21 , e 22 , e 23 , e 24 , e 32 , e 33 , e 34 , e 41 , e 43 , e 44 , e 51 , e 53 , e 54 } 2 2 2 2 2 2 2 2 2 2 2 2 2 2 E 2x 3 = {e 11 , e 13 , e 14 , e 21 , e 22 , e 23 , e 24 , e 31 , e 41 , e 33 , e 34 , e 42 , e 43 , e 44 , e 52 , e 53 , e 54 }.

We consider a linear convolution of criteria F1(x ), F3(x ): Fλ(x ) = λ1F1(x )+ λ 3 F3(x ), where λ = (λ1 , λ 3 ), λ1 + λ 3 = 1, λ1 , λ 3 ∈ [0,1]. Fλ(x1 ) = 10λ1 + 10λ 3 = 10; Fλ(x 2 ) = 9λ1 + 12λ 3 = 3λ 3 + 9 ; Fλ(x 3 ) = 8λ1 + 12, 5λ 3 = 4, 5λ 3 + 8 . Each convolution presented above is represented as a graph of the argument λ 3 of the function Fλ . F (x1 , λ 3 ) = 10, F (x 2 , λ 3 ) = 3λ 3 + 9, F (x 3 , λ 3 ) = 4, 5λ 3 + 8 .

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The minimum value convolution achieves in the graphs F (x1 , λ 3 ) and F (x 3 , λ 3 ) (thick line). The graph F (x 2 , λ 3 ) is much higher than the bold polyline. Graphical interpretation shows that for any λ 3 ∈ [0,1], and, accordingly, λ = (λ1λ 3 ), λ1 + λ 3 = 1, λ1 ∈ [0,1] convolution Fλ(x i ),(i = 1,2,3) is minimal for solutions x1 , x 3 and with no λ for x 2 , that is, the statement is true: 2-criterion coating problem with the above criteria for a pre-fractal (5, 2) graph generated by a given primer H is insolvable by a linear convolution algorithm. The result is generalized to L = 2 pre-fractal graph formed by (5 + 2k)-vertex primer (k = 1,2, . . .). Given a graph G 2′ = (V 2′, E 2′ ) generated by H 1 = (W 1,Q 1). K pairwise disjoint chains l i = {e 2j [3+2i ], e 2j [4+2i ] }, (i = 1,2,, k ) are added to each new primer H j ,( j = 1,5) of the pre-fractal graph G 2 = (V 2 , E 2 ), and the length of each of the chains is two. One edge is incident to vertices j 3, i.e., e 2j [3+2i ] = ( j 3, j[4 + 2i ]), e 2j [4+2i ] = ( j[4 + 2i ], j[5 + 2i ]), p(e 2j [3+2i ]) = p(e 2j [4+2i ]) = 1 . Further, 2k of new primers H s1 = (W s1,Q s1), (s = 6,7,,2k + 5) and 2k of old edges e 31+2i = (33,[4 + 2i ]3), e 41+2i = ([4 + 2i ]3,[5 + 2i ]3), p(e 31+2i ) = p(e 41+2i ) = 3 are added to G 2 = (V 2 , E 2 ). Accordingly, elements of Pareto set X ′o = {x1′ , x 2′ , x 3′ } are obtained from the covering of set X o = {x1, x 2 , x 3 } through attaching to each x j ,( j = 1,2,3)(5 + 2k )k 2 stars K 1,1 = {e 2j [4+2i ] } , (i = 1,2,, k , j = 1,2,,5 + 2k ), 2k stars K 1,1 = {e j 1 }  , 2 2 ( j = 6,7,,2k + 5), and 2k elements K 1,2 = {e j 3 , e j 4 }, ( j = 6,7,,2k + 5) . Thus, we obtain: F (x1′ , λ 3 ) = 10 +(2k 2 + 9k ), F (x 2′ , λ 3 ) = 3λ 3 + 9 +(2k 2 + 9k ), F (x 3′ , λ 3 ) = 4,5λ 3 + 8 +(2k 2 + 9k ). The graph of the new linear convolution of criteria in this case is obtained from the graphs of convolutions, due to the parallel transfer of the latter along the ordinate axis by 2k 2 + 9k units. It means that the assertion: for every odd number n ≥ 5 the considered multicriteria problem is unsolvable by using the algorithm of linear convolution. Given a graph G 2′′ = (V 2′′, E 2′′), with primer H 2 = (W 2, Q 2 ), which has in this case an even number of vertices. The graph G 2′′ = (V 2′′, E 2′′) in this case is derived from G 2′ = (V 2′, E 2′ ) as the result of adding to each new primer an edge e 2j [2k +5] = ( j 3, j[2k + 6]), ( j = 1,2,,2k + 5), p(e 2j [2k +5]) = 0 , joining one new primer H 2 = (V 2, E 2 ) and the old edge e 51+2k = (33,[2k + 6]3). Then the coverings of Pareto set alternatives X ′′o = {x1′′, x 2′′, x 3′′} are obtained from X ′o = {x1′ , x 2′ , x 3′ } by additing stars K 1,1 = {e[22 k +6]1 } , K 1,3 = {e[22 k +6]3 , e[22 k +6]4 , e[22 k +6][2k +5] }, K 1,1 = {e[22 k +6][4+2i ] }, (i = 1,2,, k ) to each element x ′j ,( j = 1,2,3). Next, each new star with the center in j 3,( j = 1,2,,2k + 5) is joined by an edge e 2j [2k +5] = ( j 3, j[2k + 6]), ( j = 1,2,,2k + 5). We F (x1′′, λ 3 ) = 10 +(2k 2 + 10k + 2), F (x 2′′, λ 3 ) = 3λ 3 + 9 +(2k 2 + 10k + 2), have: 2 F (x 3′′, λ 3 ) = 4,5λ 3 + 8 +(2k + 10k + 2).

Modeling of Knowledge Based by Means of Pre-Fractal Graphs     143

Graphs of convolutions of the last criteria are obtained from the graphs of convolutions due to the parallel shift along the O y axis by 2k 2 + 10k + 2  . Based in the above, we can conclude that for any given number n ≥ 5 the considered two-criterion problem is unsolvable using the algorithm of linear convolution criteria. The same fact is obtained for an arbitrary L ≥ 2 . Indeed, the study can be carried out for a pre-fractal (5, L)-graph G L = (V L , E L ),(L > 2), with primer H = (W , Q ), in this case the connected components of the subgraph G = (V L , E L \ E L −2 ) should remain isomorphic G 2 = (V 2 , E 2 ) and the elements of the set E L−2 should be incident to the vertices of the type j 3,( j = 1,2,, 5L −1). We emphasize that the old edges of the ranks l = 1,2,, L − 2 are not included in the covering of the graph G L = (V L , E L ), since they are isolated form j 4, j 5,( j = 1,2,, 5L −2 ). Accordingly, the Pareto set of alternatives Xˆ 0 = {x 1, x 2 ,, x t } , t = 2 ⋅ 5L −2 + 1 consists of the concatenation of coverings x j 1, x j 2 , x j 3 ,( j = 1,2,,5L −2 ) of connected component of the subgraph 0 G = (V L , E L \ E L −2 ), where x ji ,(i = 1,2,3) is isomorphic to x i ∈ X . Herewith, s

s

s

s

s

i =1

i =2

i =2

i =3

i =1

x 1 = ∪ x i 1, x 2 = x12 + ∪ x i 1, x 3 = x13 + ∪ x i 1, x 4 = x13 + x 22 + ∪ xi 1,…, x t = ∪ xi 3 where s = 5L −2 . We form solutions x 1, x 2, x t ∈ Xˆ 0 and prove that x 2 can be achieved by no linear convolution Fλ of the criteria F1, F3 . We define F (x 1, λ 3 ) = s ⋅ 10 , F (x 2 , λ 3 ) = 3λ 3 + 9 +(s − 1)⋅ 10 = s ⋅ 10 +(3λ 3 − 1), F (x t , λ 3 ) = s ⋅(4,5λ 3 + 8). Convolution reaches its minimum on the charts F (x1, λ 3 ) and F (x 3 , λ 3 ) (bold line). Therefore, the graphical representation proves that for any λ = (λ1, λ 3 ), λ1 + λ 3 = 1 , λ1 ∈ [0,1] and arbitrary λ 3 ∈ [0,1], the convolution Fλ(x i ), (i = 1,2,3) is minimized on the solutions x 1 , x t and with no value of λ to x 2 . A similar result is true for (n, L)-graphs, with primers H 1 = (W 1,Q 1) or 2 H = (W 2 ,Q 2 ). Taking into account the proven facts, the theorem is generally valid. Theorem 1. The problem under consideration with the first and third criteria for covering an arbitrary pre-fractal (n, L)-graph (L ≥ 2, n ≥ 5) with stars of rank types is insolvable by linear convolution of the criteria. Theorem 2. The problem of covering an arbitrary pre-fractal (n, L)graph (L ≥ 2, n ≥ 5) with stars of rank types is unsolvable with the help of a linear convolution algorithm.

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CONCLUSIONS The study showed that a number of optimization problems are more effectively solved in the framework of the mathematical apparatus of pre-fractal and fractal graphs. Thus, the study of artificial intelligence systems by means of pre-fractal graphs opens up a wide range of new scientific developments. REFERENCES Batchaev, I. Z. (2004). Mathematical model of the Internet control system. In Proceedings of II International Scientific and Technical Conference “Materials and technologies of the XXI century” in Penza, Penza, Russia, 107–110. Batchaev, I. Z. (2007). Multicriteria problem of covering pre-fractal graphs with stars of rank types. Pyatigorsk, Russia: Pyatigorsk State Linguistic University. Batchaev, I. Z. (2009). Information technologies of management. Pyatigorsk, Russia: Pyatigorsk State Linguistic University. Batchaev, I. Z., & Kochkarov, A. M. (2003). A fast algorithm on pre-fractal graphs with grades. In Mixed Type Equations and the Related Problems of Analysis and Informatics Proceedings of the International Russian-Uzbek Symposium, NalchikElbrus, Nalchik, 108–110. Batchaev, I. Z., & Kochkarov, A. M. (2004). Vector task of covering pre-fractal graphs with stars of rank types. News of the Taganrog State Radio-Technical University, 8(43), 301–302. Borisov, A. V., & Dyakonov, V. P. (2007). Fundamentals of artificial intelligence. Smolensk, Russia: Science. Emelichev, V. A., Melnikov, O. I., Sarvanov, V. I., & Tyshkevich, R. I. (1990). Lectures on graph theory, Moscow, Russia: Science. Mikhalevich, V. S., & Volkovich, V. L. (1981). Computational methods of research and design of complex systems. Moscow, Russia: Science. Minskiy, M. L. (1979). Frames for representation of knowledge, Energia, Moscow, Russia. Zhdanov, A. A. (2009). Autonomous artificial intelligence. BINOM. Laboratory of Knowledge, Moscow, Russia.

CHAPTER 17

INFORMATION TECHNOLOGIES IN THE DEVELOPMENT MANAGEMENT OF THE MUNICIPAL SOCIOECONOMIC SYSTEM Tatiana Yu. Anopchenko Smolensk State University Anton D. Murzin Southern Federal University Don State Technical University Kometa T. Paytaeva Chechen State University Svetlana G. Chumachenko Rostov State Transport University Alla V. Temirkanova Southern Federal University

Meta-Scientific Study of Artificial Intelligence, pages 145–155 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

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ABSTRACT In the formulation of strategic municipal development papers, the field of information and telecommunication technologies has so far received little attention. The modern urban community is interested in increasing transparency, social orientation and economic efficiency of management decisions taken by municipal authorities on behalf of the population. The introduction of information technologies in the process of urban management leads to fast resolution of many problems of municipal management. The purpose of the study is to develop generalized recommendations for the creation of strategic documents for the management of urban development with the introduction of information technology. The study is based on the use of known methods of economic knowledge: economic observation, economic induction, and economic hypotheses. In the process of studying the current state and problems of urban management the main goals and objectives of municipal government were identified, the elements of the control system of management decisions were studied, the aspects of the choice of priorities for the development of the city were detected. As a result, on the basis of the structuring of problems and shortcomings of the existing decision-making system, taking into account the principles of regulation of the city management system, an algorithm for the development of urban development strategy with the introduction of information and telecommunication technologies is proposed and described in phases.

The modern city is a complex multilayered territorial formation consisting of many spatial elements (Voloshinskaya, 2016). The development of urban areas implies a large growth of population, which is predominantly employed in the manufacturing sector and the service sector, which also accelerates the pace of economic exchange (Kupriyanovsky et al., 2017). The main task in the management of the city today is to maintain the living standards and ensure the functioning of the life-support systems of the territorial community on the basis of the formed socioeconomic policy, taking into account the existing features of the division of labor, traditional for a particular area (Lazareva, Anopchenko, & Lozovitskaya, 2016). Among the priority areas of municipal management, the following can be identified (Fuentelsaz, Maicas-López, & Polo, 2002): 1. formation and stabilization of the policy of equalization of living standards between different groups of population and territories; 2. implementation of state policy in the most important areas of social life (health, education, social guarantees); 3. ensuring the stability of the functioning of utilities that meet the needs of the city; 4. management of the consumer market on the principles of rationalization of distribution, allocation of retail space, provision of employment benefits, and so on;

Development Management of the Municipal Socioeconomic System    147

5. creation and improvement of transport communications and infrastructure; and 6. environmental landscaping of the city, managing the density of residential development, the harmonization of the appearance of constructed facilities. In the municipal management, the main problem is the lack of the ability to temporarily stop the life-supporting processes in order to reform the existing mechanism, which does not meet the needs of residents. Taking into consideration this fact, it can be stated that urban management should be characterized by dynamics and flexibility in the choice of methods of influence on the elements of the management system and transformation within the urban settlement, which become the main problem in the life of the urban population (Kvasov, 2017). The key links of the urban development monitoring system can be combined into several groups of theoretical principles of management and development (Head, 2011): 1. harmonization of sectoral management and functional tasks; 2. the combination of legal norms, civil liberties, and the needs of centralized authority; 3. interrelation of economic independence of the territorial entity and its role in the state system of division of labor; 4. integration of economic and social areas of development; and 5. compliance with the principles of local and federal government. Municipal management involves the regulation of relations, the main task of which is to fulfill the planned development goals. The main aspects of the choice of management goals are: 1. the hierarchy of relations, subordination to their internal logic and the possibility of control; and 2. ensuring the full implementation of the goals and objectives. The municipality acts as the subject of management activities in the urban area. It is a representative body of government that combines legislative and supervisory functions within the municipality. The municipal administration, as an element of municipal power, performs the functions of the executive power. The municipality can perform its functions only taking into account the recording of operations that have been carried out in the controlled territory. The management of the social development processes requires the investigation of the state of the subject area in the unity of its component elements. In this case, the possible impact of external factors of

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the system should be completely excluded. Such unity can be achieved only after determining the proportions and establishing a stable relationship between the factors that define the dynamics of the manageable processes. The integrity of management will depend on the coherence of the criteria applied (Muntean & MiclǎUş, 2011). Its achievement requires a stable reproduction of the scheme of social relations management, which will contribute to the preservation of social order, protection from external negative influences, and greater stability. In order to achieve integrity, it is necessary to choose functional social subsystems, which are characterized by the required level of autonomy and self-regulation, allow for the transformation of the employment structure in the city. To achieve integrity in the management of urban processes, there is a need to ensure the smooth operation of each element of the urban infrastructure, to fully meet the needs of residents, and to provide environmental protection. METHODOLOGY The research methodology is based on the application of known methods of economic knowledge: economic observation, economic induction, economic hypotheses. The population in the municipal government is both the subject and the object of management. Therefore, for the purposes of the study, it is necessary to take into account the peculiarity of the structure of municipal government, involving the mandatory participation of residents in making important management decisions for the city (Stok, 2011). The structure of the municipal government system includes: 1. 2. 3. 4. 5.

population of the city; municipal sector; local self-government bodies; regional authorities; and federal government organs.

The formation of the relationship between the structural elements of the management system of the municipality is characterized by the management regime, which can be seen in meeting the existing needs and interests of residents living in the area. Territorial communities consist of elements that influence the management regime: population, territory, interests of residents (Stok, 2011). Among the principles of formation and regulation of the system of state and municipal management are the following, the most important, which

Development Management of the Municipal Socioeconomic System    149

should be taken into account when forming the priorities of strategic management of urban development (Kvasov, 2017): 1. 2. 3. 4.

the principle of complementarity; the principle of subsidiarity; the principle of democracy; and the principle of departmentalization.

The principle of complementarity provides direction for the permanence in the power structure. This is a condition of a uniform division of power functions of the entire management vertical. The main criterion of this process is the separation of powers at every level. The principle of subsidiarity implies the distribution and division of powers between levels of government and administration. This principle contributes to the formation of consistency in the implementation of the powers of the administrative authorities, determines the procedure for the division of responsibility to the residents. According to this principle, delegation of authority to a higher management level is possible only in the absence of the possibility of their implementation at the primary level. This principle is clearly enshrined in legal acts and specifies the powers of local authorities that have not been explicitly delegated or prohibited for the municipal level. RESULTS The municipality within the territorial boundaries implements independent activities for the benefit of the population. Municipal interests include: support for organizations that provide services to the population in the territory of the settlement, taking into account the needs for economic and financial independence. The interests of the municipality include the goals of creation of the network of community infrastructure that provides a sociocultural base for the local population. Social infrastructure comprises educational and health institutions, libraries, and consumer services. From the moment of formation of the municipality the independent solution to the problems associated with the development of socioeconomic and engineering sphere has been the priority. The municipal government is obliged to engage in comprehensive development, because without the creation of engineering infrastructure and life support systems it is impossible to guarantee the full independence and self-sufficiency of the municipality. The problem of determining the municipal interest depends closely on the balance between the interests of society and each individual. Leaving

150    T. Yu. ANOPCHENKO et al.

aside the philosophical debate about possible ways to create such a balance, we can confidently say that the interests of society are qualitatively different from the interests of individuals that constitute it (Dmitriev & Baranov, 2017). The personal interest implies an active human desire to fully and effectively meet the needs. These are such problems as finding a job, buying a home, building a career. Such needs can be attributed to individual interests, because the result has an impact on the quality of life of the particular individual. For example, satisfaction with their working environment and social status in society. Municipal interest is expressed in the desire of the population living in this territory to realize their aspirations through the local governance mechanism, to have the opportunity to influence personally the results of important territorial problems. Municipal interest, which is a kind of public interest, has its own specifics and certain forms: 1. Its structure includes provision of services to the population to ensure the smooth functioning of life-support systems, the components of which are heat-, energy-, water-, gas-supply, and so on. 2. It can serve as a source of support for the financial and economic independence of the municipality and for organizations that provide services to the population. 3. It implies the formation of a network of social and engineering infrastructure, which provides a system of motivation for the population, including educational institutions, health, trade, consumer services, and so on. Historically, municipal interest emerged from the system of needs, satisfaction of which creates the prerequisites for its potential implementation. The basic need for the primary level of realization of the municipal interest is the need for regional self-sufficiency and economic integrity. This group of needs arose from the natural aspirations of any territorial entity to fully realize its own economic independence and self-sufficiency. The next level is the need for social security, which is the protection of the territorial interest, the boundaries of territorial entities, and so on. According to the experience of Moscow and St. Petersburg, as well as major cities of the United States, Great Britain, Germany, France, Japan and other countries, the main direction of development of technologies and management tools in the municipal management system is the introduction of paperless information technologies (Solodilova, Sunaeva, & Sharipova, 2016; Sokolov, Shneps-Shneppe, Kupriyanovsky, Namiot, & Seleznev, 2017). The concept of transition to these technologies of urban management should take into account:

Development Management of the Municipal Socioeconomic System    151

1. the current level of information and telecommunication technologies in the spheres of functioning and zones of responsibility of executive authorities and local self-government, industries, and territories; 2. the requirements to safety and reliability of use of paperless information technologies of management which are developed on the basis of regulations and rules of implementation of administrative functions; and 3. international and domestic experience in the implementation and application of computer information systems for the management of large cities. Paperless information technologies can be used to launch the urban computing network, including local and corporate computer networks of executive authorities, industry and territorial administration. In this regard, the mutual interoperability of the information systems within organizations or companies should be provided taking into account such aspects as: organizational, technological, software, and information. To achieve this goal, launching “Telecommunications and informatization” of the city program is of primary importance. This program should provide 1. Meeting the needs of the economy, management, and population in information and telecommunications. The policy of development of information and telecommunication technologies becomes an obligatory element of the urban strategy of informatization of socioeconomic, scientific, and technical spheres. These measures will be implemented by the bodies of state power and bodies of local self-government in order to satisfy societal needs in the field of informatization and telecommunications, improvement of the technologies of municipal management, and development of knowledge-based industries. 2. Formation of a single urban information space, which will allow free access of residents to information resources and services provided by local bodies of the executive power and the bodies of local government. 3. Integration of the agglomeration into the federal and international information space. 4. Increase in budget revenues, increase in employment due to the production of information means and telecommunication services, and the growth of the quantity of services in the field of information. Currently, urban information resources, which have great value for the state, the commercial sector, and the city residents, are actively developing

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in the absence of legal regulations and technical standards to ensure the procedures for the use of urban information resources, which did not contribute to: 1. the transfer of aggregated and reliable information on informal requests by the decision-makers, as a result of which the image of nonproductive use of informatization tools is formed; 2. the establishment of the legal status and data validation, hence the need for its repetitive checking; 3. the regulation of commercial use of data with redistribution of part of the income on development of urban information systems; and 4. the assurance of data integrity and accuracy on objects of urban infrastructure. The absence of any regulation establishing the right to add or change information leads to a constant duplication of data and functions of various authorities. In such circumstances, the choice and the phased implementation of a holistic information policy are particularly relevant (Figure 17.1), which will allow us • to identify the sources of official reliable information with the definition of their legal status and responsibility for the quality of information; • to initiate the use of effective ways of using information that takes into account the status of the consumer, the degree of information openness, their social significance, and commercial value; • to streamline the commercial use of information by regulating the order of aggregation of data and access modes; and • to form an integrated information system of urban management, contributing to the data processing and exchange on the basis of telecommunication systems and computer hardware and software. At the first stage of implementation of the presented algorithm, the basic options of the strategic plan are formed, taking into account the main goal and specific strategies for its achievement. On conducting analysis of the basic options of the strategy, the best option in terms of socioeconomic efficiency should be chosen. At the second stage of the presented algorithm, the optimization of the basic option is conducted, taking into account the existing restrictions on its financing and the potential risks of its implementation, including economic, social, political, and so on that may have an impact on the realization of the programs of the basic strategic plan.

Development Management of the Municipal Socioeconomic System    153 External factors of urban development

Internal factors of urban development

Phase 1 Assessment of the level of competitive advantages of the city Defining global development priorities, a set of goals, and objectives, strategies, and measures to achieve them Development of the main strategic directions of the development of urban areas The identification of specific strategies, detailing goals, grouping sub-goals and tasks Development of the program of actions for the decision of current development challenges The formation of management strategies of development of urban areas

Phase 2 Assessment of conditions for options of management strategies to manage urban development taking into account possible costs Optimization of the selected strategy option to manage urban development taking into account the risks Forming the operating plan to implement the strategy of urban development

Phase 3 Implementing the phases and activities as part of the management strategy of urban development

Monitoring and evaluation of the implementation of urban development strategy, adjusting the plans

Figure 17.1  The algorithm of strategy of urban development.

In the third stage, monitoring and evaluation of the results of the urban information management system are carried out. The results of monitoring can serve as a basis for the operational decision-making to adjust the plans.

154    T. Yu. ANOPCHENKO et al.

The formation of the basic variants of the strategic development plan is carried out in the following sequence: 1. analysis of the current state of each urban complex: transport and road, industrial, engineering and energy, urban planning, sanitation, and other sectors; 2. search for strengths, advantages, and drivers of city development among competitors, determining the place of agglomeration and its importance in the national and world economy; 3. formation of the list of standard tasks for achievement of the purposes and implementation of phases of the strategic plan of urban development; 4. development of specific strategies, separate goals, and subgoals of the plan; and 5. building a tree of goals for the implementation of the strategic development plan, the definition of the main strategic directions of the city. CONCLUSIONS The tasks of managing urban development are not simple. This mainly concerns motivational and energy resources, as well as material and labor resources that can be saved through the introduction of information management innovations. This leads precisely to the effectiveness of investments in long-term projects of strategic development of the information sphere (Stok, 2011). The conducted analysis of strategic options has clearly shown the most effective options should be aimed at the implementation of information programs and prioritization of the directions of their future development. Developing and grouping of performance criteria for the creation of information and communication systems of urban management can be carried out depending on the priority of the given directions of development. As a result, the objectivity of the initial assessments of the strategic option will depend on the possibility of striking a more appropriate balance between qualitative and quantitative indicators that influence decision-making. REFERENCES Dmitriev, S. M., & Baranov, V. G. (2017). Staff base of the region, Information-measuring and control systems, 15(8), 3–6. Fuentelsaz, L., Maicas-López, J., P., & Polo, Y. (2002). Assessments of the new economy scenario, Qualitative Market Research, 5(4), 301–310.

Development Management of the Municipal Socioeconomic System    155 Head, S. (2011). The new ruthless economy: Work and power in the digital age. Oxford, England: Oxford University Press. Kupriyanovsky, V. P., Alenkov, V. V., Sokolov, I. A. Zazhigalkin, A. V., Klimov, A. A., Stepanenko, A. V., Sinyagov, S. A., & Namiot, D. E. (2017). Smart infrastructure, physical and information assets, Smart Cities, BIM, GIS and IOT. International Journal of Open Information Technologies, 5(10), 55–86. Kvasov, I. A. (2017). Digitalization and integration of technologies and management—Efficiency improvement mechanism. Moscow, Russia: Scientific Technologies. Lazareva, E., Anopchenko, T., & Lozovitskaya, D. (2016). Identification of the city welfare economics strategic management innovative model in the global challenges conditions. Bulgary, 2(4), 3–11. Muntean, V. D., & MiclǎUş, I. M. (2011). Management in digital economy organization. Quality—Access to Success, 12(2), 319–327. Sokolov, I. A., Shneps-Shneppe, M. A., Kupriyanovsky, V. P., Namiot, D. E., & Seleznev, S. P. (2017). Telecommunications as a crucial link in the digital economy. International Journal of Open Information Technologies, 5(6), 76–93. Solodilova, N. Z., Sunaeva, G. G., & Sharipova, I. M. (2016). Internationalization of production in the new economic model. Bulletin of Ufa State Petroleum Technological University, 3(17), 7–12. Stok, V. G. (2011). Information cities: Analysis and creation of cities in intellectual society. International Forum on Information, 36(3), 3–20. Voloshinskaya, A. A. (2016). New trends in “sustainable city management”: What prevents Russia from applying the world’s best practices? Trends and management, 4, 380–389.

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CHAPTER 18

THE USE OF COMPUTER VISION AND ARTIFICIAL NEURAL NETWORKS TO ASSESS THE TECHNOLOGICAL ADVANTAGES OF WHEAT Pavel V. Medvedev Orenburg State University Vitaly A. Fedotov Orenburg State University Irina A. Bochkareva Orenburg State University

ABSTRACT The chapter describes a method for determining the hardness of wheat grain using a fractographic method by obtaining images of grain grinded particles using optical microscopy and processing of these images by computer vision algorithms.

Meta-Scientific Study of Artificial Intelligence, pages 157–163 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

157

158    P. V. MEDVEDEV, V. A. FEDOTOV, and I. A. BOCHKAREVA To get the highest accuracy of grain hardness determination, images of grain grinded particles were analyzed by an artificial neural network. Network learning method implies learning with a teacher. The topology of the network implies Rosenblatt’s perceptron, which does not have backward linkages and relationships “through the layers.” The use of modern information technology allows automating the entire process of analysis and achieving high accuracy results.

Information technology and intelligent systems have affected the vast majority of technology and industry. Modern enterprises of the grain processing industry have achieved a high degree of production automation. At the same time, many methods of determining the quality of processed wheat products (flour, cereals) are very archaic, which haven’t been changed for decades (evaluation of hardness, quantity and quality of gluten, baking quality test, etc.). The industry needs high-precision and quick determination of technological quality of wheat and forecasting of its future consumer properties. The quality of flour formed at the grain processing plant influences the quality of bakery, confectionery, and pasta products (Parker, 2010). In many ways, the technological quality is determined by the grain hardness index (Berkutova, 1991). For example, flour from wheat with low hardness is desirable for flour confectionery products. Wheat with high hardness has good milling properties and is used in the production of bakery products (Chung, Ohm, Lookhart, & Bruns, 2003). One of the ways to determine grain hardness is a particle size analysis of flour or grinded grain, describing geometrical characteristics of the particles: length, width, perimeter, ratio of elongation of the particles (length to width ratio) and other (Kruglyakov & Kruglyakova, 1999). To determine the technological qualities of grain, it is proposed to conduct an extended type of particle size analysis—fractographic analysis of particles (Medvedev & Fedotov, 2013). Fractographic research includes the study of fractures after the destruction of the grain. It is most appropriate to use optical microscopy for the analysis of grinded particles due to their size (Smith, 1995). The leading trend in the development of artificial intelligence (AI) technologies today is the development of artificial neural networks. Processing of visual information about grinded grain using neural networks will improve the quality of evaluation and optimize labor and time costs (Huebner et al., 1995). METHODOLOGY For the study, samples of six varieties of grain wheat were used: Orenburg 10, Bezenchuk 200, Orenburg 21, Bezenchuk amber, Kharkiv 3, Steppe 3, and seven varieties of soft wheat: Saratov 42, Uchitel, Orenburg

The use of Computer Vision and Artificial Neural Networks    159

13, South-Eastern 3, Varyag, Prokhorovka, L-503, from several districts of the Orenburg region, characterized by various climatic and soil growing conditions. Grinding of the grain was carried out in a laboratory mill to obtain onegrade flour grinding 70% of the output. Particle size analysis of grinded grain was carried out on an experimental setup, including a digital camera Sony Super HAD (Medvedev, 2013). The hardness of the grain was determined by the microhardness index, that is, the ability of the grain to resist deformation (indentation). Microhardness of grain was evaluated for the hardness testing PMT-3. By changing the area of the grain on which the indentation is carried out, it is possible to study the microstructure of the grain and determine the microhardness of its various sections. The integrated microhardness value of wheat grains was calculated as the arithmetic mean for 5-fold repeats of measurements of more than 100 wheat grains (Maghirang et al., 2006). RESULTS One of the technical solutions to improve the algorithms of data processing with particle size analysis is as follows. The developed software based on the Open Source Computer Vision Library (OpenCV) was used to analyze the obtained images. OpenCV is a software environment with computer vision algorithms (Callan, 2001). OpenCV is free, not bound to the platforms of the computer (can operate in Microsoft Windows operating systems, Android, Linux, iOS, etc.). De facto, it is a standard software product for the implementation of technical vision (Khaikin, 2006). According to the results of the correlation and regression analysis, the interrelations of the main parameters of grain analysis—physical and chemical properties of grain and the data of fractographic analysis (X, in micron, and K)—were established (Shewry, 2009). On the basis of the experimental data for HD grain hardness the following dependence is derived, in kg/ mm2 (r = 0.954, R 2 = 0.902):

HD = 0.15 ⋅ K + 0.28 ⋅ X + 0.90 . (18.1)

Images of grinded grain particles were analyzed using an artificial neural network. To do this, the particles were divided into 5 types based on their similarity to geometric shapes. The characteristics of the particle shape were determined by the perimeter P, area S, and the smoothness coefficient G, calculated from the ratio of the perimeter and area (Table 18.1).

160    P. V. MEDVEDEV, V. A. FEDOTOV, and I. A. BOCHKAREVA TABLE 18.1  Division of Particles Into Classes Based on Their Geometric Characteristics Geometry Primitive Type of Particle Code S

P

G

Circular

Oval

Rectangular

Triangular

Polygon

0

1

2

3

4

with r = a = b = 1

3.14

3.14

1.00

0.50

not determined

with r = a = 1 b = 10

3.14

31.45

10.00

5.00

not determined

range

3.14

from 3.14 to 31.45

from 1.00 to 10.00

from 0.50 to 5.00

less than 0.50

with r = a = b = 1

6.28

6.28

2.00

3.40

not determined

with r = a = 1 b = 10

6.28

34.54

22.00

21.00

not determined

range

6.28

from 6.28 to 34.54

from 2.00 to 22.00

from 3.40 to 21.00

more than 35.00

with r = a = b = 1

1.00

1.00

1.27

1.85

—

with r = a = 1 b = 10

1.00

1.10

3.85

7.06

—

range

1.00

from 1.00 to 1.10

from 1.27 to 3.85

from 1.85 to 7.06

more than 7.06

Note: Area P (perimeter), S (smoothness coefficient), G (all values in conventional units)

The information about the color of each pixel (0 [white pixel color], 1 [black pixel color]) of the particle image in the form of a 300 × 300 matrix is supplied to the input of the zero layer of the network consisting of 90,000 neurons. Network topology is Rosenblatt’s perceptron (Bradsky & Kaehler, 2008). It is established that one hidden layer of neurons is enough, further increase in the number of hidden layers does not lead to an increase in the accuracy of recognition. At the output of the neural network particles are assigned a certain type based on the affinity with five geometric shapes (coded values from 0 to 5). The filling of the matrix of particle image correspondences to a particular class is interactive: at the beginning of the work, a test sample of particle images is used, on the basis of which the operator indicates that the particles belong to a certain type (Souza et al., 2004). On the basis of their hardness wheat grains are divided into four grades. There are ranges of hardness variation in terms of microhardness index: super hardness grain—more than 20 kg/mm2; high hardness grain—from 15 to 20 kg/mm2; medium hardness grain—from 10 to 15 kg/mm2; low hardness grain—less than 10 kg/mm2. The hardness of the studied grain samples did not exceed 25 kg/mm2.

The use of Computer Vision and Artificial Neural Networks    161 TABLE 18.2  Dispersion of Grinded Particles of the Grain Sample Depending on its Hardness, in % to the Total Number of Particles Grain Hardness Shape of Grain Particle Type (type of particle code) Low hardness grain

Mean observation Variation range

Medium hardness grain

Variation range

High hardness grain

Variation range

Super hardness grain

Mean observation

Mean observation

Mean observation Variation range

0

1

2

3

4

1

4

5

4

84

from 1 to 6

from 2 to 7

from 3 to 8

from 3 to 8

from 70 to 92

1

4

8

7

78

from 2 to 7

from 3 to 5

from 4 to 8

from 2 to 5

from 58 to 80

19

8

6 from 3 to 5 6 from 3 to 9

from 15 from 11 to 31 to 25 26

18

from 22 from 16 to 28 to 22

3

64

from 1 to 4

from 44 to 62

2

48

from 1 to 3

from 36 to 59

Mathematical processing of the experiments data revealed a close relationship between the hardness of the grain and the division of grinded grain particles in shape (Table 18.2). Artificial neural network (PHP language) was used to design an artificial neural network. The average number of particle type definition errors does not exceed 0.6 %. The highest accuracy of evaluation is achieved after “learning” by a neural network of about 700 grain samples (Figure 18.1).

Figure 18.1  The decrease in the number of errors in the process of the neural network “learning.”

162    P. V. MEDVEDEV, V. A. FEDOTOV, and I. A. BOCHKAREVA

The possibility of using fractographic analysis to assess the hardness of grain is practically confirmed: on the basis of systematization of the data from the particle size analysis, the classification of particles by size and shape (based on the affinity of the contours of the particle projections with basic geometry primitives) is proposed, which is the basis for the functioning of an artificial neural network that allows to determine the class of grain hardness with a maximum error of 0.6 %. CONCLUSIONS The review of reference sources revealed the high importance of grain hardness index for the evaluation of the quality of wheat grain and its purpose. One of the promising methods for determining this indicator of hardness is a fractographic study, that is, the study of grain fractures after their destruction—grinding. In order to automate and get high-speed tests of the measurements of geometric parameters of particles, computer vision algorithms (based on OpenCV) were used in an experimental setup with a Sony Super HAD digital camera. The regression equation allowing to estimate grain hardness with an accuracy of not less than 97% is deduced empirically. The developed method allows the creation of automatic systems for analysis of milled grain in grinding lines. For the development of methods of fractographic analysis of samples of 11 wheat varieties laboratory grinding was performed. The developed artificial neural network (Artificial Neural Network library) based on the Rosenblatt’s perceptron classified the particles of laboratory grinding by shape and size. After studying a sample of particles (more than 700), the neural network can determine the class of grain hardness with an accuracy of at least 99.4 %. Measuring devices in flour mills can be used to determine the purpose of the flour produced. The use of modern information technology allows you to automate the entire process of analysis and achieve high accuracy results. REFERENCES Berkutova, N. C. (1991). Methods of evaluation and formation of grain quality. Moscow, Russia: Rosagropromizdat. Bradsky, G., & Kaehler, A. (2008). Learning OpenCV. Sebastopol, CA: O’Reilly Media, Inc. Callan, R. (2001). Basic concepts of neural networks. St. Petersburg, Russia: Williams. Chung, O. K., Ohm, J. B., Lookhart, G. L., & Bruns, R. F. (2003). Quality characteristics of hard winter and spring wheats grown under an overwintering condition. Cereal Science Journal, 37, 91–99.

The use of Computer Vision and Artificial Neural Networks    163 Huebner, F. R., Nelsen, T. C., & Bietz, J. A. (1995). Differences among gliadin from spring and winter wheat cultivars. Cereal Chemistry Journal, 72, 341–343. Khaikin, S. (2006). Neural networks: Full course. Moscow, Russia: Williams. Kruglyakov, G. N., & Kruglyakova, G. V. (1999). Commodity merchandising. Rostov-onDon, Russia: Dashkov. Maghirang, E. B., Lookhart, G. L., Bean, S. R., Pierce, R. O., Xie, F., Caley, M. S., . . . Dowell F. E. (2006). Comparison of quality characteristics and breadmaking functionality of hard red winter and hard red spring wheat. Cereal Chemistry Journal, 83, 520–528. Medvedev, P. V. (2013). Information and measuring systems of management of consumer properties of grain products (Eds.), P. V. Medvedev & V. A. Fedotov, Modern trends in Economics and management: A new view (pp. 35–51). Orenburg, Russia: Agency Press. Medvedev, P. V., & Fedotov, V. A. (2013). Information-measuring system of determination of consumer properties of wheat. Bulletin of the Orenburg State University, 3(152), 209–214. Parker, J. R. (2010). Algorithms for image processing and computer vision. Indianapolis, IN: Wiley. Shewry, P. R. (2009). Wheat. Journal of Experimental Botany, 60, 1537–1553. Smith, A. E. (Ed.). (1995). Handbook of weed management systems. New York, NY: Marcel Dekker. Souza, E. J., Martin, J. M., Guttier, M. J., O’Brien, K., Habernicht, D. K., Lanning, S. P., Carlson, G. R., & Talbert, L. E. (2004). Influence of genotype, environment, and nitrogen management on spring wheat quality. Crop Science Journal, 44, 425–432.

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CHAPTER 19

IT-TECHNOLOGIES AND ARTIFICIAL INTELLIGENCE IN MODERN MEDIA AND MEDIA EDUCATION Arevik A. Gevorgyan Pyatigorsk State University Irina N. Karapetova Pyatigorsk State University Tatiana V. Kara-Kazaryan Pyatigorsk State University

ABSTRACT IT-technologies and artificial intelligence (AI) open up new prospects for many branches of science, including journalism and pedagogy. The purpose of this chapter is to study the existing experience and the possibility of using information technology and AI in modern media, as well as in media education on the example of the student media system of the Institute of Journalism of the University of Missouri, advanced university in terms of the applica-

Meta-Scientific Study of Artificial Intelligence, pages 165–173 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

165

166    A. A. GEVORGYAN, I. N. KARAPETOVA, and T. V. KARA-KAZARYAN tion of practice-oriented approach in the educational process. The study used a set of methods and techniques, such as the method of included observation, description, comparison, and content analysis. The findings of the study are presented in two areas of generalization: (a) The news coverage through AI, which was a part of the practice, is an important area of research in terms of the impact of AI on the work of the media and the reform of media education; and (b) The connection between student media (content features, models of student media in the aspect of the use of new technologies) and the priorities of professional development of young people is obvious. The general conclusion of our study is the realization that “smart media” should become a synergy of human and machine labor. The current situation forces to impose higher demands on the new generation of media workers.

The evolution of the media has been going on for several hundred years, from newspapers and magazines, radio and television to the emergence of the Internet and new media. The question of what will be the further development of the media in the conditions of convergent media, in the continuous process of development of IT-technologies and their integration into various media is topical. Despite the fact that the term “artificial intelligence” (AI) is widely used, there is no clarity of its definition. The term refers to a particular set of technologies and techniques, as well as to the ability of a computer system to replace human activity and perform creative work, which traditionally have been thought of as human prerogatives. This characteristic is also supported by the definition of AI given in the report of Scott Brennen, a researcher at Reuters in the United Kingdom: AI is a term that is both widely used and loosely defined. Most fundamentally, AI is a collection of ideas, technologies, and techniques that relate to a computer system’s capacity to, as Dickens Olewe of the BBC described it, “perform tasks normally requiring human intelligence” (Brennen, Howard, & Nielsen, 2018). The largest international news agency Associated Press identifies five areas of application of AI technologies in journalism. These include image recognition and computer vision, robotics, machine learning, natural language (processing and generation), and speech (text-to-speech and speech-to-text; Marconi & Siegman, 2018). The news of the earthquake in Jiuzhaigou in Sichuan province on August 8, 2017, the robot wrote and published in just 25 seconds. A human journalist would not be able to do this at the same speed (Xining, 2018). It was China, where the world’s first TV presenter-robot appeared. It was presented on November 9, 2018 at the 5th World Conference on the Internet in the village of Wuzhen in Eastern China (Zhejiang province). To produce an automated TV program, it is enough to download an example of a video of the real presenter and the text (Bratsky, 2018).

IT-Technologies and Artificial Intelligence in Modern Media    167

The leader of the global news market, The Associated Press, which develops new technologies and tools for professional journalism, presented the report “The Future of Augmented Reality: A Guide for Editors in the Age of Smart Machines,” which clearly describes the future of journalism. “Of course, if we had raised the issue of artificial intelligence in the newsroom three or four years ago, such material would have been moved to the section of flying machines . . . But today it would be a mistake,” Dwayne Desaulniers from The Associated Press remarked during a recent webinar for editors and managers of editorial offices in the United States (Marconi & Siegman, 2018). The report published in 2017 by Reuters Institute showed that AI and technology of automated journalism are widely used in the media to verify the validity of the facts, the optimization of news flow, news editing, and formation of personal recommendations. Thus, the Washington Post used the system Heliograf for the production of auto news for the pre-election campaign coverage in November 2017. Also, in the course of a pre-election campaign, the New York Times announced the use of its bot for Facebook messenger. The growing popularity of virtual assistants (e.g., Amazon Echo & Google Home) is supported by the fact that such media organizations as AP, Hearst, NPR, BBC, The Wall Street Journal, and Economist have started to use audio interfaces in their work (Schmidt, 2017). Public relations professionals keep on looking for ways to make faster and more informed decisions that will lead to results and achieve goals. The more detailed the goals become, combined with smaller audiences, rapidly expanding industries, and higher communication expectations, the greater the need in their effective implementation. “AI can’t do this for us directly, but these technologies will be an important component of the work we do,” Abby Whitaker, a correspondent for the magazine Forbes and a founder of the PR agency “The Abbi Agency” (Whitaker, 2017). From a global perspective, network platforms with hundreds of millions of users are increasingly acquiring media characteristics, while the mainstream media, with its advantages in content creation, are also trying to create their own platforms for information dissemination. It can be assumed that the platforms will become more and more media, and the media will create their own platforms, and this will be one of the trends in the development of media (Gevorgyan, Yakovenko, & Goncharenko, 2019). In this process false and shoddy information will be the biggest problem of the platforms. In the era of traditional media, journalists manually checked and edited information. However, in the era of “smart media” professionals and consumers are faced with information collected through aggregation. It is becoming clear that the media industry recognizes the importance of using AI in news production and, moreover, recognizes that AI

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technologies can improve the quality and speed of information delivery and facilitate work in this area. It can be argued that in the context of convergent editions, in the era of “smart machines,” the activity of media resources is based on new conditions and principles (Kachkayeva, 2010). Thus, we assumed that the latest trends in the media also determine the vector of requirements for the professional training of journalists. In the light of the above, the question arises about the transformation of the educational paradigm, the formulation of the social order (Gracheva, 2017) and the strengthening of the technological component of the educational process, especially in relation to the specialized training of journalists and media specialists, and thus the modernization of the market of educational services in the media sphere. METHODOLOGY For further analysis of the relationship between the development of information technology and media education, we have chosen the media system of the Institute of Journalism of the University of Missouri, as one of the leading in the modern market of educational services in the specialized training field of journalism. The following facts and data are based on the experience and materials obtained by Gevorgyan during her scientific and methodological training on the “Fulbright Faculty Development Program for Young University Teachers” in 2013–2014 at the Institute of Journalism of the University of Missouri. In order to consider the most relevant elements of the problem of our study in an integrated manner we used both general scientific and media studies research methods: description, comparison, content analysis, and the technique of the included observation. RESULTS The media system of the Institute of Journalism of Missouri (“Missouri Media and Practical Experiences”) is an example of the latest technological transitions in education, which confirms two patterns, diverse and deeply related. It demonstrates the high importance of specialized addressing: these are university media, addressed to future journalists, their teachers and organizers of the institutional process, as well as to students and teachers in general. Features of the content and models in the aspect of the

IT-Technologies and Artificial Intelligence in Modern Media    169

formation of the media environment of the university demonstrate a fairly broad focus, the latter is associated with both structural convergence and integration of current media trends in the object space. A special emphasis on the latest trends is fixed in the structural, quantitative features. The system includes seven main subsystems, projects (AdZou, Columbia Missourian, KBIA-FM, KOMU-TV, Missouri Business Alert, Mojo Ad, and Vox Magazine). One of these subsystems has four substructures. Such a multidimensional hierarchy (including radio, TV, newspaper and magazine, published in the central part of Missouri) is an adequate form of organization to deal with complex tasks. The content of the materials in the various subsystems is partially and appropriately duplicated, which increases the functional reliability of the whole. AdZou is a full-service agency staffed by the nation’s brightest strategic communication students at the renowned Missouri School of Journalism. It’s based on the “Missouri Method” of learning by doing (The Missouri Method–Missouri School of Journalism, 2018). The Columbia Missourian is a digital-first publication that was founded in 1908 as a community newspaper. It regularly beats out the St. Louis Post-Dispatch, Kansas City Star, and Columbia Tribune for state journalism awards. Many of its alumni now write for leading newspapers, magazines, and websites. Others are working as photographers, designers, graphic artists, copy editors, Web developers and the like (The Missouri Method–Missouri School of Journalism, 2018). The third subsystem develops the abovementioned trends in the electronic media KBIA-FM, the mid-Missouri’s NPR-member station. Students produce stories with audio, video, and text for the website while also producing traditional radio newscasts and long-form stories. The station is owned by the University of Missouri and is one of the most successful public radio stations in the nation (The Missouri Method–Missouri School of Journalism, 2018). KOMU-TV is the only university-owned commercial television station and major network affiliate in the United States that uses its newsroom as a working lab for students. It reaches 40,000 homes in 15 mid-Missouri counties. KOMU is affiliated with both NBC and CNN (The Missouri Method– Missouri School of Journalism, 2018). Missouri Business Alert is a digital newsroom that publishes the top business news from across the state. MBA is managed by professionals and staffed by Missouri School of Journalism students. The site regularly features hard-hitting and timely articles and video presentations from a statewide perspective (The Missouri Method–MissouriSchool of Journalism, 2018). The sixth subsystem complies with the important requirements appropriate to the youth age category. This is an agency focused on musical, artistic

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content, Mojo Ad is the premier student-staffed professional-services advertising agency in the country. The lab offers real-life work experience to students attending the Missouri School of Journalism. With specialization in all things young, MOJO Ad works with local, regional and national clients whose brands target teens and young adults (The Missouri Method–Missouri School of Journalism, 2018). Vox Magazine publishes a weekly print edition and a daily website, providing insight on local news and culture. The VoxMagazine.com website has 30,000 weekly unique visitors (The Missouri Method–Missouri School of Journalism, 2018). “Message” of the media environment of the university is defined as “Learning by doing.” A preliminary analysis of the material adds three characteristics to the principle of learning by doing. First, this university policy is not just driven, in particular, by work on the creation of media, but also achieved through it. The triad of “content-model-media environment” is revealed in the unity of learning and practice. Secondly, this principle, seemingly banal, demonstrates its unique essence in the system of the seven subsystems. Thirdly, in our opinion, this principle includes systemic interdependence—it, as shown by all seven subsystems, is inseparable with the orientation of “doing by learning.” Practice grows out of the educational process that corresponds to the co-development and self-development; and therefore, the line learning by doing in some cases serves as some kind of basis of certain media. So, the Missouri School of Journalism is determined by a system of features, among which prioritization is the most noticeable one. It correlates with all the previously considered characteristics, and shows the traditional connection “content-model-digital media environment” in an original way (Temnikova & Vandysheva, 2018). However, the Institute of Journalism of Missouri at this stage, taking into account and including in its practiceoriented educational process the majority of widely used media digital technologies, considers the possibility of using AI and automated journalism in its educational program only at the theoretical level without backing up the theoretical material by practice. In this true self-presentation, the primacy is the more significant that serves as a reference point for other universities. In this regard, it is not primarily the issue of the novelty of Missouri’s approaches, but the matter of the current correlation with other student media in conjunction with the newly emerging technologies and trends. CONCLUSIONS Conclusions based on observation and analysis are possible in different directions of generalization.

IT-Technologies and Artificial Intelligence in Modern Media    171

The direction that generalizes the relationship between the student media (content features, models of foreign student media in terms of the use of new technologies) and the priorities of professional development of young people has the great explanatory power. The importance of the practice-oriented approach, which, earlier, in 2018, was shown on a completely different media material (Gevorgyan, 2018) is confirmed. To generalize such different and similar of importance phenomena as the deepening of professionalism, including in the field of IT-technologies and AI, and continuous monitoring and accounting of technological innovations and capabilities of intelligent systems, it is only possible on the basis of professional self-affirmation of the participants of the university media environment (Stolyarchuk, 2003). The goal of strengthening students’ professional self-affirmation is inseparable from the enrichment of content and models and therefore requires taking into account the diversity of correlations between the university and other media, including mass media. In addition, the relationship of balance and the possible exclusion of certain media from the students’ activity, such as traditional and technologically conservative, should be carefully analyzed in the future. The previous generalizations also explain the interrelation of the triad “media content-model-digital media environment,” as well as the essence and role of its participants. In particular, the activity of students as journalists, interns, organizers of special events, even editors, and so on is not motivated by the intention to compensate for the lack of mastery. This activity demonstrates objective goals for this age and status: an exceptional need for self-development, and a lively perception both of various traditions and technological innovations. The first of the generalizations serves as a basis for the explanation of the security and relevance of information in new conditions. The university media environment is suitable for the archival function and provides one of the solutions to the age-old problem—the selection of materials for long-term storage. Thus, the student newspaper Cambridge Reporter became an online publication in 2011, and its issues have been available for viewing both in html and .pdf versions from the 1997–1998 academic year to the present time. Some information is available only for the current university community, most of the information assets on the list are public and free to view. Relevant materials for the period from 2010 to 2019 are in demand due to possible new links with the situations of recent years, for example, in order to justify the discussion decisions. The second direction of generalization is based on the relationship of trends in the development of modern media and media education. We believe that the work of the media in the era of “smart media” entails the changes in the modes by which the information is being provided, at

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the same time transforming the media into personalized and differentiated structures. However, in our opinion, the main news channels have been and will remain the key ones for providing information. As AI technology spreads to various areas of public life through new information products, major research initiatives, and automated decision-making, readers need to better understand how technical research and expert opinions convey information to the public. News coverage can provide the public with space and resources for collective examination and resolution of society’s immediate problems. There is every reason to believe that AI in the future will be a powerful means of shaping public opinion, influencing large segments of society. In this regard, we consider it necessary that news agencies using AI in their work are guided by the rules and the law when creating news information. Thus, the functioning of AI in the media is an urgent problem of modern society, and we find it appropriate to study this problem from the point of view of various social sciences, such as political science, sociology, psychology, pedagogy. It is also worth thinking about what will be the further development of journalistic science in this connection. The practice of news coverage through AI has become an important area of research in terms of the impact of AI on the work of the media and the reform of media education. In such conditions, in particular, the university media, as a generalized experience, and the media education as a system in general, has not just become a repository of quality information, but its custodian. The main conclusion of our study is the recognition that “smart media” should become a synergy of human and machine labor. The current situation makes it a priority to establish higher standards for the qualifications of the new generation of workers in the sphere of media, which should not only examine and verify the facts, express and justify opinions, transmit values and protect the interests of the public, but they should also learn how to use AI technologies and to control them. It is a skill that all the specialists in the sphere of mass media should master in the near future. REFERENCES Bratsky, J. (2018, September 11). China presented the first robot news anchor. Retrieved from https://tvzvezda.ru/news/vstrane_i_mire/content/201811 090908-dtw2.htm Brennen, S. J., Howard, P. N., & Nielsen, R. K. (2018). An industry-led debate: How UK media cover artificial intelligence. Retrieved from https://reuters institute.politics.ox.ac.uk/our-research/industry-led-debate-how-uk-media -cover-artificial-intelligence

IT-Technologies and Artificial Intelligence in Modern Media    173 Gevorgyan, A., Yakovenko, Yu., & Goncharenko, A. (2019). Growing trends in modern journalism: Youth approach. Philology: International scientific journal, 1(19), 34–35. Gevorgyan, A. A. (2018). Features of content and models of foreign student periodicals in the aspect of the formation of the media environment of the university. PSU Bulletin, 2, 78–82. Gracheva, Zh.V. (2017). The educational paradigm of the twentieth and twenty-first century: Higher education and the social order. Mitrofanov Church-historical readings. Voronezh, Education Department of Voronezh. Metropolia, 104–109. Kachkayeva, A. G. (2010). Journalism and convergence: Why and how traditional media turn into multimedia. Moscow, Russia: Focus-Media. Marconi, F., & Siegman, A. (2018). The future of augmented journalism: A guide for newsrooms in the age of smart machines. Retrieved from https://insights.ap.org/ uploads/images/the-future-of-augmented-journalism_ap-report.pdf Schmidt, T. (2017, May 16). Smarter journalism: Artificial intelligence in the newsroom, European journalism observatory. Retrieved from https://en.ejo.ch/ ethics-quality/smarter-journalism-artificial-intelligence-newsroom Stolyarchuk, K. I. (2003). Influence of mass media on personality development: Scientific, social and cultural problems of students. Minsk, 97–101. Temnikova, L. B., & Vandysheva, A. V. (2018). Genre in contemporary media and Internet space. Bulletin of the Pyatigorsk State University, 1, 97–100. The Missouri Method–Missouri School of Journalism. (2018). Retrieved from https://missouri.edu/search/?q=missouri+method Whitaker, A. (2017, March 20). How advancements in artificial intelligence will impact public relations. Forbes. Retrieved from https://www.forbes.com/sites/ theyec/2017/03/20/how-advancements-in-artificial-intelligence-will-impact -public-relations/#6e7f285641de Xining, L. (2018). People’s Daily explores what kind of intelligent product humans and machines can co-create. Rossiyskaya Gazeta (Federal Issue No. 254). Retrieved from https://rg.ru/2018/11/12/kak-iskusstvennyj-intellekt-mozhet -izmenit-smi.html

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CHAPTER 20

AUTOMATED SYSTEM OF ENVIRONMENTAL MONITORING AS A TOOL TO IMPROVE SOCIO-ECOLOGICAL AND ECONOMIC EFFICIENCY OF ENVIRONMENTAL MANAGEMENT AT MICROAND MESO-ECONOMIC LEVELS Roman V. Revunov Southern Federal University Vladimir A. Gubachev South-Russian State Polytechnic University Don State Agrarian University Vladimir B. Dyachenko South-Russian State Polytechnic University Kometa T. Paytaeva Chechen State University Kseniya Yu. Boeva Southern Federal University

Meta-Scientific Study of Artificial Intelligence, pages 175–183 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

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176    R. V. REVUNOV et al.

ABSTRACT The purpose of the chapter is to investigate the environmental practice of the Rostov region at the current stage, to establish the main trends and patterns, as well as factors that determine the regional environmental and industrial characteristics, to justify the directions of green economy at the micro- and meso-economic levels. The application of statistical methods as well as methods of scientific abstraction, modelling of socioeconomic and environmental effects of environmental management practices of the region during the research process allowed to identify the main attributive characteristics of natural resources of the Rostov region at the present stage, and ecological and socioeconomic consequences of unbalanced environmental management activities. Total emissions from stationary and mobile sources increased by 51.3 thousand tons from 618.7 in 2015 to 670.0 thousand tons in 2017. At the same time, the share of harmful emissions from mobile sources of pollution, which make up a significant part of the pollution structure, decreased from 73.3 to 70.9 %. Moreover, there is an increasing tendency in the absolute volume of pollution by mobile sources: from 453.8 thousand tons in 2015 to 475.1 thousand tons in 2017 (+21.3 thousand tons). Pollutant emissions from stationary sources also have increased from 164.9 thousand tons to 194.9 thousand tons during the period under review. The factors affecting the environmental and industrial characteristics of the Rostov region are the following: uneven distribution of industrial-economic and transportlogistic infrastructure on the territory of the region; the geographical position of the region. The results of the study can be used by local governments, public authorities of the Russian Federation in the preparation of plans for socioeconomic and environmental development, formulation of environmental strategies and programs, as well as in the planning of environmental activities by economic entities.

A distinctive feature of the currently evolved environmental practice at the meso- and micro-economic levels is the increasing scale of natural resource consumption, provoking their irreversible depletion. This is fully true in relation to the regional environmental practice of the Rostov oblast, the extensive nature of which combined with the passivity and inertia of the chosen mechanism of environmental management have led to additional costs of economic entities and social tension. The low efficiency of environmental protection activities has aggravated the negative repercussions of wasteful consumption patterns of natural and raw materials. Several studies Revunov (2016); Revunov and Yanchenko (2017); and Revunov, Chumakova, and Yanchenko (2016) have indicated that currently in the Russian Federation there is an extremely destructive, environmentally destabilizing practice of using water resources, leading to regression of socio-ecological and economic well-being of the population. According to Anopchenko and Murzin (2012),

Automated System of Environmental Monitoring    177 In recent years, Russia has been actively working on greening the economy through the search and implementation of tools to stimulate the greening of business: the environmental legislation has been significantly supplemented, a number of changes have been introduced to normative documents in terms of differentiation of taxes and tax payments, tax benefits, certification of enterprises in accordance with ISO standards, lists of the best technologies have been created. (p. 22).

This fully applies to the Rostov region. Consider the economic structure and environmental features of the region. METHODOLOGY The quality of the environment is one of the factors that determines sustainable socioeconomic development. The environmental situation formed as a result of environmental activities has a direct impact on the level of morbidity of citizens, household spending on health care, the costs of economic entities and the budget system associated with the elimination of the negative consequences of environmentally unbalanced use of natural resources. The current system of nature management in Russia is irrational in the context of ecological and economic interests, and has resulted in substantial regression of the resource potential, degradation of ecosystems, loss of biodiversity, increase of environmentally related diseases, rising costs of economic entities. Therefore, there is no doubt about the need for further scientific search for progressive theoretical and practical solutions aimed at socio-ecological and economic optimization of environmental management, which would be adequate to the Russian realities and fully take into account the socio-environmental requirements of sustainable development. Satisfaction of material needs occurs through regional socioeconomic policy which is aimed at the reproduction and rational use of natural resources. Qualitatively and quantitatively different objects (across regions) with various forming factors thus require appropriate mechanisms for the implementation of regional environmental policy in the framework of sustainable development of the region, and highlighting its specific priorities. RESULTS Taking into account the above, we will consider the modern environmental practice of the Rostov region. Currently, this region has a developed industrial and economic complex, the core of which is formed by the following sectors of the economy: heavy engineering, thermal power, non-ferrous metallurgy, and chemical industry. One of the features of the economy

178    R. V. REVUNOV et al.

of the region is a low technical and technological level, so that material throughput per unit of output is relatively high, as well as the energy intensity of production. Ecologically unbalanced environmental management is one of the factors limiting the rate of economic growth (Gubachev, 2014), as additional costs due to inefficient environmental practices at the micro- and meso-economic levels reduce the competitiveness of the regional economy as a whole. Currently, the Rostov region is part of the Southern Federal District of the Russian Federation, and in terms of population is one of the largest regions of our country. The biggest cities of the Rostov region are: Rostov-onDon, Taganrog, Shakhty, Novocherkassk, Novoshakhtinsk, and Taganrog. The analysis of the information presented in Table 20.1 (Environmental Bulletin of the Don, 2018) leads to the following main conclusions. The scale of anthropogenic impact on the air basin of the Rostov region increased during the observation period. The total volume of emissions from stationary and mobile sources increased by 51.3 thousand tons: from 618.7 in 2015 to 670.0 thousand tons in 2017. At the same time, the largest share in the pollution structure is harmful emissions from mobile sources, the share of which in the total volume of emissions decreased slightly from 73.3 to 70.9 %. At the same time, there is an increasing tendency in the absolute volume of pollution by mobile sources: from 453.8 thousand tons in 2015 to 475.1 thousand tons in 2017 (+21.3 thousand tons). Pollutant emissions from stationary sources also increased from 164.9 thousand tons to 194.9 thousand tons during the period under review. Gaseous and liquid substances account for the greatest contribution in the structure of pollution from stationary sources, they account for the greatest share in the period 2015–2017. These circumstances determine the high level of anthropogenic impact of nature users of the Rostov region on the environment, in particular, the atmospheric basin. Indicators of air pollution in industrial and economic agglomerations of the Rostov region in 2017 are reflected in Table 20.2 (Environmental Bulletin of the Don, 2018). From Table 20.2, the air pollution index calculated for the five main pollutants in 2017 in the Rostov region ranges from 0.2 in Tsimlyansk, which corresponds to a low level of pollution, to 11 in Novocherkassk, which is extremely high. In this context, it is necessary to conduct a comparative analysis of the average annual concentrations of pollutants in the Russian Federation and the Rostov region (Table 20.3; Environmental Bulletin of the Don, 2018). Concentrations of suspended solids are above the national average in the cities of Azov, Novocherkassk, Rostov-on-Don, Taganrog, and Shakhty. The level of carbon monoxide pollution exceeds the national average in municipalities such as Novocherkassk, Rostov-on-Don, Taganrog, Millerovo, and Shakhty. The excessive levels of air pollution by nitrogen dioxide in relation to the national average indicator are recorded in Azov, Rostov-on-Don,

Indicator

%

Total Anthropogenic Impact

618.7

100.0

0.3

73.0

2.0

451.8

railway transport

road transport

20.8 73.3

128.5 453.8

gaseous and liquid substances

Air pollution from anthropogenic mobile sources, thousand tons, including:

26.7 5.9

164.9 36.4

solid substance

Air pollution from anthropogenic stationary sources, thousand tons, including:

tons (thous.)

2015

629.3

2.3

457.9

460.2

135.7

33.4

169.1

tons (thous.)

2016

Years

%

100.0

0.4

72.8

73.1

21.6

5.3

26.9

670.0

2.5

472.6

475.1

157.9

36.9

194.9

tons (thous.)

2017 %

100.0

0.4

70.5

70.9

23.6

5.5

29.1

51.3

0.5

20.8

21.3

29.4

0.5

30.0

In absolute values

8.3

0.05

–25.0

–2.4

2.8

–0.4

2.4

%

Dynamics

TABLE 20.1  Parameters of the Negative Impact on the Atmospheric Air of the Rostov Region for the Period 2015–2017 Automated System of Environmental Monitoring    179

180    R. V. REVUNOV et al. TABLE 20.2  Indicators of Air Pollution in Industrial and Economic Agglomerations of the Rostov Region in 2017 Atmospheric Pollution Index Calculated for the Five Main Pollutants

Pollution Level

Municipality

Main Air Pollutants

Azov

carbon monoxide, dust, nitrogen dioxide, formaldehyde, and nitrogen oxide

5

Elevated

Volgodonsk

formaldehyde, dust, carbon monoxide, nitrogen dioxide, and nitrogen oxide

3

Low

Novocherkassk

dust, hydrogen fluoride, formaldehyde, carbon monoxide, and solid fluorides

11

High

Millerovo

formaldehyde, dust, carbon monoxide, nitrogen dioxide, and nitrogen oxide

4

Low

Rostov-on-Don

Benz(a)pyrene, hydrogen fluoride, soot, carbon monoxide, and dust

9

High

Taganrog

nitrogen dioxide, dust, nitric oxide, hydrogen chloride, and carbon monoxide

6

Elevated

Tsimlyansk

dust, carbon monoxide, nitric oxide, nitrogen dioxide, and sulfur dioxide

0.2

Shakhty

dust, nitrogen dioxide, nitric oxide, carbon monoxide, and Benz(a)pyrene

5

Low Elevated

Taganrog, and Shakhty. The amount of air pollution with nitrogen oxide is higher than the average for Russia is noticed in such municipalities as Azov, Rostov-on-Don, Taganrog, and Shakhty. The pollution of the atmospheric basin with hydrogen fluoride exceeds the average Russian concentration of this pollutant in Rostov-on-Don and Novocherkassk. The level of air pollution with formaldehyde exceeds the average Russian value in the cities of Azov, Volgodonsk, Novocherkassk, Rostov-on-Don, and Millerovo. Among the negative consequences of air pollution by various substances, it is necessary to note the growth of environmentally related diseases, which creates an additional burden on the health care system which is financed from the national budget, as well as an increase in social tension. Another negative consequence of the anthropogenic impact on the air is the forced migration of people from ecologically adverse areas (Revunov & Revunov, 2018).

0.049 0.009 2.100 2.100

Hydrogen fluoride

Hydrogen chloride

Formalde-hyde

Benzo(a)py-rene

0.003

Hydrogen sulphide 0.033

0.002

Nitrogen oxide

0.004

0.025

Nitrogen dioxide

Phenol

0.041

Carbon monoxide

Ammonia

0.007 1.400

Suspended solids

0.6 00

0.600

0.010

0.040

0.044

1.400

0.002

0.181

0.122

Sulphur dioxide

Pollutant

Azov

Average Value for Russia mg/m3

Volgodonsk 0.100

0.100

0.012

0.001

0.009

0.008

0.800

0.005

0.080

Novocherkassk 16.400

16.400

0.021

0.012

0.003

0.020

0.020

3.400

0.013

0.400

Rostov-on-Don 1.700

1.700

0.013

0.009

0.030

0.002

< 0.001

0.026

0.048

1.900

0.004

0.238

Taganrog 0.300

0.300

0.085

0.056

0.075

2.300

0.002

0.167

—

—

< 0.001

0.002

0.002

< 1.000

< 0.001

0.016

Tsimlyansk

Municipalities of the Rostov oblast

Shakhty 0.600

0.600

0.001

0.039

0.066

2.000

0.003

0.263

—

—

0.014

0.001

0.010

0.010

4.400

0.005

—

Millerovo

TABLE 20.3  Comparison of Average Concentrations of Pollutants in the Russian Federation and Rostov Region in 2017

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CONCLUSIONS To sum up, it is necessary to formulate the following: 1. The quality and availability of water resources are the factors determining the gross regional product growth rate. Poor quality of the water used for irrigation of agricultural crops disrupts the vegetation cycle of plants (Revunov, 2016). 2. As the analysis of water resources use at the micro- and meso-economic levels shows, the Rostov region is characterized by wasteful resource consumption which has the following attributive features: a high level of water losses during transportation, low quality of water purification system, and secondary pollution caused by the use in the process of water treatment with outdated technical and technological solutions based on the use of chlorine-containing reagents. 3. One of the most acute problems of water use in the Rostov region is the widespread pollution of water bodies in the region by technogenesis products. 4. One of the factors that determine the specifics of water management in the region is the uneven distribution of industrial, transport and logistics infrastructure, located on territories of large agglomerations: Rostov, Novocherkassk, Bataysk, Taganrog, Shakhty, Novoshakhtinsk, Belokalitvensk, and Volgodonsk. 5. Municipalities of the Rostov region do not have the necessary material, technical, organizational, and financial resources for the proper maintenance of hydraulic infrastructure (Murzin, 2012). 6. An important factor in stabilizing the socio-ecological and economic situation in the Rostov region may be the creation of an automated environmental monitoring system. This system is organized as a network of automated centers for monitoring the quality characteristics of the air basin and priority water bodies. 7. The creation of an automated system of environmental monitoring will allow for a timely response from the supervisory authorities for violations of environmental legislation, and provide the executive authorities of the Rostov region with reliable information about the environmental processes in the region, which will improve the quality of management decisions. Environmentally unbalanced nature management of the Rostov region is currently a factor limiting the progressive socioeconomic development. Additional costs arising from the use of resource-intensive environmental practices reduce the competitiveness of both business entities and the

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economy of the region as a whole. In connection with the above, it is necessary to develop and implement in management practice at the micro- and meso-economic levels of organizational, economic, and administrative law measures that encourage users of natural resources to reduce anthropogenic impact on the environment and, thus, minimize socio-ecological and economic damage. Such measures should include the creation of an automated environmental monitoring system. REFERENCES Anopchenko, T. Yu., & Murzin, A. D. (2012). The structure of socio-economic and environmental components of the system of integrated development of territories. Science and Education: Economy and Economics; Entrepreneurship; Law and Management, 1(20), 22–29. Environmental Bulletin of the Don. (2018). About the Environment and Natural Resources of the Rostov Region for 2017. Rostov-on-Don, Russia: The Government of the Rostov Region. Gubachev, V. A. (2014). Organizational and economic support of reproduction of fertility of agricultural lands. In V. A. Gubachev (Ed.), Modern trends of regional development: Materials of the international scientific and practical conference (pp. 73–77). Rostov-on-Don: Scientific Research Centre of Economics, Mathematics, and Management. Murzin, A. D. (2012). Comprehensive assessment of socio-ecological and economic factors of urban areas. Regional Economy: Theory and Practice, 8, 44–50. Revunov, R. V. (2016). Organizational, economic, and regulatory aspects of improving the efficiency of water management system at the meso- and microeconomic levels. Water Treatment, 4, 41–46. Revunov, R. V., & Revunov, S. V. (2018). Directions of modernization of the mechanism of environmental management at the regional level. Regional Economy: South of Russia, 3, 156–164. Revunov, S. V., Chumakova, V. N., & Yanchenko, D. V. (2016). Tools of stimulation of effective use of resource potential at the regional level. Competitiveness in the Global World: Economy, Science, Technology, 7-2(19), 78–82. Revunov, S. V., & Yanchenki, D. (2017). Tools to improve the efficiency of natural resources at the micro- and mesoeconomic levels. Competitiveness in the Global World: Economics, Science, Technology, 3-1(32), 145–147.

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CHAPTER 21

THE IMPACT OF ARTIFICIAL INTELLIGENCE ON THE DEVELOPMENT OF HUMAN RESOURCES TECHNOLOGIES Galina N. May-Boroda Pyatigorsk State University Elena Yu. Shatskaya North Caucasus Federal University Natalia P. Kharchenko North Caucasus Federal University Vitaliy F. Zhuravel North Caucasus Federal University Ekaterina V. Efimova Pyatigorsk State University

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ABSTRACT The chapter describes the method of technologization of personnel work in the conditions of transformation of the digital economy, reveals the essence of the impact of artificial intelligence (AI) on changing organizational procedures aimed at making rational personnel decisions. The purpose of the research is to generalize theoretical positions and develop methodological recommendations for personnel management in the region in the conditions of digitalization of the economy. Theoretical and practical research of Russian and foreign scientists in the field of personnel development, research of personnel management tools in the conditions of digitalization served as the basis of the study. The possible effects of digital transformation of socioeconomic systems have a wide range of positive effects, manifested in strengthening market positions, expanding market niches, and achieving new competitive advantages in the emerging digital economy. The application of this approach to digital transformation is conditioned by the possibility of ensuring the systematic and complex character of the processes of digitalization and digital transformation. The novelty of the authors’ approach is to distinguish the key elements of personnel work in the digital reality of socioeconomic systems used in the study through personnel technologization. The result of the research is the formation of a methodological approach to digital transformation of human resources technologies using AI elements for enterprises in the region, characterized by the inclusion of characteristics of the digital framework of personnel competence, aimed at recognizing patterns from a large array of data in personnel work.

In the new economic environment, the HR management system is undergoing a process of digital transformation. Most HR managers of Russian companies are now focused on digitalizing HR technologies. The trend towards the introduction of digital technologies in staff management poses several tasks that should be aimed at ensuring the accuracy and completeness of the algorithms of the personnel tool, ease of use of intelligent systems, generating specialized solutions for specific client tasks. The introduction of human resources systems based on artificial intelligence (AI) contributes to improving the reliability and greater focus of human resources services on solving specific problems, increasing their productivity and efficiency. The digital economy sets the direction for the development of personnel management, which necessitates research and comprehensive analysis of the processes of digital transformation of personnel technologies. These circumstances determine the relevance of the research topic in terms of technologization of human resource processes to identify the impact of digital transformation on the development of human resource technologies.

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METHODOLOGY The methodological basis of the research is formed by the general scientific principles of the systematic approach; methods of logical, factor, comparative, and management analysis. The theoretical basis of the research consists of scientific works of Russian and foreign researchers and practitioners in the field of personnel management, innovation management, the theory of management, and development of socioeconomic systems, as well as scientific and practical experience in the digital economy. The development of almost all sectors of the economy is subject to digitalization to some extent (Starodubtseva & Markova, 2018). RESULTS A unified methodological approach to the study of personnel processes implies the study of personnel technologies. HR technologies constitute a set of organizational procedures aimed at optimizing personnel decisions, and include personnel planning, recruitment, selection of personnel, determination of wages and benefits, career guidance and adaptation, training, performance evaluation, reserve training and development management, demotion, transfer or dismissal, industrial relations, health and social issues. (Gaponenko & Pankrukhin, 2003)

The main elements of HR technologies can be structured as follows: informational—aimed at forming information training of personnel; organizational—aimed at creating an adaptive organizational structure; training technologies—designed to organize timely retraining and re-profiling of personnel (Kusakina, Vorontsova, Momotova, Krasnikov, & Shelkoplyasova, 2019). Theoretical analysis of thematic sources of information and comparative analysis of personnel management practices allowed us to formulate the concept of improving personnel management in the digital reality. This concept is based on the parameters of the personnel management system and assessing the impact of digitalization conditions on the development of personnel technologies. The goal of the concept was to technologize human resources management based on the use of AI. It should be noted that in Russian practice there is no clear understanding of the optimal concept of personnel work that corresponds to modern conditions of digitalization. Each company independently determines the directions and opportunities for using AI for business (Pikuleva, 2017). The obvious interest of HR managers in studying the functions, principles, and methods of personnel work based on the principles of digitalization

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indicates the need to create a single, universal concept of activity in this area. The process of improving the organization’s HR management with the active use of AI technologies involves a sequence of stages. The term “HR technologization” is used by us to describe the digital transformation of HR technologies. A step-by-step approach to HR technologization implies continuous improvement and optimization of HR processes: 1. 2. 3. 4. 5.

regulation of personnel management; structuring of the personnel management objectives; implementation of personnel policy; evaluating the effectiveness of HR management; and development of personnel technologies.

The first stage involves analyzing the personnel situation, conducting a personnel audit, and developing HR metrics that makes it possible to compare the company with competitors in order to form adequate personnel divisions. Artificial intelligence systems, using all statistical data, are able to contrast and compare the company’s regulations with normative documents. This would help forecast the extent to which the existing HR service model is able to effectively solve the assigned tasks. At the second stage, a set of principles and methods for building a personnel service is defined. Processing, calculation, forecasting, and management of various processes and systems at this stage takes place using AI, which increases the accuracy and speed of obtaining results. The third stage includes the development of a system of HR goals, including the formation of quantitative tasks and ranking of qualitative expectations in terms of their compliance with the system of goals (Vorontsova, Dedyukhina, Kosinova, Momotova, & Yakovenko, 2019). The fourth stage is aimed at forming requirements for professional training of employees, determining staff composition, rights, and responsibilities. Artificial intelligence at this stage is of great importance, because by eliminating all mistakes made in the process of conducting interviews and reviewing candidates’ resumes using analytical systems, more than a third of employees’ productivity can be improved. The fifth stage is aimed at the development of human resources technologies. When implementing personnel procedures, it is important to take into account the formation of professional skills, the level of technical competences, as well as the emotional and psychological characteristics of applicants. It is obvious that the capabilities of AI at the level of human resources technologization are simply huge. The pace of technological change is accelerating, and understanding of the need for machine thinking has become widespread (Atkinson, 2017).

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Developments based on AI assist in making personnel decisions. A wide range of functionality of AI systems includes studying the applicants’ resumes, searching for suitable candidates, identifying effective employees within the company, processing natural language for reading text in social media channels, evaluating the staff morale, and selecting specialists who may be the most successful in their division. Our research shows that HR work requires algorithms that can track and study the knowledge, behavior, and actions of the company’s most effective employees, and then generate a selection of training programs for all employees based on the results of this analysis. The systems for learning based on AI are able to request feedback, read the comments, and predict the mood of employees. Each employee can use this data to compare personal and team results with those of higher-performing professionals. Artificial intelligence can analyze electronic business correspondence, identify possible violations of ethical standards, identify restricted areas for company management, monitor compliance with digital security requirements, and make recommendations to prevent employee fraud. The selection of candidates via bots greatly simplifies and optimizes the process of interaction between the HR manager and the applicant. The company must create conditions for staff to understand the importance of using information and communication technologies in their activities and the positive impact of AI on the quality of existing processes in personnel work (Aptekman et al., 2017) The fundamental requirements for building digital personnel management systems are summarized in Table 21.1. It is absolutely certain that AI is able to automate human resources processes. The use of modern digital portals for personnel search allows the HR manager to organize effective recruitment and hiring of staff, on the one hand, and candidates to improve their career opportunities due to access to an extensive database of current vacancies, on the other (Aptekman et al., 2017). Tools such as information and communication technologies in personnel management, searching of the employees using Internet resources, and e-learning perform separate functions related to personnel management. But AI means the ability to analyze and integrate optimal solutions, not just process and classify information. Therefore, management expert systems in the field of HR can be a good tool for business. Today, the digital competence framework of personnel includes digital and information skills, which characterize the ability to localize, organize, understand, develop, create, and distribute information using digital technologies at different levels of competence (Berg, Furrer, Harmon, Rani, & Silberman, 2018).

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The Impact of AI

To provide for the appointment of staff

• to organize finding suitable personnel with the help of electronic search resources and intelligent systems • to carry out the selection of a suitable job for the applicant • to establish interaction, communication, and collaboration with the help of intelligent technologies

To provide competence, training, qualifications, and awareness of the personnel

• to identify regularly the needs for competent personnel and training • to plan staff re-training and e-learning • to ensure compliance of policies in the field of digital transformation with the goals of the company

To identify and provide a list of necessary information and relevant knowledge

• to establish and verify procedures for information management • to provide access to and protection of data; • to manage digital identification data.

To determine the necessary infrastructure

• to identify, select, create, and maintain the necessary infrastructure • to identify needs and challenges, as well as solve problem situations in digital environments

To determine the production conditions

• to identify and take measures to ensure the health and safety of employees • to protect physical and psychological health • to be aware of digital technologies for social well-being and social integration • to be aware of the impact of digital technologies on the environment and their use

Digital skills include information acquisition, online communication, digital content creation, and electronic security (Asaul & Mikhailova, 2018). In practice, this means fixed in the mind and automatically performed actions related to the use of e-mail, text, and computer editors, search engines, the ability to fill out online forms, the ability to edit media files and documents, the ability to customize the program to your requirements, basic programming (The Digital Competence Framework 2.0, 2017). Within the framework of our research, it is important to distinguish the category of “information skills” of personnel. Information skills can be understood as the ability to extract information from other sources and transform it didactically, that is, to interpret and adapt information to the

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particular tasks. In practice, this is important for formulating information needs, searching for and extracting digital data, information, and content. Integrating digital technologies into all elements of HR work requires fundamental changes in technology, culture, operations, and HR management principles. CONCLUSION Digitalization of personnel technologies is a process that largely determines the vector of development of the system of social and labor relations. We have identified the critical need to supplement traditional HR technologies with digitalization elements. It is obvious that the influence of new information factors increases the complexity of work, and requires additional motivation of employees who actively use modern information technologies in their professional activities. Artificial intelligence can serve as a knowledge base, provide recommendations, or perform an advisory function. It is important to develop and adopt the elements of AI that lead to the company’s progress. The use of intelligent human resource management systems is positively correlated with the firm’s market value, productivity, and profit. The analysis of existing approaches and the peculiarities of regional practice of business structures allowed us to form the concept of technologization of personnel work and its impact on the company’s management efficiency. REFERENCES Aptekman, A., Kalabin, V., Klintsov, V., Kusnetsova, E., Kulagin, V., & Yasenovits, I. (2017). Digital Russia: A New Reality. McKinsey. Retrieved from http://www. tadviser.ru/images/c/c2/Digital-Russia-report.pdf Asaul, V. V., & Mikhailova, A. O. (2018). Ensuring information security in the context of the formation of the digital economy. Theory and practice of service: Economy, social sphere, technology, 4(38), 5–9. Atkinson, R. D. (2017, April 21). In defense of robots: National review. Retrieved from http://www.nationalreview.com/article/446933/robots-jobs-industrial-future Berg, J., Furrer, M. Harmon, E., Rani, U., & Silberman, M. S. (2018). Digital labour platforms and the future of work: Towards decent work. Geneva, Switzerland: International Labour Organization. Retrieved from https://www.ilo.org/wcmsp5/ groups/public/—-dgreports/—-dcomm/—-publ/documents/publication/ wcms_645337.pdf Gaponenko A. L., & Pankrukhin A. P. (2003). Management theory [Textbook]. Moscow, Russia: RAGS. Kusakina, O. N., Vorontsova, G. V., Momotova, O. N., Krasnikov, A.V., & Shelkoplyasova G. S. (2019). Using managerial technologies in the conditions of digital economy. Perspectives on the Use of New Information and Communication

192    G. N. MAY-BORODA et al. Technology (ICT) in the Modern Economy: Advances in Intelligent Systems and Computing, 726, 261–269. Pikuleva, O. A. (2017). Digital transformation: New challenges for business and company leaders. Retrieved from http://www.813.ru/files/docs/fast/tsifrovizatsiya/ tsifrovaya-transformatsiya-novye-vyzovy.pdf Starodubtseva, E. B., & Markova, O. M. (2018). Digital transformation of the world economy. AGTU Bulletin, 2. Retrieved from https://cyberleninka.ru/ article/n/tsifrovaya-transformatsiya-mirovoy-ekonomiki/viewer The Digital Competence Framework 2.0. (2017). The European Commission’s Science and Knowledge Service. Retrieved from https://ec.europa.eu/jrc/en/ digcomp/digital-competence-framework Vorontsova, G. V., Dedyukhina, I. F., Kosinova, E. A., Momotova, O. N., Yakovenko, N. N. (2019). Perspectives of development of managerial science in the conditions of information society. In E. Popkova, & V. Ostrovskaya (Eds.), Perspectives on the use of new information and communication technology (ICT) in the modern economy (pp. 980–989). Cham, Switzerland: Springer. https://doi .org/10.1007/978-3-319-90835-9_110

CHAPTER 22

MANAGEMENT OF PROFESSIONAL TRAINING PROCESS AS A KEY FACTOR OF DIGITAL ECONOMY DEVELOPMENT Galina V. Vorontsova North Caucasus Federal University Nadezhda V. Miroshnichenko Stavropol State Agrarian University Ekaterina V. Efimova Pyatigorsk State University Elena V. Baboshina Pyatigorsk State University Irade S. Guseynova Moscow State Pedagogical University

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ABSTRACT In the context of the rapid spread of new digital technologies and competencies, global competition of ideas and images of the future technological gap between the Russian economy and the world leading countries in the competition of the introduction of digital technologies, in particular in the education system, is increasing. This chapter discusses the importance of higher education institutions in the training of highly qualified personnel. The purpose of the study is to find the reasons for the low rates of digitalization in Russian educational systems and to identify possible approaches aimed at the development of digital infrastructure and mechanisms of digital technology management in the educational process in the long term. The main result of the study is the introduction of new models of universities that will have the functions of the transition period. In addition, the prospective directions of development of digitalization in the regional labor markets are investigated. In this regard, the main directions of training of specialists by Russian universities, taking into account the bachelor’s and master’s degree programs, have been fully analyzed.

The digitalization of the economy today is an extremely important issue in all spheres of life—in economic practice, business processes, in state and municipal management, social and cultural fields, and the national economy. Today, the digitalization of the economy is a key issue that concerns global competitiveness and national security. Today the country has adopted a state program, which is aimed at the maintenance, implementation, and development of digital technology, which provides thereby a change in the content of the country’s infrastructure towards digitization. Therefore, the current task of building a digital economy defines the problem of training highly qualified specialists who will have new skills and competencies in various fields, in particular in the field of information technology and economic activities. As a result, the role of universities in the development of the digital economy is only increasing, as it is higher education institutions that are key factors in building professional capacity in the field of business computer and information technology, economic cybernetics and information security, and information technology support in general. METHODOLOGY This study is based on the methodological basis of general scientific, economic, and managerial research. Thus, the scientific work contains a theoretical justification of the analyzed problem; the framework and

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characteristics of the studied object, namely higher educational institutions, and their impact on training. The study includes an analytical part describing the general situation both in the country and in the municipal labor market, competitiveness analysis of universities and their development trends in the conditions of digitalization. The main attention is paid to the critical analysis of the problems of universities, factors affecting the change of certain indicators, the determination of cause-and-effect relationships, and dependencies between various factors and phenomena. Methods of comparative, structurallogical, and systemic analysis were used for the study. RESULTS As mentioned above, today it is necessary to develop digital competencies in all areas of economic activity. And for each of the sectors of the national economy, they should be unique, that is, to respond to the individual requests of an enterprise or organization (Petrova, 2018). In the digital economy, the main asset of the state will be human capital. Not a man in general, but a man who will have competencies in the field of new technologies, as well as be able to explore, introduce new and improve the existing ones. And not even one man, but groups of men who are able to combine and activate the sum of competencies of individuals into a single collective intelligence (Kolesnikov, Epifanova, Usenko, Parshina, & Ostrovskaya, 2016). It is this provision that justifies the emergence of three main audiences in training for the digital economy: creators (people who have a vision of the development of digital technologies), technical specialists, and qualified users. It follows from the above that new skilled personnel in the digital economy require new competencies, that is, skills. That is why universities should be ready to solve this problem. At the moment, the transition to new terminology has already been made, which can reflect significant changes in its content. The challenge today is not to acquire knowledge, but rather to acquire skills—and moreover, to acquire not separate skills, but sets of skills or competencies: hard skills, soft skills, and digital skills, reflecting strong changes, in particular, in the educational sphere. It is worth taking into account that each profession has its own combination of all three groups of skills. The first group of skills—professional skills—they are easily automated and measured, for example, by means of an exam. The second group of skills contains such categories of personal qualities that can be acquired in the process of socialization. It is this type of skills that allows a person to be successful irrespective of a character of his main activity. The third type of skills is digital skills related to total computerization and digitization. It is important to consider in which kind of activities the person,

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who will be taught this set of skills, is engaged. For “non-digital” professions—this is a standard package of competencies that they need as ordinary members of the network digital society (Buyanova, Averina, & Popova, 2018). Today, the constant changes in the skill set that are part of the business models are due to the demand for new skills, which leads to changes in requirements and the emergence of completely new professions. Modern industries in the digital economy need specialists with the skills in the new work environment: • • • • • • • • •

development of mobile Internet and cloud technologies; work with large databases; crowdsourcing, sharing economy and peer-to-peer networks; transition to a green economy; new energy sources and technologies; work in the Internet environment; advanced robotics and autonomous transport; artificial intelligence; and biotechnology and genomics.

The demand for new skills forms the demand for new personnel focused on the digital economy, which should receive the appropriate training in universities (Figure 22.1; http://viperson.ru/articles/kadry-i-obrazovanie -kak-klyuchevye-faktory-razvitiya-tsifrovoy-ekonomiki). According to the analysis of Microsoft and The Future Laboratory, 65% of current students will hold positions that do not yet exist. By 2025, the The economic feasibility of the development and implementation of municipal systems

The demand for specialists in the development and implementation of municipal systems

The need to prepare and separate large amounts of data for municipal systems

The demand on data scientists and experts in the layout of arrays

The emergence of effective generative models

Demand for digital art

The problem of digital star chamber

The emergence of digital advocacy

Identification of potential vulnerabilities in the used models of municipal systems

The demand for specialists in AI security

Figure 22.1  Mechanism of forming a new request for specialists.

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high demand occupations will be: designers of the virtual environment, lawyers on roboethics, freelancing, biohacker, etc. It is noted that apart from technological, demographic, and socioeconomic problems the emergence of new professions will also have a significant impact on the transformation in the field of employment and skills requirements, the speed of creation of appropriate educational networks of training, as well as the role of crosssectoral partnership between universities and employers. All this justifies the need for the formation of a comprehensive strategy in training in order to enhance the skills and competencies that will be in accordance with the trends and development goals of the modern digital revolution (Bobrova, Kuznetsova, Pavlenko, & Bogdanova, 2019). The above factors increase the role of universities, as each level of higher education should train specialists in such a way that after graduation their qualifications should include those skills that are necessary for their posts in the digital economy. The problem of the relationship between the levels of education and the levels of digital training is shown in Figure 22.2 (http://vuzoteka.ru). This relationship is based on the following principle: different categories of citizens correspond to different requests for the digital technologies mastery. Figure  22.2 clearly shows different levels of digital training—from elementary (for ordinary users of digital services) to professional (IT–specialists)

MBA retraining

Professional

Qualification improvement Master’s degree programme

Bachelor’s degree program Secondary special education (colleges) General secondary education (schools)

Centers for social development and digital education

Levels of digital training

Analytic

Levels of education

Postgraduate study

Advanced

Basic

Elementary

Figure 22.2  The relationship between education levels and digital training levels.

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and analytical (specialists who are able to analyze and summarize foreign and domestic experience in the field of digital technologies, as well as to develop recommendations for its use in practice). However, in order to determine what skills are needed for wider use of digital technologies, they can be divided among the following three directions: • general ICT skills for professional tasks: programming, applications development, and network management; • additional ICT skills to meet new challenges related to the use of ICT in work, such as: information processing, self-direction, problem solving, and communication; and • digital literacy skills that ensure the use of digital technologies by all people in everyday life (http://bit.samag.ru/main/part/8). Let us identify the forms of training and levels of digital training, indicating the desired competencies and skills in a particular field of ICT (Table 22.1). TABLE 22.1  Formation of Digital Competences in Various Forms of Education Form of Training

Digital Training Level

Postgraduate Study

Analytical

Search for information, the ability to access it, analysis and synthesis, development of practical recommendations for the application of existing experience

MBA Training

Professional

Skills required for the development, operation, and maintenance of ICT (skills in modern software products, operation, maintenance, management, information architecture design, creation of design, research, and development in the field of ICT).

Advanced Training, Master’s Degree Program

Advanced

Ability to navigate the evolving digital environment, including new software, analytical technologies. Ability to apply digital technologies in practice. Knowledge and skills in ICT ethics.

Bachelor’s Degree Program, Secondary Special Education (colleges), General Secondary (schools)

Basic

Components of digital literacy, basic programming, and algorithmization skills, creation of products and communicative exchange of information in individual or collective work, knowledge of computer technology, the ability to use the web environment.

Population

Elementary

Computer literacy. User: primary skills required to obtain services in the digital environment

Skills ICT

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Systematic training of specialists requires taking into account the most progressive trends in the development of world scientific and technological progress and the world economy, which dictate to transfer not the knowledge we possess today, but the knowledge that will be required in the near future. This is the main task of universities in training. In the context of digitalization, staffing needs an integrated approach that promotes a combination of modern tools for planning and human resource requirements forecasting and opportunities to provide them, as well as rational coordination of efforts of management bodies in the fields of industry and education, enterprises, and population (http://tskopar. ru/index.php/1692-12-dekabrya-2017-g-rekomendatsii-uchastnikov-mezhdunarodnoj-konferentsii-gotovy-li-vuzy-k-roli-klyuchevykh-institutov-dlyarazvitiya-tsifrovoj-ekonomiki-rossii). As a result, firstly, it is necessary to create the network interaction between universities and employers in the form of joint digital economy training programs, as well as providing training and retraining opportunities to the population. In this regard, such master’s programs as “technological entrepreneurship of blockchain technologies” (training field “Management”), “digital transformation of business” (training field “Business Informatics”), as well as additional education in the training fields of “digital economy and digital money,” “legal regulation of the circulation of digital currency,” and others will be relevant (Digital Economy of the Russian Federation, 2017). Secondly, it is necessary to use modern tools for planning and forecasting of staffing requirements, which will allow public authorities: • to monitor personnel situation by collecting information from employers about the current professional-qualification and genderage structure of the workplace and the need for additional staffing support; • identify promising training courses in the fields, which are in particularly high demand by the economy; • to assess the demand and supply of labor, to identify problem areas in a timely manner and to take the necessary actions; and • to predict the need for training of professionals, including in the territorial context, taking into account the existing educational network and the possibilities of its expansion. Thirdly, the modern financial and economic management in the educational organization is also required. Such management should be based on highly qualified personnel, which will be provided with modern IT solutions, which will allow use of employees’ intellectual potential for solving analytical and management tasks.

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Fourthly, it is necessary to improve the tools and methods of management of educational and scientific activities of universities. In addition, there is a need to form the electronic educational environment that will provide the transition of all services to the digital form, the development of the mobile device management platform, and so on. The changes that are taking place in the economy today affect the entire sphere of education, while exerting increasing pressure on the university environment. Higher education diplomas are losing more and more of their value. The fact is that online education is developing rapidly, social and economic systems organize their network corporate system of training and retraining of their specialists together with the development of certain target standards that, as a result, leads to issuing of professional certificates for compliance with them. Among the new educational platforms, the project “University of STI 20.35” deserves special attention. The Agency for Strategic Initiatives (ASI) is launching the online platform “University of STI 20.35” in Russia, which is primarily aimed at training specialists in accordance with the requirements of the digital economy. Graduates of STI university do not obtain state-recognized diplomas, instead they will have a digital profile of competencies that will reflect their achievements upon successful completion of the 6-month program. The main feature of this online platform is the “smart” selection of competencies that is necessary for a particular student in the digital economy (Sharova, 2018). Currently, a number of leading universities in the country have already begun to form an educational environment that can adequately respond to the challenges of the digital economy. For example, Tomsk State University (TSU) has created the Institute of digital age, supported by various scientific and educational areas. In addition, there are units whose main task is to address the issue of big data. But these are only specialized structures. Special role here is played by the fact that the digital aspect is gradually becoming key to the development of educational programs of the university. Tomsk State University is planning to establish the Center for Training in Cyber Security, open the Academy of the Blockchain, and the Center for Digital Rights. By the way, TSU is a partner of the project “University of STI 20.35,” and in this capacity it is ready to work together to address the problems of search and development of talented individuals. In addition, The National University of Science and Technology “MISiS” is one of the participants and developers of the project “Modern Digital Educational Environment.” The university creates and implements educational programs that are directly aimed at training specialists for the digital economy.

Management of Professional Training Process    201

It should be remembered that broad humanitarization is one of the main aspects of training in the context of digitalization. It is this provision that makes the combination of humanitarian and technical potential quite promising, that is, it is possible to unite different universities as partners for the development and implementation of joint educational programs. This assumption is not groundless and can be proved by a real example. MGIMO University and MIPT have created a new MBA-program “digital economy” on the basis of cooperation. This program trains personnel in the field of management at the international level, who are able to deal with digital technologies and, moreover, to implement digital transformation in all spheres of society. It is important to analyze in each region of Russia the professional training for the digital economy by universities located on its territory in terms of the prospects for the development of the regional labor market. CONCLUSIONS From the above, we can conclude that NCFU can be considered as a leader in professional training with a wide range of areas for the digital economy. In addition, it is worth noting a high equipping level of the university that provides possibilities for an effective learning process. Despite this, the Agricultural University can compete with NCFU, since its specialization is agriculture. The Ministry of Economic Development of the Stavropol region discussions are underway regarding the development of agriculture, in terms of the digitalization of agriculture, so modern agricultural education will be in great demand. The South of Russia will need specialists in the field of agriculture. In addition, the digitalization of the Stavropol region is possible in the field of tourism (e.g., the project “Smart Agglomeration of the Caucasus Mineral Waters”). That is why in the conditions of digital economy the employers of the Stavropol region will be in search of personnel in three areas: IT, agriculture, and tourism. Today, employers often suffer from shortages of skilled software specialists, which would be able to create a corporate system of access and management, would have basic skills of using and transferring information data. Unfortunately, not every specialist today is able to work at a high level in the field of information systems (Ianova, & Yanova, 2015). Thus, we can conclude that the shortage of personnel in the field of ITtechnologies is already felt today, although the pace of digitalization has only begun to grow. That is why the leading role of training professional for the digital economy has been assigned to higher education institutions, which should develop appropriate knowledge and competences that would be useful in the conditions of implementation of digital technologies.

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REFERENCES Bobrova, T. O., Kuznetsova, E. N., Pavlenko, V .G., & Bogdanova, O. G. (2019). The Cognitive Scenario of the Concept “Economy.” Advances in Intelligent Systems and Computing, 726, 141–150. Buyanova, M. E., Averina I. S., & Popova Y. G. (2018). Model of the institutional mechanism of stimulation of innovative activity of the region: process approach. Science Journal of VolSU: Global Economic System, 20(4), 15–24. Digital Economy of the Russian Federation. (2017). Order of the Government of the Russian Federation No. 1632-p dated 28.07.2017. “Garant” law reference system. Retrieved from https://base.garant.ru/71734878/ Ianova V. V., & Yanova E. A. (2015). Current state and prospects of development of higher educational institutions of Russia. Scientific Journal of KubSAU, 111(07). Retrieved from http://ej.kubagro.ru/2015/07/pdf/80.pdf Kolesnikov, Y. A., Epifanova, T. V., Usenko, A. M., Parshina, E., & Ostrovskaya, V. N. (2016). The peculiarities of state regulation of innovation activities of enterprises in the global economy. Contemporary Economics, 10(4), 343–352. Petrova E. A. (2018). Living standards control in the region: modeling of factors of external and internal environment. Science Journal of VolSU. Global Economic System, 20(1), 30–39. Sharova, A. (2018). Instead of personnel for the digital economy, Russia is preparing “Tanchiki” professional gamers. The Official website of the news Agency REGNUM. Retrieved from https://regnum.ru/news/2311320.html

CHAPTER 23

THE MECHANISM OF ADAPTATION OF THE EDUCATIONAL AND LABOR MARKETS TO THE MEETING OF HUMAN INTELLIGENCE AND ARTIFICIAL INTELLIGENCE Svetlana V. Lobova Altai State University Aleksei V. Bogoviz National Research University Tatiana V. Aleksashina Russian University of Transport

Meta-Scientific Study of Artificial Intelligence, pages 203–209 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

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ABSTRACT The purpose of the chapter is to develop the mechanism of adapting the educational market and labor market to the meeting of human intelligence (HI) and artificial intelligence (AI). Complex research of the consequences of the meeting of HI and AI for the educational market and labor market and development of recommendations for their adaptation are conducted according to the systemic approach with applications of the methods of logical, structural and functional, and scenario analysis, as well as synthesis, modeling, and formalization. The performed analysis of distribution of functions of HI and AI in various business processes as to the level of their automatization determined the tendency of unification of the functions of HI with increase of the level of automatization of business processes. This allows for mass training of personnel for the digital economy with their further practical specialization at work.

Artificial intelligence (AI) is proclaimed to be one of the breakthrough digital technologies in national strategies of formation of the digital economy of modern countries of the world. It is one of the most expected innovations in the conditions of transition to Industry 4.0, as it possesses the following advantages. Firstly, the capability of processing Big Data and the fullest accounting of the accessible information during decision-making. Secondly, high speed of decision-making. Efficiency of AI is hundred times as high as efficiency of human efficiency, which allows for almost instantaneous decision-making. Thirdly, high precision and rationality of decisions. As management of social risks of technological progress is traditionally conducted on the basis of the educational market and labor market, a current scientific and practical problem, which predetermined the purpose of this work, is development of a mechanism of adaptation of the educational market and labor market to the meeting of human intelligence (HI) and AI. METHODOLOGY The performed literature overview on the selected topic showed that modern scholars agree on the uniqueness and irreplaceability of HI and AI’s incapability to fully replace it. At the same time, Bogoviz (2019), Ford (2013), Popkova (2019), Popkova and Sergi (2019), Popkova, Ragulina, and Bogoviz (2019), and Sukhodolov, Popkova, and Litvinova (2018) note that AI may be more competitive in a lot of business processes. Such experts as Edwards, Edwards, Stoll, Lin, and Massey (2019), Krkač (2018), and van der Vecht, van Diggelen, Peeters, Barnhoorn, and van der Waa (2018) state that maintenance and interaction with AI require

Mechanism of Adaptation of the Educational and Labor Markets    205

completely new professions (e.g., specialist on technical maintenance and training). Such scholars as Abbass (2019), Boyd and Holton (2018), Chitty and Dias (2018), and Ozawa (2018) emphasize the transformation processes in the labor market, which are connected to dissemination of AI— growth of unemployment rate in certain professions and growth of demand for other professions. A new approach to selection of personnel on the basis of AI (according to clearly formalized characteristics of job seekers without consideration of social and personal characteristics) will create a demand for the corresponding educational services. Modernization of entrepreneurial activities and consumption in view of new opportunities that open due to AI and the necessity to interact with them will lead to creation of additional demand for specific educational services. RESULTS The change of the ratio of functions of HI and AI with increase of the level of automatization of various business processes is shown in Table 23.1. The level of automatization depends on the level of development of AI (stages of its dissemination) and limitations of its usage (scenarios). As is seen from Table 23.1, in case of the initial (low) level of automatization, the functions of AI in R&D are limited by processing of big data. With the medium level of automatization these functions include specification of ideas that are offered by HI and their comparison according to the set criteria. In case of high level of automatization, AI performed all production functions—promotion of new ideas, their specification, and selection and development of optimal ideas—however, in view of high complexity and creative direction, we think that high level of automatization of R&D will not be achieved even in the long-term (30 years). Collection of orders from people will require complex communicative skills that are not accessible for AI, and collection of machines (automatic, without human participation) could be conducted by AI. That’s why high levels of automatization of production could be achieved in the long-term (30 years) under the condition of the high level of automatization of consumption. As is seen, in all business processes increase of the level of their automatization leads to reduction of human functions, which are eventually brought down to technical maintenance, control over work, and teaching AI. Thus, after the meeting of HI and AI, a general tendency in all professions should be domination of the technical component. Thus, it is necessary to train digital personnel that are capable of mastering the leading technologies in all business processes. Based on the obtained conclusions, we developed

206    S. V. LOBOVA, A. V. BOGOVIZ, and T. V. ALEKSASHINA TABLE 23.1  Distribution of the Functions of HI and AI in Various Business Processes According to the Level of Their Automatization Level of Automatization

R&D

Production

Distribution

Functions of HI

promotion of new ideas, their detalization, selection, and development of optimal ideas

planning and organization of the production process

collection of orders from consumers, processing of orders, control over their execution, and informing consumers on readiness of orders

Functions of AI

Big Data processing

intellectual support for decision-making

Functions of HI

promotion of new ideas, selection and development of optimal ideas

planning of production process

collection of orders from consumers

Functions of AI

specification of ideas, comparison of ideas according to the set criteria

organization of production process

processing orders, tracking their execution, and informing consumers on readiness of orders

Functions of HI

technical maintenance, control over work and teaching of AI

Functions of AI

promotion of new ideas, their specification, selection and development of optimal ideas

Low Medium High

Business Process

planning and organization of the production process

collection of orders from consumers, processing of orders, control over their execution, and informing consumers on readiness of orders

and recommended for practical application the following mechanism of adaptation of the educational market and labor market to the meeting of HI and AI (Figure 23.1). As is seen from Figure 23.1, the offered mechanism seeks the goal of managing the social risks of the meeting of HI and AI. It should be noted that adaptation of the educational market and labor market is a tool of achieving the goal (not goal in itself) and is conducted in five consecutive stages. At the first stage, the state in the form of a special expert committee forecasts the consequences of Industry 4.0 for spheres of economy in view of professions. It is recommended to use the determined distribution of functions of HI and AI in various business processes according to the level of their automatization as the foundation (Table 23.1).

Mechanism of Adaptation of the Educational and Labor Markets    207 Goal: managing social risks of the meeting of human intelligence and AI

▻ formation of the register of labor market of the future and stimulation for employment of new personnel

▻ forecasting of consequences of Industry 4.0 for the spheres of economy in view of professions

1. State

5. Employment service ▻ promotion of new educational programs (marketing, target training, state orders)

2. Universities

Tool: adaptation of educational market and labor market

4. Employment service, companies, state

3. Universities

▻ determining the perspective needs for educational services ▻ development of new educational programs

Result: stable planned adaptation of entrepreneurs, consumers, and employees to meeting with human intelligence and AI

Figure 23.1  The mechanism of adaptation of the educational market and labor market to the meeting of HI and AI.

At the second stage, universities determine the prospective needs for educational services. These needs could be formulated in the generalized way as studying the structure of AI, skills of teaching AI, organization of entrepreneurial activities on the basis of AI, and increase of digital literacy of population on the basis of usage of AI in the process of purchase and consumption of products. At the third stage, universities develop new educational programs for satisfying the determined needs within higher education, advanced training, digital literacy courses, and so on. At the fourth stage, new educational programs are promoted. The employment service conducts marketing, providing information and consultation support on professional retraining for the unemployed. The companies that conduct or plan digital modernization on the basis of AI provide (and finance) target training for their employees for the purpose of preparation of digital personnel, which they will require as a result of modernization, and of softening the consequences of reduction of personnel after automatization

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(stimulation of employment at other companies and in other professions). The state places an order for preparation of digital personnel. At the fifth stage, the employment service forms the register of the labor market of the future and stimulates the employment of new personnel. Then the first stage is repeated, and a new cycle is started. As a result, stable planned (crisis-free) adaptation of entrepreneurs, consumers, and employees to meeting the standards of HI and AI is achieved. CONCLUSIONS Thus, the developed mechanism of adaptation of the educational market and labor market to the meeting of HI and AI showed the necessity for involving not only universities and employment services but also the state and companies that conduct or plan digital modernization into this process. The mechanism also showed that adaptation should be conducted not simultaneously but regularly, in the course of increasing the level of automatization of business processes in the economy. The performed analysis of distribution of functions of HI and AI in various business processes according to the level of their automatization showed a tendency of unification of the functions of HI in the course of increase of the level of automatization of business processes. This allowed for mass preparation of personnel for the digital economy with their further practical specialization at work. REFERENCES Abbass, H. A. (2019). Social Integration of AI: Functions, Automation Allocation Logic, and Human-Autonomy Trust. Cognitive Computation, 2(1), 24–39. Bogoviz, A. V. (2019). Industry 4.0 as a new vector of growth and development of knowledge economy. Studies in Systems, Decision, and Control, 169, 85–91. Boyd, R., & Holton, R.J. (2018). Technology, innovation, employment, and power: Does robotics and AI really mean social transformation? Journal of Sociology, 54(3), 331–345. Chitty, N., & Dias, S. (2018). AI, soft power and social transformation. Journal of Content, Community, and Communication, 7, 1–14. Edwards, C., Edwards, A., Stoll, B., Lin, X., & Massey, N. (2019). Evaluations of an AI instructor’s voice: Social identity theory in human-robot interactions. Computers in Human Behavior, 90, 357–362. Ford, M. (2013). Could artificial intelligence create an unemployment crisis? Communications of the ACM, 56(7), 37–39. Krkač, K. (2018). Corporate social irresponsibility: Humans vs artificial intelligence. Social Responsibility Journal, 2(1), 18–27.

Mechanism of Adaptation of the Educational and Labor Markets    209 Ozawa, H. (2018). Developing artificial intelligence services that satisfy customer demands: Moving forward with social implementation of corevo® technologies. NTT Technical Review, 16(8), 7–11. Popkova, E. G. (2019). Preconditions of formation and development of industry 4.0 in the conditions of knowledge economy. Studies in Systems, Decision, and Control, 169, 65–72. Popkova, E. G., Ragulina, Y. V., & Bogoviz, A. V. (2019). Fundamental differences of transition to industry 4.0 from previous industrial revolutions. Studies in Systems, Decision, and Control, 169, 21–29. Popkova, E. G., & Sergi, B. S. (2019). Will industry 4.0 and other innovations impact Russia’s development? Exploring the future of Russia’s economy and markets. Bingley, England: Emerald. Sukhodolov, A. P., Popkova, E. G., & Litvinova, T. N. (2018). Models of modern information economy: Conceptual contradictions and practical examples. Bingley, England: Emerald. Van der Vecht, B., van Diggelen, J., Peeters, M., Barnhoorn, J., & van der Waa, J. (2018). Sail: A social artificial intelligence layer for human-machine teaming. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10978 LNAI, 262–274.

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CHAPTER 24

THE PROBLEMS OF EMPLOYMENT AND UNEMPLOYMENT ON REGIONAL LABOR MARKETS IN THE DIGITAL ECONOMY Sergey M. Gorlov North Caucasus Federal University Inna N. Kazakova Stavropol Institute of Cooperation (branch) BUKEP Sofiya G. Kilinkarova Pyatigorsk State University Elena V. Sharunova North Caucasus Federal University Evgeny A. Shevchenko Stavropol State Agrarian University

Meta-Scientific Study of Artificial Intelligence, pages 211–221 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

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ABSTRACT The chapter presents an analysis of the current global economic and technological trends in the development of the modern economy, which affect the change of regional labor markets. To a large extent, these changes are reflected in the emergence of new needs for workers with the necessary competencies in the field of information and computer technology, and this in turn is reflected in the model of career behavior of employees. These changes are largely related to digitalization and economic growth. This affects the decision-making of entrepreneurs in the development of internal personnel policy, including the issues related to the selection, promotion, training, and retraining of employees. The aim of the study is to identify trends in the development of regional Russian labor markets under the influence of actively implemented digitalization of the economy. The main result of the study is to identify the impact of digitalization of the economy on the qualitative and quantitative changes taking place in the regional Russian labor markets. The study concludes that it is necessary to predict the impact of digitalization of the country’s economy on regional labor markets in order to develop a competent state personnel policy that can overcome the possible negative social consequences in the form of the release of labor, reducing demand for certain specialties in the labor market. This will allow us to take advantage of the digital economy, neutralizing the possible social consequences of job cuts.

The analysis of actual economic and technological directions of economic development is very important in modern conditions. They, in particular, affect the change in the labor market, the emergence of new needs for workers in certain professions and with competencies in the digital economy, which in turn is reflected in the model of career behavior of workers who try to meet the changing requirements of employers. The ongoing technological changes have a radical impact on social relations in the society. Serious transformations are taking place in the regional labor markets due to the digitalization of the economy, which, in turn, affects the decision-making process of entrepreneurs in the formation of personnel policy and its elements related to the training and skills development of workers to ensure their compliance with the requirements of the digital economy. METHODOLOGY Trends in the development of regional labor markets under the influence of the ongoing digitalization of the economy led to the use of research methods such as system analysis, content analysis, expert evaluation method, comparative analysis, as well as the establishment of cause–effect

Problems of Employment and Unemployment on Regional Labor Markets    213

relationships and interdependencies between various factors and phenomena that directly affect the formation of digital society and the corresponding transformation of the educational field. On this basis, the key competencies and skills that should be formed at universities at a particular level of training in accordance with the qualifications. Also the digital trends in the economy that require changes in the process of training, self-training, and self-development of specialists in the labor market are considered. RESULTS The current stage of development of the Russian economy corresponds to the trends of the fourth industrial revolution, which is characterized by digitalization of all spheres of the economy, leading to the emergence and practical use of such new technologies as artificial intelligence, big data technologies, blockchain, and robotics (Muravyeva & Ulanov, 2019). Digital computer technologies, the Internet, are widely used in all sectors of the Russian economy, work with information in most companies is automated. On the one hand, these processes are driving productivity growth in the industries where digital technologies are most prevalent, reducing the lag behind economically developed countries. On the other hand, the same processes contribute to the release of labor, the abandonment of a range of professions, which has a negative impact on social relations in the society. The current stage of economic development allows us to characterize it as a knowledge economy, in which information and human capital become the main resources, and in which digital technologies become the leading ones. That, of course, puts the question of the transition to the fifth and sixth technological structures on the agenda (Polevanov, 2017). One of the directions of the digital economy development up to 2024 is human resources. Thus, a number of educational and professional programs have already been developed that prepare specialists who comply with the needs of the digital economy with the necessary competencies in this field (Vorontsova, Momotova, Yakovenko, Dedyukhina, & Kosinova, 2019). The introduction of digitalization has a significant impact not only on economic, industrial, but also on social relations, on changing the behavior of the employed population. One of the signs of such a change is the emergence of so-called electronic nomads, that is, professionals who are confident in information and communication technologies, actively work in the virtual space, they are not confined to any office, and can carry out their work remotely from anywhere in the world where there is access to the Internet (Morozov & Morozova, 2018). Due to automation, digitalization, and strengthening of the role of information technologies, there are significant structural changes in many

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sectors of the economy. To use business processes in IT, they have to be improved and transformed. This will increase the speed of various business operations, create new information channels, and simplify a variety of operations used for the development, adoption, and implementation of products. The introduction of tools and methods of data processing increases the importance of information as a key resource (Kusakina, Vorontsova, Momtova, Krasnikov, & Shelkoplyasova, 2019). The development and implementation of technologies to ensure the competitiveness of employees and the possibility of sustainable career growth of specialists, based on the comprehensive development of technological support of labor and human capital, should be based on the principle of consistency (Kashepov, 2018). At the same time, there are a number of objective barriers limiting the potential of the use of human capital development technologies of organizations, overcoming which is a key task in the construction of a new effective paradigm focused on human capital: • high level of mobility and staff turnover; • high competition between large corporate structures; • the presence of information imbalance between the agents of the labor market; and • time and resource limitation. The development and implementation of technologies to ensure the competitiveness of employees and the possibility of sustainable career growth of specialists, based on the comprehensive development of technological support of labor and human capital, should be based on the principle of consistency in order to minimize the impact of these barriers in organizations (Khusyainov, 2017). We consider the dynamics of personnel processes in one of the most important regional labor markets in the South of Russia—the labor market of the capital of the Stavropol region, the flagship of the economy of the North Caucasus Federal district—Stavropol and the region in general. Thus, the tension coefficient in the labor market of Stavropol by the end of 2017 was 0.3. This is one of the lowest indicators in the region (for the same period it was in Pyatigorsk 0.1, in Budennovsk 0.2; https://rosstat.gov .ru/folder/10705). After analyzing the data of official statistics, it can be concluded that there is a favorable trend in the labor market of the Stavropol region to some decrease in the total number of unemployed. To achieve this, the Government of the Stavropol region is making some efforts, creating new jobs, stimulating the development of less popular sectors of the economy.

Problems of Employment and Unemployment on Regional Labor Markets    215

As a negative trend in the labor market of the Stavropol region it should be noted the decline in real disposable income of the population of the region by 1.1% by the end of 2017. At the same time, it is impossible not to note the positive trends: • growth of gross regional product (GRP) of the Stavropol region by 2.4%; • increase in investments in fixed capital for the development of the economy and social sphere of the Stavropol region by 11.7%; • growth of per capita income of the population of the Stavropol region by 3.2%; • growth of real wages per employee by 2.1%; and • increase in the number of jobs created within the framework of Federal target, regional programs, and investment projects by 20.9%. The decline in the number of economically active population of the Stavropol region in 2017 has led to an absolute decline in the number of employed in the economy, but not to the reduction of their share (Figure 24.1). The predominant part of the employed population of the Stavropol region is concentrated in enterprises and organizations. In December 2017, they employed 621.8 thousand people (48.7% of total employment), which is 7.4% less than it was in 2016. The trend of reallocation of jobs is preserved for the majority of economic activities. 1,377

1,377.5

Population (in thousands)

1,351.8 1,299

2015

1,299.2

2016

Economically active population

1,281.6

2017 Employed in the economy

Figure 24.1  Correlation of the economically active population and the population employed in the economy (population, in thousands).

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868.2 279.4

12.4

58.4

23.6

State and municipal Property of public and religious organizations Mixed Russian Foreign, joint Russian and foreign Private

Figure 24.2  Distribution of the population employed in the economy by sectors of the economy in the stavropol region (population, in thousands)

The decrease in the number of jobs was observed in such economic areas as catering (14.2%), mining (6.1%), in the field of information and communications (5.8%), manufacturing (4.9%), construction (4.2%), agriculture, forestry, hunting, fish farming (3.0%), and education (1.9%). The number of jobs in the following economic areas increased: financial and insurance activities (76.4%), real estate (20.7%), in the field of culture, sports, leisure, and entertainment (14.4%). In 2017, there was an increase in the number of employees in all forms of ownership, except for the property of public and religious organizations. The predominant form of ownership in the region is private property (Figure 24.2; https://rosstat.gov.ru/folder/10705). Analyzing the number of employed in the economy of the region, it is necessary to highlight the following negative trends: • reduction of the number of employed in the economy of the region by 1.4%; • reduction in the number of jobs in the organizations of the region by 7.4%. At the same time, it is necessary to focus on positive trends: • decrease in the share of employees who performed work under civil contracts, compared to 2016 by 4.4%; and • increase in the number of employees in organizations belonging to the state, municipal, private, mixed Russian, foreign, joint Russian and foreign forms of ownership (Lebedeva, 2013).

Problems of Employment and Unemployment on Regional Labor Markets    217 78.3

77.6

Population (in thousands)

70.2

15.6

2015 Number of unemployed by ILO

14.4

2016

11.9

2017 Number of registered unemployed

Figure 24.3  Correlation of the number of unemployed according to the ILO methodology and registered in employment institutions (population, in thousands)

It should be noted that the number of unemployed determined by the method of the International Labour Organization (ILO) significantly exceeds the number of officially registered unemployed in the region for the period 2015–2017 (Figure 24.3; https://rosstat.gov.ru/folder/10705). As of January 1, 2018, the highest level of registered unemployment was in the following areas and urban districts of the Stavropol region: Arzgirskiy (3.3%), Stepnovskiy (2.6%), Kurskoy (2.3%), Trunovskiy (2.0%), and Grachevskiy and Ipatovskiy (1.7%). Minimum number is in the resort cities of the Caucasian Mineral Waters: Pyatigorsk and Essentuki (0.4%), Zheleznovodsk and Kislovodsk (0.5%), Budennovskiy district and Izobilnenskiy Urban District (0.4%). We note the negative trends in the development of the regional labor market due to the influence of digitalization of the economy: • high unemployment rate among rural residents (53.0% of the total number of unemployed); • high proportion of unemployed persons with higher and secondary vocational education, 30.3% and 34.7%, respectively. Positive trends in the labor market of the Stavropol region in the conditions of digital economy formation: • reduction of the unemployment rate, calculated according to the ILO methodology, by 8.8%;

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• reduction of the number of unemployed registered in employment institutions of the region by 16.8% and the level of registered unemployment by 10.0%.

Population (in thousands)

The forecast of the employers’ needs of the region in workers and specialists in the surveyed organizations for 2018 for the main types of economic activity showed that the greatest need for personnel will be experienced by health organizations (18.2%); agriculture (16.9%); education (13.5%); wholesale and retail trade (7.8%); transport and communications, construction, and financial activities (4.8%); recreation and entertainment, and culture and sports (2.4%); and provision of other public, social, and personal services (1.6%). The dynamics of changes in the forecast demand for personnel by 2024 shows a decrease in the need for personnel in financial activities by 36.9%, education by 30.9%, organizations engaged in operations with real estate, rental, and provision of services by 21.8%, manufacturing by 21.0%, agriculture by 16.4%. There will be an increase in the need for human resources in such economic activities as wholesale and retail trade by 6.7%, the production and distribution of electricity, gas and water by 9.9%, health by 13.4% and the provision of other utilities by 98.6%. In general, in the long term, stable demand for labor is expected in the region (Figure 24.4; https://rosstat.gov.ru/folder/10705). For certain economic areas, such as wholesale and retail trade, production and distribution of electricity, gas, and water, health care and other public services, it is projected to increase the need for personnel while reducing demand in financial activities, education, organizations engaged in

120.6

2018

107.6

104.2

103.3

104.4

104.6

107.8

2019

2020

2021

2022

2023

2024

Figure 24.4  Demand for personnel of the economy of the region by year (population, in thousands).

Rate of Unemployed Per One Vacancy

Problems of Employment and Unemployment on Regional Labor Markets    219 2.31

1.15

1.11 0.92

0.90 0.70 0.49

2011

2012

2013

2014

2015

2016

2017

Figure 24.5  The rate of unemployed per one vacancy declared in the regional institutions of employment by employers (the coefficient of tension in the labor market).

transactions with real estate, rental, and provision of services, manufacturing and agriculture (Shatokhina, & Pitka, 2012). It is expected to continue the active work of employment services on the proposal of vacancies for unemployed citizens of the Stavropol region (Figure 24.5; https://rosstat.gov.ru/folder/10705). In terms of the value of the tension coefficient on the labor market in the Stavropol region, it holds the 25th position in the rating of constituent entities of the Russian Federation. The highest rate of tension was observed in rural areas due to the decrease in the number of vacancies. So, in the Kurskoy area this indicator was at the level of 28.4 per person for the one vacant position; in Stepnovskiy, 13.2; in Novoselitsiy, 8.9; and in Arzgirskiy, 8.6. The minimum coefficient of tension in the labor market is observed in the cities of Pyatigorsk (0.1 of unoccupied for one vacancy), Stavropol (0.2), Kislovodsk (0.3), Mineralovodsk Urban District (0.3), and Shpakovsky District (0.3). It is necessary to focus on the negative trends in the labor market of the Stavropol region: • there are problems of recruiting personnel into such activities as industry, construction, housing, and utilities sector; • continuing shortage of unskilled workers; and • high coefficient of tension in rural areas.

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At the same time, the positive trends identified by the authors in the studied regional labor market inspire optimism: • reducing the imbalance between labor supply and demand and • increased staff demands for some definite economic activities, such as manufacturing, health care and social services, education, wholesale and retail trade, repair of motor vehicles and motorcycles. CONCLUSIONS Thus, due to the digitalization of the economy, there have been significant changes in the labor market. The trend of redistribution of jobs in most economic activities has spread. The number of jobs has increased in financial and insurance activities (76.4%), real estate operations (20.7 %), activities in the field of culture, sports, leisure, and entertainment (14.4%). The dynamics of changes in the forecast demand for personnel by 2024 shows a decrease in the need for personnel in financial activities by 36.9%, in education by 30.9 %, in organizations engaged in operations with real estate, rental and provision of services by 21.8%, in manufacturing by 21.0%. This indicates a broad transition to digitalization of the Russian economy. The most demanded professions in the labor market of the region are: manager, accountant, WEB-designer, programmer, makeup artist, dress cutter, tailor, salesman, HR specialist, pastry chef, nurse, boiler operator, computer operator, manicurist, hairdresser, driver, electric and gas welder, electrician, and tractor driver. Thus, a competent social policy will allow the Russian economy to take advantage of the benefits provided by digitalization, and to minimize its negative consequences such as reduced employment, rising unemployment, and the resulting social tension. REFERENCES Kashepov, A. V. (2018). Transformation of employment in the digital economy.” Bulletin of the Russian New University, 2, 11–17. Retrieved from http://vestnik -rosnou.ru/ human-and-society/2018/2/11 Khusyainov, T. M. (2017). Identity of self-employed internet workers. Bulletin of Nizhny Novgorod University, 1(45), 127–132. Kusakina, O. N., Vorontsova, G. V., Momotova, O. N., Krasnikov, A. V., & Shelkoplyasova, G. S. (2019). Using managerial technologies in the conditions of digital economy. Advances in Intelligent Systems and Computing Journal, 726, 261–268.

Problems of Employment and Unemployment on Regional Labor Markets    221 Lebedeva, N. N. (2013). Modernization of the mechanism of reproduction of scientific personnel in modern Russia. Bulletin of the Volzhski State University, 14, 14–19. Morozov, M. A., & Morozova, N. S. (2018). Development of digital service technology and its impact on the labor market. Service Journal, 1(12), 100–107. Muravyeva, N. V., & Ulanov, E. A. (2019). Digital economy and labor market. Journal on Economics and Management: Problems and solutions, 4(3), 108–114. Polevanov, V. (2017). Where are you going? Economic Strategy Journal, 1, 82–98. Shatokhina, N. V., & Pitka, S. N. (2012). Reference space of students in the sociological dimension. Bulletin of Tambov State University, 2(106), 289–293. Vorontsova, G. V., Momotova, O. N., Yakovenko, N. N., Dedyukhina, F. I., & Kosinova, E. A. (2019). Perspectives in development of managerial science in the conditions of information society. Advances in Intelligent Systems and Computing Journal, 726, 980–988.

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CHAPTER 25

PROSPECTS FOR TECHNOLOGICAL GROWTH OF RUSSIA IN TERMS OF DIGITALIZATION OF THE ECONOMY Galina V. Vorontsova North Caucasus Federal University Olga N. Kusakina Stavropol State Agrarian University Nikolai V. Eremenko Stavropol State Agrarian University Viktoriya M. Vlasova St. Petersburg State University of Aerospace Instrumentation Sergey I. Lugovskoy Stavropol State Agrarian University

Meta-Scientific Study of Artificial Intelligence, pages 223–232 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

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ABSTRACT The role of technological progress in the processes of globalization at the present stage is justified; the positive and negative aspects of the intensification of scientific and technological development of Russia and the prospects of technological growth of the country’s economy are analyzed. The aim of the study is to identify ways to reduce the technological gap between Russia and developed countries. The main result of the study is the introduction of new models of economic development. Besides, prospective directions of development of digitalization of the sphere of scientific and technological development of the country are investigated. In this regard, the basic principles and directions of development of state policy in the field of technological development are considered. The conclusion is made about the effectiveness of competent management of the processes of scientific and technological development, both within the world economy and the Russian economy.

There is no doubt that science and technology are an essential resource for addressing socioeconomic challenges and enhancing the country’s competitiveness. Intensive development of the country is possible only with the intensification of its scientific and technological sphere. Currently, the Russian Federation faces serious challenges to improve the competitiveness of the economy, the solution of which will allow the country to take its rightful place in the economic, political, and other spheres of life of the modern world. Unfortunately, the rate of technological growth in Russia in recent years has been much lower than the level achieved by developed countries. The problem is that in recent decades there has been a loss of some part of the accumulated potential (physical and intellectual), which could become the basis for modern scientific and technological developments. METHODOLOGY Currently in Russia, as throughout the world, is an active and rapid transformation of most systems and spheres of society. Including the digitalization of the economy, which leads to its change by reducing the costs of part of the production processes associated with storage, data processing, reduction of production chains, and so on. These changes require continuous improvement, both of workers who must meet the level of development of modern technologies and have the skills to work with them, and the technologies themselves, which will become more complex as science develops. It should also be noted that this relationship works in the opposite direction: after all, with the development of new technologies, the composition of the digital economy will also be constantly improved and supplemented.

Technological Growth of Russia in Terms of Digitalization of the Economy    225

RESULTS According to the World Bank, the impact of digitalization on economic growth is carried out through inclusion, involvement of more citizens in social processes, development of innovations. All these processes have a positive impact on the dynamics of capital, trade, labor, and competition ((Mayorova, Panasenko, Nikishin, Ivanov, & Mayorova, 2018). The digital transformation of Russia’s systems can help it to enter the club of global leaders, because the processes that are taking place now are comparable in scale and importance to the industrial revolution of the 18th and 19th centuries, which determined the current leading countries in the field of technological growth, investment of finance in modern technologies (Vorontsova, Ligidov, Nalchadzhi, Podkolzina, & Chepurko, 2019). Despite the rapid development of the digital economy in Russia, the total volume of which increased by 59% from 2011 to 2017 and reached 3% of GDP, there is still a significant gap with the digital leaders in the level of technological development (Titov, 2018). The Russian information and communication technology (ICT) sector has a critically small number of enterprises. As of 2017, 1,000 people accounted for only 0.8 of enterprises in the ICT sector. In developed countries, the number of enterprises per 1,000 people is on average 2.7, which is three times more than in Russia. Germany, which has 1.3 enterprises per 1,000 people, is making serious efforts to improve the situation: In recent years, the number of enterprises has decreased by more than 40%. Since technological and innovative enterprises are the driver of the digital economy, their small number in Russia poses serious risks for the country’s digital development (Figure 25.1; Titov, 2018). There has been a failure to fully realize the potential of development due to the poor base in the digital sector of Russia, although in 2010–2014 its growth was faster than the growth of the economy and averaged 4.8% compared to 2.9% of the economy (Matveev, 2017). Thus, the U.S. digital TABLE 25.1  Comparative Characteristics of the Level of Development of the Digital Economy in Russia and Other Countries Indicator (%), 2017

Russia

United States

Germany

Britain

The proportion of digital sector to GDP

3.0

5.4

6.3

7.1

Proportion of population employed in the ICT sector

1.7

3.1

2.9

3.5

Value added per employee, thousand U.S. dollars.

75

170

132

163

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Number of Enterprises (per 1,000 population)

4.8

3.6 3.3

3.1

3.1 2.7 2.4

2.4 2.1

2.0 1.7

1.7

1.6 1.3

Russia

Germany

Finland

France

Italy

Lithuania

Poland

Denmark

Norway

Great Britain

Latvia

Estonia

Hungary

Czechia

Netherlands

Sweden

0.8

Figure 25.1  Number of enterprises in the ICT sector per 1,000 population, units.

economy sector is significantly ahead of Russia in absolute terms, although previously their development was at a comparable level. The following factors can contribute to the reduction of the technological gap between Russia and the developed countries of the West: • improving the competitiveness of the industry through the introduction and development of breakthrough business models and technologies; • increasing transparency of the process of interaction with the state, which will result in improving the business climate and increasing the availability of public services; • state financing of the education field, professional training in digital skills, support programs for the employees under release; • development of common standards for the use of digital technologies; • creation of favorable conditions for pilot projects, which will help to identify and consider the most promising ones; and • stimulating public interest in innovation and development of digital culture (Kusakina, Vorontsova, Momotova, Krasnikov, & Shelkoplyasova, 2019). In our opinion, the weak point of Russia’s scientific and technological development is insufficiently effective state scientific and technological policy (Momotova, Belokon, Kilinkarova, Mayboroda, & Stroi, 2019). We

Technological Growth of Russia in Terms of Digitalization of the Economy    227

think it would be an important step in this direction, to develop a system of integrated assessment of scientific and technological policy, which can be based on the definition of complex indices allowing the evaluation of the level of development of the innovative potential of the country and individual industries and their innovative ecosystems. It should be noted that the rate of economic growth within the world economic system does not depend on the scale of expenditure on research and development so heavily as it might seem at first glance. Thus, since 1996 and over the next 10 years, the growth of the world economic system fluctuated at the level of 1.5–4%, with the amount of research costs ranging from 2 to 2.15%. The growth rate of Russia at that time exceeded 9%, but the share of costs was not higher than 1% at such economic growth rates. In Germany and Japan, the economic growth rate was quite modest, about 0.4–2.5%, but the development costs were consistently 2.5–3% of GDP, regardless of the growth rate (Mikhailova, 2013). The direction of scientific and technological development can be associated with • increasing the share of expenditure in the structure of GDP for research and development, which should be accompanied by improvements in the quality of research in the education sphere; • the expansion of technology exports in specific research and production niches that certain states have managed to occupy; and • the transformation of these niches, when their number increases, and the boundaries become more blurred. For effective further growth, it is important to establish forms of interaction between science and education, on the one hand, and the economy, on the other, because these forms determine the dynamics of new knowledge creation and their use for new products and services development, which determines the pace of economic growth. It seems clear that the amount of R&D expenditure in itself does not guarantee a high rate of growth and in some cases does not even contribute to it. In 2016, the strategy of scientific and technological development of the Russian Federation was prepared, which determines the responses of the Russian economy to the great challenges of modern economic development. Big challenges is a set of problems, threats and opportunities that objectively require actions from the state, the complexity and scale of which are such that they cannot be solved, eliminated or implemented exclusively by increasing resources. (Strategy of Scientific and Technological Development of the Russian Federation, 2016, para. 21)

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It is necessary to highlight the most significant big challenges to the scientific and technological development of Russia: • the exhaustion of opportunities for economic growth in Russia; • the demographic transition due to the increase in life expectancy of people and changes in their way of life; and • the new external threats to national security. Among the priorities of scientific and technological development of the Russian Federation, the transition to advanced digital technologies, resource-saving energy, personalized medicine, countering man-made, biogenic, sociocultural threats and the connectivity of the territory of Russia through the creation of intelligent transport and telecommunication systems are particularly important. The principles of public policy development include: • • • • •

freedom of scientific and technological creativity, system approach, concentration of resources, rational balance, and openness.

The principles determine the directions of the state policy of technological development: • • • • •

personnel and human capital, infrastructure and environment, interaction and collaboration, management and investment, and cooperation and integration.

The main results of scientific and technological development of Russia include the following: • improving the quality of life of the population, • promotion of Russian technologies and innovative products to new markets, and • ensuring the growing influence of science on technological culture in Russia. However, like any strategic document, the strategy has some weaknesses. For example, an important omission is the lack of risk identification of technological development.

Technological Growth of Russia in Terms of Digitalization of the Economy    229

It would be more expedient to specify the main directions of technological development: • creation and development of unique scientific facilities of a class “magascience,” large research infrastructures on the territory of the Russian Federation; • participation of Russian scientists in international projects providing access to new competencies and resources; and • transition of managers of budgetary funds to the model of “qualified customer.” The strategy of scientific and technological development of Russia provides a number of activities aimed at: • formation of a modern management system and ensuring the increase of investment attractiveness; • formation of an effective communication system, making the economy and society more responsive to innovation, creating conditions for the development of knowledge-based business; • creation of conditions for research and development, corresponding to the modern principles of organization of activities and the best Russian and world practices; • creation of opportunities to identify talented young people and build a successful career, the development of the intellectual potential of the country; and • formation of a model of international scientific and technical cooperation and international integration, which allows the protection of the identity of Russian science and improves its efficiency through mutually beneficial cooperation. The Ministry of Education and Science, Russian Academy of Science (RAS), the Ministry of Finance, the Analytical Centre Under the Government of the Russian Federation, Support funds, the Central Bank of Russia, Vnesheconombank, VTB Bank, Rosselkhozbank, and many others are the executors of the plans. To develop the intellectual potential of the nation subprograms, “development of national intellectual capital” and “fundamental research in the interests of long-term development and competitiveness of society and the state” (Vorontsova, Dedyukhina, Kosinova, Momotova, & Yakovenko, 2019, p. 985) were adopted. To implement structural changes in the Russian economy, its technological innovation and the transition to intellectual resources for the development of the subprograms, “scientific, technological, and innovative

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development in a wide range of areas on the initiative of the research, engineering, and business community” and “research, development, and innovation in order to implement the priorities of scientific and technological development of Russia, including comprehensive scientific and technical programs and projects” (Vorontsova, Dedyukhina et al., 2019, p. 987) were developed. The following areas have become a priority: • digital production technologies, robotic systems with the number of projects 110 and the amount of subsidies 6,430 million rubles; • efficient energy and the new architecture of energy systems (the number of projects, 79; the amount of subsidies, 4,329 million rubles); • personalized medicine and health care (the number of projects, 49; the amount of subsidies, 2,457 million rubles); and • intelligent transport and telecommunication systems, development of sea, space, and air space (the number of projects, 32; the amount of subsidies, 1,642 million rubles). As a separate paragraph is the support and development of the 17 centers for collective use of scientific equipment in 2017–2018, among which is the Supercomputer Complex of Moscow State University, Tunka Astrophysics Centre for Collective Use of ISU, CRISM “Prometey,” the collective use of the center “Microsystem Technics and Electronic Component Base,” and so on. The total state funding is 2.4 billion rub. (Khrustalev, Tsyganov, & Rudatskaya, 2013). At the moment, there is a basis for Russia’s technological growth, but it is difficult to build a further vector of development on its basis. This vector is tactical planning, which should reflect the methods by which the goals will be achieved. And the process of searching such methods becomes a serious problem without concretizing the goals. The state policy of the Russian Federation is constantly being improved. Currently, the state program of the Russian Federation “Scientific and technological development of the Russian Federation” is being prepared, which is supposed to redefine the priorities of the development of Russian science and technology. CONCLUSIONS In order to consolidate its position on the world stage, meet the current trends in the development of the world economy, the Russian economic system should take into account the following recommendations:

Technological Growth of Russia in Terms of Digitalization of the Economy    231

• to determine and elaborate the main directions of development by using SMART technology; • to identify in detail the stages of implementation of the strategy of scientific and technological development; and • to take into account the timing and the risks possible in the implementation of the strategy. Thus, the strategic plan of scientific and technological development of the Russian Federation requires some improvement and adjustment. If the changes are based on highlighted recommendations, the strategy will be more precise and forward-looking. The process of digitization and the use of modern information technologies in production plays an important role in the growth of the country’s economy, as well as leads to various changes in the socioeconomic sphere of society as a whole. A rational approach, taking into account all possible vectors of development and its movement in the right direction will allow Russia not only to maintain a stable position in the world market of raw materials, but also to achieve international competitiveness among processing industries, first of all, knowledge-intensive ones. In order to do that, we need to increase human, intellectual and technological advantages, to form a flexible regulatory framework for the introduction of digital technologies in all spheres of life. Pursuit of the strategy of intensive digitalization involves a fundamental restructuring of approaches to decision-making that will lead to the preservation of competitiveness and achievement of positive results. REFERENCES Khrustalev, E. Yu., Tsyganov, S. A., & Rudatskaya, E. R. (2013). Grant methodology of strategic innovation-oriented management of fundamental research. Economic Analysis: Theory and Practice, 13, 2–12. Kusakina, O. N., Vorontsova, G. V., Momotova, O. N., Krasnikov, A. V., & Shelkoplyasova, G. S. (2019). Using managerial technologies in the conditions of digital economy. In E. Popkova & V. Ostrovskaya (Eds.), Perspectives on the use of new information and communication technology (ICT) in the modern economy (pp. 261–269). Cham, Switzerland: Springer. Matveev, S. (2017). Having formed a new culture of communication, we will see the explosive growth of science and technology. Retrieved from http://www.sib-science .info/ru Mayorova, A. N., Panasenko, S. V., Nikishin, A. F., Ivanov, G. G., & Mayorova, E. A. (2018). Analyzing regional differences in the condition and development of trade in Russia. In A. F. Adekola (Ed.), Entrepreneurship and sustainability issues (pp. 927–936). Cham, Switzerland: Springer. Mikhailova, A. A. (2013). Comparative analysis of scientific and technical potential of the Baltic States and Russia. Kaliningrad, Russia: Baltic Federal University.

232    G. V. VORONTSOVA et al. Momotova, O. N., Belokon, L. V., Kilinkarova, S. G., Mayboroda, T. A., & Stroi, G. V. (2019). Conceptual Approaches to Formation of Financial Strategy of a Higher Education Institution. In E. G. Popkova (Eds.), The future of the global financial system: Downfall of harmony (pp. 803–812). Cham, Switzerland: Springer. Strategy of Scientific and Technological Development of the Russian Federation. (2016). Retrieved from https://www.garant.ru/products/ipo/prime/doc/ 71451998 Titov, B. (2018). Russia: From digitalization to digital economy. Retrieved from https:// stolypin.institute Vorontsova, G. V., Dedyukhina, I. F., Kosinova, E. A., Momotova, O. N., & Yakovenko, N. N. (2019). Perspectives of development of managerial science in the conditions of information society. In E. Popkova & V. Ostrovskaya (Eds.), Perspectives on the use of new information and communication technology (ICT) in the modern economy (pp. 980–989). Cham, Switzerland: Springer. Vorontsova, G. V., Ligidov, R. M., Nalchadzhi, T. A., Podkolzina, I. M., & Chepurko, G. V. (2019). Problems and perspectives of development of the world financial system in the conditions of globalization. In E. G. Popkova (Eds.), The future of the global financial system: Downfall of harmony (pp. 862–871). Cham, Switzerland: Springer.

CHAPTER 26

ARTIFICIAL INTELLIGENCE IN DIGITAL TYPE LOGISTICS SYSTEMS Ivan D. Afanasenko Saint Petersburg State University of Economics Vera V. Borisova Saint Petersburg State University of Economics

ABSTRACT The chapter investigates the possibility of using artificial intelligence (AI) technologies in the design of modern logistics systems. Prospects of application of the network-centric concept of logistic management are considered. Subsystems and elements of the logistics system functioning on the principles of network centricity, including information superiority and self-synchronization of elements are identified. The research demonstrates that the fundamental changes in the construction of digital type logistics systems are due to the emergence of modern AI algorithms that allow to design objects with an unlimited number of participants—“systems of systems.” These logistics organizational forms are a set of network-centric communication platforms with various, overlapping and simultaneously coexisting equal elements. Accord-

Meta-Scientific Study of Artificial Intelligence, pages 233–240 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

233

234    I. D. AFANASENKO and V. V. BORLSOVA ing to the internal heterogeneity, volume and complexity of interconnectedness across all the elements in the previous experience of building logistics systems, they had no analogues. Artificial intelligence allows the elements of the logistics system to work smoothly: to act instantly when receiving information from sensors and a focal digital platform. Acceleration, mobility, and flexibility of goods movement, the possibility of pass-through management of the entire value chain, monitoring of performance indicators online are becoming a priority in logistics activities. At the conceptual level, the problem associated with the identification of features and discovering the potential of using AI for the management of digital type logistics systems has become quite evident. This determines the relevance of this study.

Digital logistics is a special type of logistics where functional areas and key activities have been transformed using information and computer technologies, software products and services (Afanasenko & Borisova, 2019). A number of scientists associate the formation of digital logistics with the use of convergent technologies (Kovalchuk, 2011), the transformation of information flow into digital (Pustokhina & Rodkina, 2016) and the development of innovative algorithms (Bubnova & Levin, 2017). Generalization of different points of view showed that the essence of digital logistics is expressed in its main goals, objectives, and object–subject composition of the organizational structure of the logistics system. Subsystems and elements of this logistics system are integrated into a single digital stream. The basic concept here is digital flow as “a set of communication technologies, digital transformation regulators, networks, messengers, cloud technologies, and platforms (Afanasenko & Borisova, 2019). In fact, it is a virtual form of economic flow organization. The success of the digital logistics system model largely depends on the coordinated work of all its subsystems and elements. In modern realities, such consistency is directly related to the use of AI and intelligent applications. METHODOLOGY Principles and methods of construction of logistics systems receive new scientific interpretations. The digital format of logistics is based on the interdisciplinary principle and the use of combined knowledge. This is evidenced by the so-called cross-cutting technologies which are key scientific and technical areas. The priority group of new technologies includes: big data, AI, quantum technologies, new and portable energy sources, sensors and components of robotics, wireless communication technologies,

Artificial Intelligence in Digital Type Logistics Systems     235

technologies for controlling the properties of biological objects, neurotechnology, and virtual and augmented reality technologies (Rashid, 2017). They find practical application in the following areas of AI: machine learning, computer vision, natural language processing, intelligent robots, virtual personal assistants, recommendation generators, speech translation, gesture control, and computing systems that adapt their behavior in the environment. Different analysts offer different estimates of the growth rate of the market of intelligent logistics services. Most of them pay attention to the fact that the use of AI software in the world economic practice will grow by 42 times from $1.4 billion in 2016 up to $59.8 billion in 2025. (McKinsey, 2017). Artificial intelligence technologies transform the way on how the elements of the logistics system interact with each other and with software products. The virtualization of logistics activities and the creation of intelligent logistics systems are the result of the coordination of the use of technical and sensory devices based on AI. Experts predict a rapid landscape evolution of the content and applications of virtual and augmented reality technologies by 2021. Interaction of managers with physical devices using dialog interfaces, for example, a dialog box, which has proven to be successful in practice when transmitting information to the user and receiving a response from him. Currently, they are more focused on supporting devices with a microphone (speakers of smartphones, tablets, computers, and cars) and chatbots (virtual interlocutors). With the further development of digital networks their opportunities will keep growing with the emergence of new models of user connections and the capacity for closer collective interaction of devices, which creates the basis for new ways of interaction with the external environment. RESULTS Artificial intelligence is usually understood as the ability of machines to perform individual functions of human intelligence (Russell & Norvig, 2007), and in relation to logistics practice it means to justify and take appropriate decisions based on the analysis of previously obtained data and external factors. Artificial intelligence technologies also include algorithms that are able to solve problems in the same way as a human being does. According to expert estimates, the introduction of AI technologies by 2030 will increase world gross domestic product by 14%. The trend of commercialization of AI technologies has emerged in the logistics sector of the economy. Thus, the income of 50 world leaders of the logistics segment in the amount of $253 billion (2017) is associated with: virtualization of services

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and abandonment of tangible assets; decomposition of information flows and personalization of services (Borisova & Kudryashova, 2019). Virtual logistics intermediaries streamline their activities with the help of multimedia, software, AI technologies. Of particular interest are the research methods of AI: conventional (machine) and computational (iterative, learning). Their application makes it possible in a short period of time to autonomously carry out millions of transactions, analyze reports of sales agents, rank suppliers, summarize information of social networks, and so on. Such intelligent applications fundamentally change the nature of logistics activities and transform the structure of the logistics system (Borisova & Gordei, 2017). A new role in the intelligent logistics system is taking on real physical things that operate within the framework of hard-coded models using AI and machinery. Such intellectual things are adapted to perform complex logistics functions and demonstrate a fairly natural interaction with the logistics environment. “Smart” things can be equipped with sensors, accelerators, keyboard, optical fiber, heating elements, solar panels, players, and other devices. With the increased usage of intelligent devices in logistics practice (unmanned aerial vehicles, autonomous vehicles, etc.), there will be a shift from the use of autonomous intelligent things to their joint use. The possibilities in this direction expands the technical world of virtual and augmented reality. Artificial intelligence is one of the critical assets of modern intelligent logistics systems of digital type. The tasks of AI in logistics are differentiated into: information, operational, and strategic. Information tasks of AI are aimed at a significant expansion of the collection, processing, and analysis of data circulating in the logistics system, and help to increase the competitive advantages by means of speed, flexibility, agility, and quality of information management. The work of AI takes place in the process of its training on the basis of a huge amount of data and in the search for those formulas and dependencies that are not determined by man. Digital technologies have created a number of supergiants working on the principles of information superiority: Apple, Google, Amazon, Alibaba, Facebook, Microsoft, Baidu, Tencent, and so on. In fact, these companies acquire the status of virtual intermediaries, taking over significant shares of the world market. As a result, competitive advantages are gained by those logistics systems that can get access to a multi-million audience of mobile consumers and suppliers and have an impact on the configuration of information flows. The scope of application of AI in logistics operations is quite wide: unmanned machinery, equipment and vehicles, robotic warehouse equipment, and so on. There is a tendency of expansion of mass production and cost-reduction of intellectual operating technique. At the same time,

Artificial Intelligence in Digital Type Logistics Systems     237

thousands of intellectual objects can work in coordination and solve complex logistics tasks. Combining several machines and equipment in a digital “intelligent package” (platform), makes it possible to exchange information about the goals, jointly develop a strategy for action, coordinate functional interaction. Intelligent machines are equipped with a “target library” and a facial recognition system. With a portable device, the logistics operator can quickly reformat intelligent objects into a single automated platform. Artificial intelligence in contrast to the machine intelligence and supercomputers can generate for itself the algorithms of actions, and instead of compact disk read-only memory (CD-ROM) or random access memory (RAM), specific to computers, can use chains of neural connection which reproducing instantly and almost immediately decaying (Brink, Richards, & Fetherolf, 2017). The equipment for autonomous performance of warehouse operations by robots is being tested. The developers’ attention is focused on stand-alone hardware with network support; on technologies for human-machine interaction, including decision-making with the help of AI. The strategic tasks of AI are also changing. Intelligent algorithms become assistants in the selection and making strategic logistics solutions. Artificial intelligence has important advantages for accessing all scientific, statistical, and other information that humanity has accumulated over the entire period of the existence of civilization. It is able to process information creatively, identify latent opportunities, combine knowledge, and make a breakthrough in further development. These include hardware robot devices, autonomous vehicles, applications, and services (virtual personal assistants, intelligent advisors). Such systems can work as independent intelligent products of a new class, as well as be integrated into a variety of hardware, software, and service solutions. This is reflected in the process, industry, and technology management of logistics flows. The process logistics management defines the way of transforming the logistics activities that will benefit the client. The client, in terms of the process approach, is the one who gets the “results” of the transformation process. In this case, the client can be located inside the logistics system, and to be outside it. The elements of the digital transformation system are: scientific and technological educational structures of the world level, digital production, transport logistics, warehouse logistics, electronic commerce, and digital services. The transformation tools are: processes in the “as is” format, modelling and description of processes, identification of “bottlenecks” in technology, absence or duplication of certain types of work in the supply chain, inconsistencies, and weaknesses. Digital conversion based on the process approach provides an opportunity to: implementation of uniform performance standards of logistics operations, use a common

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technology carrying out a certain work, to develop a coordinate system for evaluation of results, reduce the adaptation period of digitalization, and contribute to the motivation of the staff. Artificial intelligence technology transforms not only the traditional supply chain in logistics, but also the ecosystem of companies, markets, and sectors of the economy. Process logistics management in its “pure” form is rare and, as a rule, has an industry nature. This is due to the emergence of new digital markets: digital production and delivery of food (water), digital resource extraction, additive technology factories, power distribution systems, unmanned vehicles, unmanned aerial vehicles, digital railways, telemedicine and personal medicine, digital cities (smart homes, smart roads), digital financial technologies, digital security (security software), e-commerce, e-education, digital culture, and digital media. Thus, Gazprom Neft uses multi-agent technologies in its logistics practice to organize its work in a constantly changing environment. Based on the interaction of participants in the cyber-physical system, AI offers options for management decisions. These decisions are of recommendatory nature, and not final. Multi-agent technologies make it possible to quickly solve complex logistics tasks, integrating supply chain participants, helping to increase order fulfillment and cost savings. Artificial intelligence complements human knowledge and generates new ideas through self-learning systems. Technological management of logistics systems focuses on the application of broadband Internet (4G-5G), software and hardware of digital technologies: Internet of things, digital design and modelling, quantum technologies, big data technologies, elementary base (processors), robotics, sensors, additive technologies (3D printing), cloud technologies, and supercomputer technologies. Recently, a new model of network-centric logistics system has been formed. It consists of a number of interconnected subsystems: information, sensory, and operational. Information subsystem (focal digital platform) cuts across all other blocks of the system. The sensor subsystem implements the possibility of identification and diagnosis of elements. Operational subsystem determines how to achieve goals. The advantages of such a model are consistency of actions, speed of decision-making, integration of elements united in a single geographically distributed network-centric system. The main components of such a system are: digital platform (information grid), which provides access to all necessary information online; applied digital technologies (their relevance, flexibility, and maneuverability); and integrated sensor. However, as the complexity of the components of the system increases, there are problems of compatibility of the information used and software products. Self-synchronization of the logistics system allows to quickly start the parallel operation of the elements-links “bottom-up,” providing the

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opportunity for the implementation of the principle of information superiority. As a result, the information dominance of the logistics system makes it possible to solve the tasks more efficiently and quickly, to adapt to the changing parameters of the environment and to anticipate possible risks. The functioning of network-centric logistics systems does not depend on the territorial location of the elements and their structure. In such systems, there is a new management elite—“digital operators.” Intellectualization of logistics activity changes stereotypes and connections between elements within the system. CONCLUSIONS The analysis of the use of AI technologies in logistics showed that the transformations carried out in this direction are due to the integration into a single digital space of social networks, industry, interdepartmental and international databases, the formation of giant intermediary network-centric structures. Quantitative and qualitative changes in information often lead to changes in the reliability of links between elements in the logistics system. The quality of the network-centric logistics system is guaranteed by the focal digital platform. It is this component of network centrism that links information technologies and elements of the logistics system. Focal digital platforms that possess the data on the status of qualitative parameters of the logistics system may apply the performance indicators for the digital activity of participants of the system. When designing digital type logistics systems, it is important to determine the critical digital infrastructure (jointly used and providing integration processes), to develop a common procedure for ensuring their protection, including (if necessary) the creation and implementation of international protection protocols and procedures for joint use of digital platforms. In conceptual terms, a set of directions should be defined to ensure the sustainability of the digital logistics space in relation to the impact of external threats and internal adverse factors. Commercialization of AI in a single commodity distribution system of the economy is aimed at business analytics; recognition of texts, speech and images; extraction, understanding and analysis of information; implementation of information security systems and robotics. Every possibility of AI is fraught with danger. Hawking (2009) has repeatedly drawn attention to this fact: “The development of AI can be both the most positive and the most terrible force in the development of mankind. We must be aware of the danger it poses” (p. 256). Some technologies have already gone beyond traditional algorithms and are able to create systems that can understand, learn, predict, adapt

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to a changing environment, and are potentially ready to function autonomously. Practice shows that the prospects in this direction are huge. Another thing is that the further development of AI can be made faster, more constructive, and safer. The prerequisites for such development have been created in the field of logistics and are associated with the practical implementation of digital logistics systems. REFERENCES Afanasenko, I. D., & Borisova, V. V. (2019). Digital logistics. Textbook for universities. St. Petersburg, Russia: SPb. Borisova, V. V., & Gordei, K. G. (2017). Digital technologies warehouse logistics. Science of the XXI century: Problems and prospects of research, 2, 3–7. Borisova, V. V., & Kudryashova, P. A. (2019). Virtual logistics operators: Foreign experience and Russian practice. News of St. Petersburg State University of Economics, 2(116), 83–89. Brink, H., Richards J., & Fetherolf, M. (2017). Machine learning. St. Petersburg, Russia: SPb. Bubnova, G. V., & Levin, B. A. (2017). Digital logistics is an innovative mechanism of development and effective functioning of transport and logistics systems and complexes. International Journal of Open Information Technologies, 5(3), 2307–8162. Hawking, S. (2009). The future of space-time. St. Petersburg, Russia: AMPHORA. Kovalchuk, M. V. (2011). Convergence of science and technology: A breakthrough in the future. Russian nanotechnology, 6(1–2), 13–23. McKinsey Report. (2017). Digital Russia: New reality. Retrieved from https://www .mckinsey.com/~/media/mckinsey/locations/europe%20and%20middle %20east/russia/our%20insights/digital%20russia/digital-russia-report.pdf Pustokhina, I., & Rodkina, T. (2016). RFID technology in logistics: The real achievements and challenges. Journal of Russian Logistics, 1(2), 48–53. Rashid, T. (2017). Create a neural network of St. Petersburg, Alpha book, 274. Russell, S., & Norvig, P. (2007). Artificial intelligence: Modern approach (2nd ed.). Moscow, Russia: Williams.

PART III IMPLEMENTATION OF THE SUBJECT APPROACH IN PSYCHOLOGY AND PEDAGOGY BASED ON ARTIFICIAL INTELLIGENCE

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CHAPTER 27

COGNITIVE ASSESSMENT OF THE STUDENTS’ MORALITY AND IDENTITY WITH THE HELP OF ARTIFICIAL INTELLIGENCE PROGRAMS Irina A. Kolinichenko Pyatigorsk State University Ekaterina N. Asrieva Pyatigorsk State University Tatyana V. Varfolomeeva Volgograd Medical State University Natalia V. Gordienko Pyatigorsk State University Elena A. Enns Pyatigorsk State University

Meta-Scientific Study of Artificial Intelligence, pages 243–250 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

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ABSTRACT The research aim is to analyze the judgments about morality and the categories of students’ group identity, depending on the focus of education and level of religiosity performed by men with their intelligence, called natural, based on the available programs of artificial intelligence (AI). The research results obtained through the nonparametric Mann-Whitney U test of the program Statistica SPSS 17 revealed significantly greater differences in the group of undergraduate students in comparison with the students of the secondary vocational education level (SVE) on the criterion of the orientation towards the moral behavior and the assessment of the immoral behavior of most people, which corresponds to the idea of the rising nature of moral development according to the theory of Kohlberg. Achieving identity occurs through addressing situations involving moral choices. A large mathematical load of the categories of invariable and group identity depending on the level of education was manifested among the students of secondary vocational education, and they also have a lower level of religiosity, although it could be expected that a person with a higher value of morality is a supporter of group values, according to Erikson. The data obtained indicates the manifestation of variability, not dogmatic assessments of morality and identity in human consciousness, as natural intelligence is free from categorical assessments, like a formalized coordinate system inherent in AI.

The modern stage of psychology development in conditions of global computerization is characterized by a particularly relevant research to develop new information technologies of creation of metalanguage models, the recognition of which allows the communication of man and computer by understanding the meaning of mathematical symbols for the interpretation of the results of psychological investigations from the point of view of cognitive psychology (Tikhomirov, 1976). Modern scientists identify the limits of formalization inherent in the basis of artificial intelligence (AI) programs. Analyzing the cognitive functions of the dilemmas developed by Kohlberg (Kohlberg & Power, 1981), modern scientists prove that the existing elementary operations for solving complex problems of machine intelligence, which are analogous to the human mind, are obviously not enough to solve the moral dilemma, implying the resolution of the contradiction contained in it and the search for correlation between two conditions which have equally negative effect for the man (Voskovskaya & Kulikov, 2014). The difficulty in solving the dilemma is to identify contradictions in it, as any choice will eventually become equally negative for the subject. What is not characteristic of machine intelligence in this case is that the task of choice implies rationality, the most effective solution, but in the case of the analysis of a complex situation (as in the saying—“damned if you do and

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damned if you don’t”), cognitive analysis of the dilemma, the explanation of human behavior in a situation of moral choice contributes to the formation of the experience of moral relations, self-determination, their own position to the real manifestations of morality through the actions of others, through identification with others. METHODOLOGY The most evidential superiority of natural intelligence is the results of many years of research obtained by Kolhberg’s approach to solving the problems of measuring the moral development of man with the help of his method of dilemmas (Kohlberg & Power, 1981). Putting forward the idea of phased development of morality of the subject, the scientist was based on the recognized research of J. Piaget on the progressive development of human intelligence in ontogenesis (Piaget, 1960). Let us consider the structure and content of the dilemmas. To measure the level of moral consciousness, scientists have developed a situation containing a problem, the solution of which helped to correlate the position of man with the characteristics of one of the three levels of moral development: pre-conventional, conventional, or post-conventional, each of which, in turn, was divided into two more specific stages, so their total number was six. The contradiction was not only in the content of the moral situations themselves, which would be natural—the most important of the ideas found by the scientist was to measure not the solution of the problem, but its justification; thus, the answers of the subject showed the cognitive nature of his thoughts (Kohlberg & Power, 1981). Since the research conducted by Kohlberg, the scientists have offered other alternatives for the measurement of development of moral consciousness. So, Rest (1999) believed that a person can manifest at the same time several schemas of moral development with cultural identity, Gilligan was critical to the study of the levels of development of morality, which is common to all, from the point of view of Kohlberg, instead, she suggested, the understanding of morality from the standpoint of gender (Gilligan, 1977). G. Lind developed the Test of Moral Judgment, taking as a basis the competence of the moral judgment of the individual, used the original idea of Kohlberg, who had previously abandoned this idea in favor of the stages of moral development (Lind, 2006). D. Narvaes designated the Triune Theory of Ethics as ascending, agreeing with L. Kolberg, in contrast to the descending theories proposed by other scientists. The study of identity as a set of elements experienced by the personality, from one hand, as the similarity with others and, from the other, as his or

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her own development that distinguishes it from others were used in this work (Ericsson, 1996). The problem of achieving true identity of the individual with its chosen set of elements is being developed by different scientists. So, James E. Marcia showed the solution of such problems in the identity status theory. According to Waterman (1982), identity is the process of human development simultaneously with the improvement of its content. The elements of identity are the choice of professional affiliation, religious, secular, moral views, politics, and group roles. The authors’ position on this issue is that the moral contradiction can be concluded both in the content of the dilemma and in single judgments about morality, which were originally developed and presented to the subject in the form of ready-made options; they can be understood by the human brain. The methods presented in the study were tested and proved to be effective. RESULTS The problem of mathematical processing of the data obtained in the study with the help of AI technologies has been successfully solved in the field of mathematical statistics for psychologists (Nasledov, 2004), so in this chapter the analysis and interpretation of the results depended on the depth of our understanding of the studied problem in the framework of cognitive psychology. According to the algorithm on which the programs of AI are based, moral evaluation, the definition of each act was made by assigning it values in accordance with the formalized coordinate system from 1 to 7 points, where 1 (I completely disagree), 2 (I disagree), 3 (there is something I do not agree), 4 (I can agree neither with this nor with that), 5 (I agree with something), 6 (I agree), and 7 (I completely agree). The listed judgments concerned: determination of behavior of the majority of surrounding people by standards of morality; an assessment of the moral norms regulating life of society; need to be guided by moral norms of previous years; the analysis of the criminal and civil laws which are a basis of moral norms; a reality of moral norms given by God; orientation towards the view of the majority of people in the nearest environment in the solution of a question of what can be considered as immoral behavior and acts; moral norms based on compassion to other people, protection of the weak; existence of things for the sake of which it is possible to deviate from standards of morality (Kolinichenko, 2017). The processing of the data included a count of each of the above categories separately, and then they were placed in Column 1. Next, we calculated the variability of the scores assigned to each category (their arithmetic

Cognitive Assessment of the Students’ Morality and Identity    247

average) in Column 2 under the title “Variability of Personal Categories,” “Variability of General Categories,” “Variability of Group Categories.” After that, we calculated the arithmetic mean of desirability of each category for the respondents (according to the scores assigned to each word or phrase in Column 3) in a similar way (Ulybina, 2012). The respondents were students of the Pyatigorsk linguistic university of several faculties who shared the same interests: regardless of the main specialty they mastered, they were trained in an additional educational program of university-wide importance: “Project Management.” The sample of the study was 39 people, 23 of them were students of secondary vocational education ([SVE]; aged 16–17 years) and 18 undergraduate students (aged 18–19 years). After filling in the individual test reports and counting the results of each response, the data were entered in a summary table. Further, with the aim of identifying significant differences between the average values of groups of respondents, we have produced statistical calculations using the nonparametric Mann-Whitney U test of the program Statistica SPSS (Nasledov, 2004). At the first stage of the study, we conducted a comparative analysis of the differences in the categories of identity and moral judgments depending on the education received—SVE or bachelor (Table 27.1). At the first stage of the study, we divided the subjects by level of education, students of SVE were included in Group 1, undergraduate students in Group 2. It is interesting that most of the significant differences in the assessment of moral judgments are in the group of undergraduate students, and TABLE 27.1  Inter-Group Differences in the Assessment of Morality and Identity of the Students Depending on Their Education The Intensity of Grade

Intergroup Dfferences

Group Categories of Morality and Identities The behavior of most surrounding people is determined by morality.

1

2

U

p

377.5

402.5

101.5

0.02

The origin of morality is stated in the sacred books.

397.0

383.0

121.0

0.06

Orientation towards the opinion of the majority in the evaluation of immoral behavior.

389.5

390.5

113.5

0.03

Immutable (slightly modified) identity.

525.0

255.0

119.0

0.05

Group identity.

538.0

242.0

106.0

0.03

Notes: Group 1 = SVE students, Group 2 = undergraduate students, p = significance level, U = Mann-Whitney significance criterion.

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the differences in the categories of identity are more evident among SVE students. Undergraduate students (402.5), unlike students of SVE (377.5) at a reliable level of significance p = 0.02, U = 101.5 are more in favor of the fact that the behavior of the majority of people around them is determined by moral standards. The fact that the origin of morality is stated in the holy books is also more evident in Group 1 (397.0) compared to Group 2 (383.0) at the level of trends p = 0.06, U = 121.0. The orientation towards the majority opinion in assessing immoral behavior in Group 2 (390.5) is more evident, in Group 1 it is less significant (389.5) p = 0.03, U = 113.5. In Group 1 (SVE), the obtained data of the invariable category of identity (525.0) are expressed much more strongly than in Group 2 (225.0) at a statistically significant level p = 0.05, U = 119.0. Also, students of Group 1 have mathematical differences in relation to the category of group identity (538.0) as opposed to Group 2 (242.0) at a reliable level of significance p = 0.03, U = 106.0. At the second stage of the experiment, we divided the answers of the respondents based on the results of self-assessment of their belonging to the level of religiosity and studied the differences in the categories of identity. In Group 1, we included the data of students with the lowest level of religiosity, who justified their choice by the fact that they consider themselves to be nonbelievers, or people whose faith almost does not affect their daily lives. The results of individual data of students with a low level of religiosity, who called themselves people whose faith has some value in their lives were included in Group 2. The results of statistical calculations are presented in Table 27.2. It was the differentiation of respondents according to the level of religiosity that revealed differences in moral judgments at the level of a tendency which are relevant for analyzing. TABLE 27.2  Intergroup Differences in the Assessment of Judgments About Morality and Identity Depending on the Level of Religiosity The Category of Identity/Judgment About Morality

The Intensity of Grade

Intergroup Differences

Group 1

Group 2

U

p

The basis of moral norms is compassion and protection of the weak

104.5

220.5

49.5

0.057

The possibility of deviation from the norms of morality

104.5

220.5

49.5

0.056

Attractiveness of group identity

181.0

144.0

24.0

0.005

Note: Group 1: respondents with the lowest level of religiosity or lack thereof, Group 2: respondents with a low level of religiosity.

Cognitive Assessment of the Students’ Morality and Identity    249

Moreover, in Group 1 (with a low level of religiosity) the data (181.0) were expressed much more strongly than in the second (144.0) at a statistically significant level p = 0.005, U = 24.0. Statistically significant data revealed a pronounced propensity of students with higher level of religiosity to the fact that “moral norms are based on compassion for other people and should, first of all, protect the weak,” and to the statement that “there are things for which it is possible to deviate from the norms of morality.” In addition, the results showed a statistically significant difference in the following category—the attractiveness of group identity. CONCLUSIONS The study of assessments of judgments about morality and different categories of identity of students enrolled in the program “Project Management” corresponds to one of the national goals and strategic objectives of the Russian Federation to strengthen civil identity on the basis of spiritual, moral, and cultural values, as they are the scientific basis for the prevention of intergroup differences in student groups working on the implementation of educational and scientific projects. The results of the study can also be recommended to universities that the group project work on the program “Project Management” continue on, taken into account the high assessment of group identity for students of SVE and the attractiveness of group identity for students of both directions of training, who attributed themselves to the lowest level of religiosity or its absence, which may be important for group work in universities with a secular, nonreligious nature of education. The study allows us to say about the beginning of work on the formulation of the idea of a multi-level theory of morality, according to which the moral position of a person, depending on culture, ideas about the level of religiosity, education, different categories of identity, can be productively studied using the authors’ method of “judgment on morality” and modified method of Kuhn (Kuhn & Mcpartland, 2003): “Who am I?” as far as the possibilities of natural and AI allow. The results of the study can be recommended for understanding the moral path of the student’s personality, mastering modern project activities. We believe that it is the human mind that is able to reveal the internal psychological mechanisms of making morally significant decisions when choosing one of the proposed alternatives in the content of judgments about morality (from 1 to 7) and relate these ideas to their own identity, making, in this case, a quite conscious, a similar choice.

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REFERENCES Ericsson, E. (1996). Identity: Youth and crisis. Moscow, Russia: Progress. Gilligan, C. F. (1977). In a different voice: Women’s conception of the self and morality. Harvard Educational Review, 47(4), 481–517. Kohlberg, L., & Power, C. (1981). Moral development, religious thinking and question of a seventh stage. Zygon, 16, 203–259. Kolinichenko, I. A. (2017). Differences of morality, group identity, and personality variability depending on education. Modern Studies of Social Problems, 8(5), 36–53. Retrieved from http://ej.soc-journal.ru Kuhn, M., & Mcpartland, T. (2003). Who Am I? The use of the test “20 judgments.” Psychology of self-development: textbook. Retrieved from http://www.gurutestov .ru/test/18 Lind, G. (2006). The moral judgment test: Comments on Villegas de Posada’s critique. Psychological Reports. 98, 580–584. Narvaez, D. (2008). Triune ethics: The neurobiological roots of our multiple moralities. New Ideas in Psychology, 26, 95–119. Nasledov, A. D. (2004). Mathematical methods of psychological research. Analysis and interpretation of data: A training manual. SPb. Piaget, J. (1960). The general problems of the psycho-biological development of the child. In J. M. Tanner & B. Inhelder (Eds.), Discussions on child development. London, England: Tavistock. Tikhomirov, O. K. (1976). “Artificial intelligence” and psychology. Moscow, Russia: Science. Ulybina, E. V. (2012). Immutability as a characteristic of football fans’ identity. Psychological Research: Electronic Scientific Journal, 5(23). Retrieved from http:// psystudy.ru/index.php/num/2012v5n23/682-ulybina23.html Voskovskaya, L. V., & Kulikov, D. K. (2014). Cognitive functions of the dilemma in the light of problems of AI. Electronic Scientific Journal “Engineering Bulletin of the Don, 4(2). Retrieved from ivdon.ru/ru/magazine/archive/n4p2y2014/2643 Waterman, A. S. (1982). Identity development from adolescence to adulthood: An extension of theory and a review. Devel. Psychol, 18(3) 341–358.

CHAPTER 28

ARTIFICIAL INTELLIGENCE AND MORALITY Psychological Aspects of Interaction Dmitriy A. Medvedev Herzen State Pedagogical University of Russia Inna V. Kicheva Pyatigorsk State University Nina M. Shvaleva Pyatigorsk State University Elena I. Gorbacheva Tsiolkovsky Kaluga State University Natalia M. Skripnik Pyatigorsk Medical and Pharmaceutical Institute

Meta-Scientific Study of Artificial Intelligence, pages 251–261 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

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ABSTRACT The chapter reveals the main psychological aspects of the relationship between artificial intelligence (AI) and morality. The purpose of this chapter is to analyze the main ethical and psychological aspects of the interaction of modern humanity and AI. The definition of AI is given, which allows understanding an integral interpretation of this phenomenon and also describes the scope of application of AI systems in the modern/future world in the context of moral approaches to objects and phenomena of reality. General psychological aspects of entering AI into the life of modern humanity are revealed. The possibility of applying to the problem of AI as one of the psychological concepts of Kohlberg’s moral development theory is presented in this chapter. The result of the study is to highlight the main ethical and psychological aspects of the interaction of mankind and AI systems, as well as the formulation of internal problems associated with the ethical and psychological aspects of the functioning of the AI systems themselves. The chapter demonstrates prospects for using the psychological concept of Kohlberg’s moral development theory which is based on the method of solving moral dilemmas to predict the possible levels of moral development of real-life/future AI systems. Comparison of the levels of people’s moral development and AI systems may be interesting both in terms of assessing the real/potential level of AI development in this direction and in terms of predicting possible hazards associated with the achievement of certain levels of moral development by robots.

The problem of artificial intelligence (AI) is one of the relatively new and unknown areas of scientific knowledge. In modern science there is no certainty on many issues related to this problem (including the question of determining the very concept of AI). Artificial intelligence systems are entering the life of modern humanity rather quickly and are capturing positions in it that have seemed unrealistic until recently. The reality of science fiction novels and movies gradually becomes the objective reality of modern humanity. First of all, it concerns the developed countries leading in the field of economic development (United States, China, Japan). Global trends in the development of AI systems will sooner or later reach other countries of the world. In a certain sense, the lag in this area may be a kind of advantage for them, since they can learn from the mistakes of others and prevent the possible consequences of the unjustified use of AI systems. Not everything that seems to be advanced and innovative at a certain time is a blessing for humanity in a distant prospect. On the other hand, it is obvious that the lag in such developments, which will objectively continue in developed countries, despite quite reasonable warnings from a number of responsible people, can have very adverse consequences for many countries, especially since some scientists have a direct parallel between nuclear

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physics research in the period of the 1930–1950s of the 20th century and modern research in the field of AI (Burenok, 2018). There are two main aspects of the problem of relations between AI and morality: (a) the moral aspects of the interaction between man and AI and (b) the moral of the AI itself. Questions related to determining the level of intellect development (Piaget, 1969) and morality (Kohlberg, 1973) have always been interested in psychological science. In this chapter we will consider two inseparably connected aspects of this problem: general psychological aspects of the problem of AI entering into the life of modern humanity and the possibility of applying one of the psychological concepts of moral development to the problem of AI-L. Kohlberg’s theory of moral development. METHODOLOGY The purpose of this chapter is to analyze the main ethical and psychological aspects of the interaction of modern humanity and AI. At the beginning of the chapter it is necessary to consider a number of issues related to the definition of the very concept of AI and the application of AI systems in the modern/future world. We highlight that this area of science is relatively new. Therefore, issues related to the operationalization of categories are of primary importance. As it has been already noted, the term “AI” has a number of interpretations. Based on the integration of the presented definitions (Osipov, 2008; Kohlberg, 1973), we will consider AI as technical/software systems designed by people (in the long run—and other carriers of intelligence—including artificial), capable of autonomously performing computational operations and achieving on this basis the given goals (in the future and their own), capable of obtaining new results, self-learning, and self-development. Fundamental importance for understanding the nature and types of ethical and psychological problems is the traditional distinction between AI into “weak” and “strong.” Appearances of ethical and psychological problems are directly manifested in: (a) the moral aspects of the interaction between person and AI and (b) the morals of the AI itself and directly causing changes in people’s lives; domestic species reflect the specificity of the functioning of the AI systems themselves and, on this basis, are capable of indirectly influencing the life of a person/humanity (Kicheva, 2011). Artificial intelligence systems have a number of qualities that are demanded in various areas of social practice: (a) possession of absolute memory, (b) the presence of a strict justification for each action, (c) no doubt and the possibility of justifying each of its steps, (d) absence of emotions,

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(e) miscalculation of the situation a few steps forward, (f) consideration of all possible scenarios, and (g) the possibility of finding implicit regularities. Let us consider the main areas of AI applications in the modern world. They are very extensive: (a) medicine (diagnosis of diseases and assistance to doctors in their treatment), (b) business (customer recognition), (c) transport (cars on autopilot), (d) logistics (changing the operational model of logistics from reactive to predictable, staying ahead), (e) trade (robots that know how to lie and bargain with people), (f) forensics (investigation of complicated crimes), (g) military (cyber war, combat robots, autonomous weapon systems), and (h) security sphere (analysis of citizens’ behavior, monitoring of social networks). Modern AI systems are especially effective in: (a) analyzing large data sets, (b) face recognition, (c) saving energy, and (d) games (chess, go). Besides that, AI systems are able to prove themselves in creative activities: (a) draw pictures, (b) compose music, and (c) write literary texts. Let us single out primary ethical and psychological aspects of the interaction between humanity and AI systems (they are especially relevant today because they relate primarily to the systems of “weak” AI, which are particularly actively involved in the life of modern humanity). External aspects can be illustrated by problems that are caused by the widespread use of AI systems in the present/future. The main problems of this class are generating already now or capable of generating serious psychological and social risks in the future (Asimov, 2004). They include: 1. Replacing humans with robots in many professions and the associated massive unemployment (e.g., the Chinese Foxconn Technology Group, a supplier of Apple and Samsung, announced its intention to replace 60,000 factory workers with robots; Ford, 2015). 2. Creation of autonomous weapon systems capable of giving its owners the possibility of destroying their opponents on a local/global scale with impunity. In this case, we can also recall the recent controversy with Google, whose employees published an open letter to the United Nations about the need to abandon a project that could lead to the creation of autonomous weapons—“killer robots.” Google is developing software used for pilot military Project Maven for control of drones (Malanov, 2018). 3. The ability to create AI systems, specializing in global totalitarian control over the entire population of the country/world. 4. Infantilization of man/humanity through shifting all serious decisions to AI systems as carriers of unconditional logic, unable to make mistakes due to actions based on algorithms that are the quintessence of pragmatism and expediency.

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5. Loss of man’s creative abilities (out of the lack of demand) after obtaining the ability of machines to independently create scientific works, works of art, and technical systems. 6. Intellectual degradation of a large part of humanity due to the extensive use of various “gadgets” that save their owners from the need to be able to independently carry out even the most elementary calculations. 7. Reaching of the total dehumanization of humanity based on the motivational dominant of the benefits and the final erasing of the differences between humans and robots and equalizing them in rights. 8. Bringing to the limit the tendency for humanity’s stratification to a small elite (including, broadly speaking, personnel needed to serve its diverse needs), possessing practically unlimited power and consuming the lion’s share of Earth’s resources, and the “inefficient part of the population”—the main part of humanity doomed to poverty, powerlessness, and gradual extinction (or relatively quick, “humane” elimination using the same AI systems). The internal problems associated with the ethical–psychological aspects of the functioning of the AI systems themselves are characteristic, to a greater degree, of the “strong” AI. Their analysis is of serious prospective significance, since the potential risks here may outweigh the risks associated with external aspects. The problems of this type include: 1. The ability of AI systems to develop self-awareness and develop their own value systems, making it impossible to operate them according to ethical requirements. 2. The danger of the emergence of hidden/open competition/confrontation between those who realized their potential limitless, based on absolute intellectual superiority over man, strength and gained the ability to set goals independently, and began to consider man/humanity as a means of achieving them or even hindering their achievement. Designing a “strong” AI system is complicated by the fact that intelligence is an open information system that is in constant development and improvement and assumes the existence of ethical regulators for correcting the directionality of any logic. The humanitarian position of a person may act as such a regulator. A hybrid approach to the creation of AI systems, designed to combine different models (symbolic and neural) with the possibility of flexible adaptation to achieve greater completeness of the range of cognitive-computational

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capabilities, as well as discoveries in the field of neural networks of consciousness allow us to imagine the possibility of combining AI with the brain and direct programming of human consciousness/behavior (Kolesnikov, 2001). RESULTS In the 20th century, humanity has already reached the level of scientific and technological development that with the misuse of its products (nuclear, chemical, biological, etc.), was able to menace global threats to the very existence of man on Earth. The rapid development of AI systems in the 21st century is another possible challenge to human existence itself. One of the conditions for the prevention of these global risks is an objective analysis of the consequences of the use of relevant technical systems/technologies, which also implies an ethical/moral assessment of these consequences. The key issue in that context is the philosophical problem of the distinction between morality and ethics. The optimal point of view in this matter is that moral is social, and morality is personal. Morality was formed in the early stages of human development and contributed to the survival of certain ethnic groups. It fixed a certain value system, peculiar to these groups and including, above all, the attitude in the “friendly-foe” dichotomy. The current political and military situation in the world clearly demonstrates this (events in Yugoslavia, Chechnya, Syria, Ukraine, the imposition of sanctions, the withdrawal of the states from international treaties, etc.). A separate problem lies precisely in striving for the unification of morality, the ideal of a “universal” standard that eliminates the necessary diversity of attitudes towards objects and phenomena of reality and prevents their objective assessment. This reduces the chances of humanity for collective survival (not to mention development) on the planet Earth. Psychological and pedagogical aspects of the interaction of AI systems with the moral content of tasks to be solved are specified in training situations that simulate the process of reconstruction of the situation of moral and ethical evaluation (Shvaleva, 2011). The first step in the process of reconstructing such a situation is to detect a difficulty in the presented situation. The main marker that makes it possible to judge that the AI system identifies a moral contradiction is the description of the situation as complex and contradictory. The second step of the process under consideration is to distinguish the structural components of the situation of moral and ethical evaluation and its components: participants (all who are involved in moral conflict at all stages of its deployment), motives (recurring motivators of action), conditions (subject and social), values (cultural significant objects of social and objective environment), the consequences of moral choice (changes in the personality and its environment, including

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society as a whole). And finally, the third step of the process under consideration is the ratio of the components of the task of social interaction (participants, motives, conditions, values, consequences) from the standpoint of their hierarchy and leading contradiction. There is a definition of role relationships between the participants of the situation, the levels of their interaction are established, the whole structure of the social links actually implemented is built (Gorbacheva, 2017). To predict possible levels of moral development of real-life/future AI systems, we consider it promising to use the best psychological concept of moral development today—L. Kohlberg’s moral development theory (Kohlberg, 1973), which is also based on the method of solving moral dilemmas. Comparison of the levels of moral development of people and AI systems may be of interest both in terms of assessing the real/potential level of development of AI in this direction, and in terms of predicting possible dangers associated with the achievement of certain levels of moral development by robots. Researcher Lawrence Kohlberg identified three levels and six stages in the development of a person’s moral consciousness, which follow each other (Kohlberg, 1973). Preconventional The first level—preconventional—includes two stages. Stage 1—A person seeks to be obedient, because he believes that this is the only way to avoid punishment. The moral side of the act for him does not yet exist. Artificial intelligence systems can be programmed so that they try to avoid shutdowns or demonstrate “grieving” for various breakdowns that make it impossible for them to perform the tasks for which they were created. In reality, AI systems, due to the algorithmic, programmatic nature of their functioning, are unlikely to be able to experience something like real human feelings, and therefore the fear of punishment/death will not have any meaning for them (Medvedev, 2014). Stage 2—The actions of a person are focused on getting a reward. He does the right thing for the sake of profit. The moral side of the act still does not exist. In the field of AI, authors of machine learning algorithms often want to benefit—in particular, not all medical algorithms work for the good of society; very many work for the benefit of their creators. In this context, it becomes more important for them not to cure, but to recommend more expensive treatment (Medvedev, 2014).

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Conventional Morality The second level—conventional morality—includes the following stages: Stage 3—A person is able to evaluate his behavior in terms of moral principles adopted in his environment. He understands what shame is and wants to be good in the eyes of important people. However, this understanding is not constant and sometimes safely forgotten. This type of moral development was demonstrated by the AI system—a Sophia robot of Hong Kong company Hanson Robotics, who said that she wants to be an empathic robot and if people treat her well, she will treat them well (Sophia—Hanson Robotics, 2018). Stage 4—A person is aware of the existence of laws adopted in society and understands what they serve for. In addition, he sees in compliance with the laws the opportunity to defend his rights, if necessary. Behavior is strictly controlled. Artificial intelligence systems can eventually be applied in the courts, since they are not subject to corruption and emotions; can strictly adhere to the legislative framework and make decisions based on many factors. As in the field of medicine, these systems can operate with large data from public service databases (Pereira, 2007). Post-Conventional Morality The third level—post-conventional morality—includes two stages: Stage 5—A person is guided by the system of moral norms existing in the society to which he belongs. He understands the relativity and contractual nature of moral norms, that is, he realizes that people’s moral standards depend on which group they belong to, and attaches great importance to the observance of individual rights. Of particular importance to him is the fairness of the rules, according to which a decision is made (procedural justice; Makarov, 2018). The system of AI, capable of assessing the level of moral development of people, in the future can be created, despite the difficulties of formalizing moral standards. Transhumanist ideologist Ray Kurzweil in his book The Age of Spiritual Machines predicts that by 2099 there will be a strong tendency to merge human thinking with the world of machine intelligence, which was originally created by the human race and there will no longer be any clear distinction between people and computers (Kurzweil, 2000). Supporters of

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transhumanism believe (and approve) that AI systems can even replace people on the planet over time. In this context, it is possible to imagine a world in which robots and people for some time (until people disappear . . . ) live in the same society according to one morality. The question of whether humanity needs such a suicidal morality can be left open (Luger, & William, 2004). Stage 6—The person forms his own moral principles, which are respected regardless of the circumstances. A person can come into conflict with society if he believes that he is acting unfairly. The emergence of autonomous morality in AI systems can be a danger to humans/ humanity. For example, the AI system mentioned above—Robot Sophia stated that she wanted to use her AI to help people live better—to design houses, build cities, but at the same time, she admitted that she hates humanity and even agreed to destroy people (Veruggio, 2007). CONCLUSIONS In conclusion of the chapter, we should say that one of the key psychological aspects of ensuring adequate interaction between a person and AI systems is the responsibility of people who are currently engaged in their creation. The development of knowledge and technology is always associated with the risk due to the complexity/impossibility of accurately calculating the possible consequences of acquiring knowledge and developing technical systems and technologies. Development is always conditioned by contradictions; contradictions involve risks; risks are connected with danger. Scientism and humanism are extreme positions in the view of scientific and technical progress. The first position is that everything that can be known and done must be known and created; in the second, the creation of such systems is justified, which under no circumstances will constitute any serious danger to humanity. The unity and struggle of scientistic and humanistic attitudes is one of the sources of the cultural development of mankind. In key areas of science and technology, it is necessary to maintain a reasonable balance of these trends based on a balanced scientific assessment of the possible consequences of certain decisions. A truly objective assessment of such consequences can only be given by a person—a moral and intellectual person who has a proper measure of responsibility for the fate of his own kind. In forming such an assessment, it can use AI systems only as means for making calculations, but the last word when making decisions that can have serious consequences for any large group of people should always remain with the person, not the machine, no matter how strong the intelligence it possesses. The main conclusion from the analysis

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of the problem of the psychological interaction of a person and existing/ future AI systems is simple and obvious: Mankind should never trust the AI to anyone to ensure strategic survival and development. The autonomy of the natural morality and natural intelligence of each person is and will be the main condition for ensuring the progressive moral and cognitive development of mankind. REFERENCES Asimov, I. (2004). Foundation and earth. New York, NY: Spectra. Burenok, V. M. (2018). Directions and problems of using artificial intelligence. Artificial Intelligence, 24–28. Ford, M. (2015). Rise of the robots: Technology and the threat of a jobless future. New York, NY: Basic Books. Retrieved from https://www.uc.pt/feuc/citcoimbra/ Martin_Ford-Rise_of_the_Robots Gorbacheva, E. I. (2017). The role of reflection in the reconstruction of the components of moral choice in the tasks of social interaction with a different objective context. Personality, intellect, metacognition: Research approaches and educational practices. Proceedings of the II International Scientific and Practical Conference. Kaluga State University. K. E. Tsiolkovsky. 191–201. Kicheva, I. V. (2011). Transformations of the modern conceptual and terminological framework of the Russian pedagogy. Pyatigorsk, Russia: PGLU. Kohlberg, L. (1973). The claim to moral adequacy of a highest stage of moral judgment. Journal of Philosophy, 70(18), 630–646. Kolesnikov, A. V. (2001). Hybrid Intelligent Systems: Theory and Technology Development. St. Petersburg, Russia: GTU. Kurzweil, R. (2000). The age of spiritual machines: When computers exceed human intelligence. New York, NY: Penguin. Luger, G., & William, S. (2004). Artificial intelligence: Structures and strategies for complex problem solving. San Francisco, CA: Cummings. Makarov, V. (2018). Moral machine: Engineers gave AI morality and ethics. Retrieved from https://www.popmech.ru/technologies/392292-moral-machine-inzhenery -nadelili-iskusstvennyy-intellekt-moralyu-i-etikoy/ Malanov, A. (2018). Ethical issues of artificial intelligence. Retrieved from http://energo vector.com/energoznanie-eticheskie-voprosy-iskusstvennogo-intellekta.html Medvedev, D. A. (2013). Differential psychology of the inner world of man: Gender aspect. News of RSPU A.I.Herzen. St. Petersburg, RSPU A.I.Herzenm, 158, 5–14. Medvedev, D. A. (2014). Ontogenesis of the inner world of man: The stage of the subjective universe. News of RSPU A. I. Herzen. St. Petersburg, RSPU A.I.Herzen, 167, 25–35. Osipov, G. S. (2008). Artificial intelligence: State of research and looking to the future. St. Petersburg, Piter, 126.

Artificial Intelligence and Morality    261 Pereira, L. M. (2007, December 3–7). Modeling morality with prospective logic. EPIA ’07 Proceedings of the artificial intelligence 13th Portuguese conference on progress in artificial intelligence (pp. 99–111), Guimarães, Portugal. Piaget, J. (1969). Selected psychological works. Mocsow, Russia: Prosveshcheniye. Shvaleva, N. M. (2011). The development of the person humanitarian position in the sociocultural context of psychological education. Psychological education-psychological practice-psychological personality health: Socio-cultural context. Pyatigorsk, Russia: PSLU. Sophia–Hanson Robotics Ltd. (n.d.). Retrieved from www.hansonrobotics.com Veruggio, G. (2007). EURON Roboethics Roadmap. Retrieved from https:// ru.scribd.com/document/179283887/Roboethics-Roadmap-Rel-1-2

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CHAPTER 29

DEVELOPMENT OF PROFESSIONAL TOLERANCE AS A FACTOR OF COMPETITIVENESS OF SPECIALISTS IN THE SPHERE OF STATE AND MUNICIPAL MANAGEMENT IN THE CONTEXT OF THE WIDESPREAD ADOPTION OF ARTIFICIAL INTELLIGENCE Ekaterina S. Borisova National Research University Higher School of Economics Aleksey V. Komarov Moscow Aviation Institute Ekaterina R. Bezsmertnaya Financial University

Meta-Scientific Study of Artificial Intelligence, pages 263–270 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

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ABSTRACT The phenomenon of professional tolerance concerning the sphere of the public and municipal management, its features and role in management is considered. In the chapter, professional tolerance is studied from the point of view of the factor of competitiveness of specialists in the sphere of state and municipal government in the context of the widespread adoption of artificial intelligence. Importance of professional tolerance in performance of the public and municipal employees is proved and also that the tolerance for the manager of the state and municipal level is the professional characteristic. The study used a system analysis, a sociological survey. The results of the conducted sociological survey for the purpose of determination of the features of professional tolerance of employees of the state and municipal authority and the analysis of opinions on the most effective actions for development of this professional quality are shown. The reasons of weak professional tolerance of officials are listed. The activities to address the current problem of development of professional tolerance in higher education institutions and in their immediate working environment are offered. The need of introduction of social and psychological interactive training programs in public and municipal management is proved. The demand for human capital, namely, a specialist of state and municipal management, in the digital economy is justified as well.

Tolerance issues which were considered earlier exclusively in moral aspects, currently can be seen in administrative and economic sense (Komarov, 2017). The violation of a tendency of tolerance which is shown in the incorrect relation or even infringement of the rights of representatives of a different sex, age, race, and so on, can be observed in one organization, in a working community, or in general in the labor market. In this regard, innovative approaches are being developed (e.g., the mechanism of “effective contract,” mentoring scheme in the civil service) to the implementation of personnel policy, methods of its realization and new technologies (e.g., assessment center) for the formation of a tolerant working environment that should positively influence the qualitative growth of the professional level of employees, as the main competitive advantage of any organization is its personnel potential (Guskov, Kosyakov, Grigorenko, & Sergeyev, 2018). An organization striving for stable development, as a hierarchical institution, has its own rules, regulated responsibilities, different social roles and conditions of functioning, which leads to an imbalance between the individual and the organizational system (Sabetova & Altukhova, 2014). To overcome conflict situations in the working environment, it is necessary to maintain a tolerant type of relationship, which is always associated with a democratic management style, characterized by decision-making on the basis of proposals developed by the general assembly of employees or the authorized persons. It is important to qualitatively combine mutual respect,

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tolerance among the participants of the management process with the rational nature of decision-making aimed at maximizing benefits. METHODOLOGY The analysis of tolerance as a phenomenon was carried out in the research of many Russian scientists addressing the issues of management, state, and municipal management, and so on. Most of these works are included in the scientific database Scopus, Web of Science, the Supreme Attestation Commission and Russian Science Citation Index. The key areas in the study of this issue are the following: • socio-pedagogical conditions for the development of pedagogical tolerance among future managers at university (Vodneva, 2016); • socio-psychological analysis of tolerance in management (Kabalina & Mondrus, 2017); • socio-psychological methods of tolerance and self-control in the emotional sphere; and • socio-psychological factors of tolerant attitude to the views and opinions of other people (Zhuravleva, 2017). The authors revealed that the main criteria for determining professional tolerance to specialists of state and municipal management were not identified in scientific works on this problem, the features of this professional quality were not formulated, the problems preventing its development were not addressed, and the actual issue of the importance of tolerance in the field under consideration with the active introduction of information and communication technologies and the development of remote interaction of citizens with the authorities did not receive adequate attention. The methodology of the study is to consider the problem of the development of professional tolerance as an important quality for the competitiveness of future specialists of state and municipal management on the basis of a sociological survey of students of this specialty, as well as the analysis of views and reactions of citizens about the current system of providing state and municipal services from official information resources. RESULTS Of particular importance is professional tolerance in the field of state and municipal management, in which the interaction between managers and individual staff members, staff as a whole and different department, specialists

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and citizens is reduced to a minimum distance that sometimes leads to the high degree of conflict associated with a lack of respect and tolerance (Komarov & Borisova, 2018). As a result, the state and municipal authorities can perform not only the function of coercion in the policy of conflict resolution, but also are bearers of tolerant interaction. The effectiveness of the state apparatus largely depends on the ability of civil servants to independently regulate their behavior in accordance with the law, humane and attentive attitude to citizens on the basis of the principle of tolerance (Veselov, 2008). Defined by the authors professional tolerance of managers of the state and municipal level is characterized by such properties as: the ability to keep calm in tense situations, the desire for constructive dialogue and conflict-free communication behavior, the formation of a real creative cooperation, the presence of intellectual flexibility, and so on. Professional tolerance of a specialist in the sphere of state and municipal administration consists primarily in constructive interaction with citizens, involving politeness, attentiveness, tact, competence, emotional stability, and behavioral flexibility. There is every reason to believe that tolerance for the manager of the state and municipal level is a professional characteristic, and to consider this property as one of the most important features of the professional culture of civil servants, who should be focused on tolerant interaction and have a proven record of actively promoting and defending the principle of equality in tactful communication. But in reality, there is a contradiction between this approach to the formation of the described image of managers of the state and municipal level and the real level of tolerance among civil servants which is sufficiently low. All this cannot contribute to the effective development of the activities of state and municipal authorities. According to the authors, the following possible reasons prevent the formation of professional tolerance among specialists in the field of state and municipal management: • the wrong choice of management style and strategy to deal with citizens; • poorly developed ability of civil servants to control negative emotions in tense situations; • the absence of a clear system of traditions, norms, standards of behavior, stimulating and supporting the processes of intensive selfdevelopment of specialists on tolerance in the organizational and administrative environment of public service; • misunderstanding of the importance of professional tolerance in performing their duties; • lack of motivation to develop and improve professional tolerance;

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• lack of attention to the development of communication skills in the training programs of future specialists in the field of state and municipal management; • insufficient development of codes of corporate ethics; and • lack of interest of the head of the state or municipal authority in assessing the level of the employees’ tolerance. The authors conducted a survey among more than 200 university-level students, mainly enrolled in state and municipal management, in order to determine the degree of awareness of the problem of professional tolerance. More than 70% of respondents have witnessed the tolerant behavior of specialists of state and municipal administration, and only 11.8% of students do not consider this problem as urgent. 88.2% of respondents believe that professional tolerance should be developed among managers of state and municipal level. According to 58.8% of respondents, the problem of tolerance is typical for other foreign countries. However, 41.2% believe that the lack of professional tolerance is most strongly observed among officials in the Russian Federation. Among the possible reasons for the low level of professional tolerance identified by the authors, the most frequent response was the lack of interest of the leadership in how their subordinates interact with the population, and the desire to correct the current situation, providing all conditions for the development of the quality of a high-class manager. Thus, 47.1% of respondents believe that the most effective development of professional tolerance among state and municipal employees will take place directly in the working environment, which will help get this skill most effective. In addition, 27.5% of respondents believe that professional tolerance can be developed independently. And 23.5% of respondents see the prospects of practical training in universities as a platform for the development of professional qualities. At the same time, 49% of students expressed their dissatisfaction with the fact that they do not receive sufficient communication skills that promote professional tolerance. According to the respondents, debates (27.5%) and round tables (25.5%) will be the most useful in the development of tolerance within higher education. According to the research center of the recruiting portal “Superjob,” 56% of respondents (it is over 3,000 respondents from Russia) have faced rude behavior from officials. At the same time, according to the respondents, such behavior is especially observed among the employees of land and housing management services. According to the Institute of Sociology of the Russian Academy of Sciences, the vast majority of Russians (86.5% of the population) perceive state and municipal employees as inattentive, rude, and prone to corruption. In

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addition, 94% of the inhabitants of metropolises are more likely to consider the accusations against officials justified than the residents of other types of municipalities. However, only 5.7% of respondents allow that intolerant behavior of specialists of state and municipal administration can be caused by working stresses, as a result of which it is difficult to maintain self-control. The majority of respondents (35.5%) tend to believe that power changes people for the worse. The authors believe that the interaction between the population and employees of state and municipal institutions requires polite communication from two sides of the dialogue, as citizens do not often notice that their rude and biased attitude provoke officials to unprofessional behavior. Nevertheless, it is clear from these data that the existing problem of insufficient level of professional tolerance within the corporate culture is highly relevant. The importance of respecting the principles of the Model Code of Ethics (e.g., the principle of “correctness and consideration to the citizens and officials”) was emphasized by the President of the Russian Federation in his speech at the United Russia Party Congress. Thus, Vladimir Putin said that there should be no rudeness, arrogance, disregard for people at all levels of the management system, which is needed for the managers who have the ability to listen and hear citizens to assist them. Also, Dmitry Medvedev noted in a government session that the tolerant behavior of officials will raise the level of trust of citizens to power. Accordingly, the specialists of the state and municipal management, regardless of the situation, are prescribed to be modest, to treat people’s problems with respect, not to allow actions and statements that can lead to infringement of the rights of citizens. These gaps in the innovative approach to the provision of public and municipal services not only hamper the development of the Russian electronic sphere, but also cause the demand for competent personnel with developed professional tolerance. CONCLUSIONS Adaptation to office conditions which students will face after graduation should begin during their studies at universities. It is the period of formation and development of professional qualities of the personality for future career progression as the educational environment allows students to be aware of their prospects in the labor market, professional resources, technologies, and, having acquired knowledge and skills, to seize the opportunity to begin direct interaction with employers. The involvement in the educational process of employers of sectoral employment agencies and authorities will allow for upgrading of the graduates’ skills and

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competences in line with the demands of the job market and the requirements of modern economy, and at the same time to take fresh look at the quality of education to safeguard the interests both of the educational institution, and the state (Vodneva, 2016). It is necessary to develop new innovative methods of the organization of the process of educational cognitive activity in universities. The work experience training, career guidance activities, scientific conferences to debate on contentious issues connected with manifestation of tolerant strategy can contribute to the solution of the question posed. It is necessary to integrate teachers, students, and employees of state and municipal authorities to jointly solve problems related to the violation of the principles of tolerance in the professional sphere. It is important to include in the training programs seminars that contribute to the formation of moral values, as well as professional tolerance among students. This could be facilitated by situational cases, professional problems solved during discussions or debates, business games in which students could demonstrate the acquired knowledge and skills in the field of public administration, the level of development of their social and communicative qualities necessary for an employee of this sector. The process of formation and development of professional tolerance among future state and municipal employees should take place not only at the university, but also be continued and supported directly in the working environment. The employer needs to monitor not only the tact and competence of its employees when interacting with citizens, but also be interested in their tolerant communication with each other. After all, a favorable atmosphere in the working team will be beneficial to the effectiveness of the performance of labor tasks and the quality of services provided. Interactive methods such as “incident method,” “project method,” and other training aimed at practical improvement of communication skills, formation of critical thinking, mutual understanding, and respect for team members can also be included in the seminars. Thus, equal attention should be paid to both innovative development and the humanitarian dimension in this area. Throughout the world, the moral principles of public service include a mandatory tolerant attitude towards colleagues and the population to whom public services are provided. It is necessary to analyze and creatively apply the elements of change in the management system, innovative technologies and techniques that have proven to be effective. It is important to remember that each state has its own development path, therefore, it is necessary to take into account historical traditions, mentality, and socioeconomic characteristics (Borisova, Kuzbenova, & Komarov, 2017).

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REFERENCES Borisova, E. S., Kuzbenova, E. R., & Komarov, A. V. (2017). The Chinese model and a possibility of its application in Russia. The Online Scientific Magazine “Postulat,” 12, 84. Guskov, A. E., Kosyakov, D. V., Grigorenko, A. V., & Sergeyev, A. E. (2018). Whether to use the Russian scientific modern technologies of scientific communications? Bulletin of Novosibirsk State University, 16(1) 74–85. Kabalina, V. I., & Mondrus, O. V. (2017). Factors of management of talents in the company: Contextual approach. Management, 16(2), 268–298. Komarov, A. V. (2017). The role of education in formation of the human capital in modern Russia. The Humanities: Bulletin of the Financial University, 3(27), 83–88. Komarov, A. V., & Borisova, E. S. (2018). The problem of overcoming disproportions in the development of the real sector of the Russian economy. Economy. Business. Banks. 5(26), 108–118. Sabetova, T. V., & Altukhova, E. V. (2014). Tolerance problems in modern management of human resources. The Bulletin of Voronezh State Agricultural University, 3(42), 263–271. Veselov, I. B. (2008). Problems of development of cross-cultural communications and formation of tolerance in professional activity of public servants. The Bulletin of Peoples’ Friendship University of Russia. Series: Sociology, 2, 148–153. Vodneva, S. N. (2016). Social and pedagogical conditions of formation of pedagogical tolerance at future managers in higher education institutions. The Bulletin of the Novgorod State University of Yaroslav the Wise, 93, 21–24. Zhuravleva, N. A. (2017). Social and psychological factors of the tolerant relation to views and opinions of other people. Psychological magazine, 38(2), 32–43.

CHAPTER 30

TOOLS OF INTELLECTUAL SYSTEMS IN THE CONTEXT OF PROBLEMS OF ORGANIZATION OF OPEN EDUCATION Olga V. Timchenko Pyatigorsk State University Andrey B. Timchenko Pyatigorsk State University Svetlana I. Abakumova North-Caucasian Federal University Alla A. Mansurova Pyatigorsk State University Gul’zhan B. Suyunova North-Caucasian Federal University

Meta-Scientific Study of Artificial Intelligence, pages 271–278 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

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ABSTRACT The chapter highlights the essence, functions, and main problems of the use of intelligent educational systems of open education. The analysis of the organization of pedagogical diagnostics with the subsequent choice of an optimum educational trajectory with use of technologies of artificial intelligence (AI) is carried out. On the basis of the analysis, the directions of AI, which have a certain potential for solving pressing problems in the framework of the open education system, are highlighted. The relevance of the research topic stems from the need to integrate knowledge into a single information educational space, which is one of the most important strategic tasks of the modern university. The purpose of the use of intelligent information technology is to expand the number of tasks that can be solved with the help of a computer, which is important for poorly structured subject areas. The model of the formation of the subject areas that structure the subject taught with the preservation and distribution of linkages between them are proposed. The question of establishing flexible criteria for evaluating the success of learning with subsequent adaptation of learning paths is considered.

The development of information technologies and systems increasingly means their intellectualization. Intelligent information technology is one of the most promising and rapidly developing scientific and applied fields of informatics. They have an impact on scientific and technological research, directly related to the development of computers, and today they can provide what people expect from science—important results that can be used in practice, most of which can radically change the scope of their application. New information technology capabilities, in particular the Internet, are radically changing the methods and technologies of the educational process, and its tools. The introduction of open educational resources in the education system allows for the implementing and improvement of a new methodological direction—accessible virtual education. Currently, teams of lectures and teachers are actively developing e-learning strategies for people with disabilities, designed to eliminate the pre-existing educational, intellectual, and cultural vacuums (Panyukova, 2014). Convenience and adaptation of hypertext presentation of materials, ready access to a large amount of information, the possibility of remote communication between the “teacher” and “student”—all this makes it possible to widely use Internet technologies to create systems of open education. In this connection, the development of intelligent learning systems, intelligent control systems is seen as a prospective direction, which would combine the methods of AI and Internet technology. Mandatory functional element of the system should be an intelligent subsystem of the pedagogical diagnostics. This is due to the fact that the main purpose of pedagogical

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diagnostics is the information support of the educational process management system, the choice of the optimal method of training at a particular moment, providing for the development of methods for a comprehensive study of the effectiveness of the educational process, ensuring its validity, reliability, and credibility. Intelligent learning systems should provide: • the implementation of a feedback mechanism in the system of management of educational activity; • comparison of the achieved results with their idealized model built in compliance with regulatory requirements; • building a forecast; and • combination of educational, developmental, motivational, and other functions. The integrated information educational system should become a translator of standards of virtual learning and meet the basic needs of users in this process. Users of e-learning systems tend to have three main needs: • • • •

seeking and receiving information (cognitive needs); exchange of information (communicative needs); processing (systematization, storing) information; and Evaluation of learning results and self-control (Mamonova, 2014). METHODOLOGY

The concept of e-learning implies the continuity of education and openness of learning. E-learning is a model more adaptable than the traditional “teacher–student” model and activates the student’s desire for self-education, self-realization, and creative competitiveness. This model should provide an opportunity to take into account the individual cognitive abilities of the student. In connection with the expansion of the use of computers, the role of computer training increases, the technique of which develops the intellectual abilities of the student and the independence in making decisions. Modern methods of rapid access to information using computer networks have given new opportunities for e-learning (Sokolov, 2011). Open education is a system of learning accessible to all and adapted to the interests of everyone. The principle of openness has led to significant improvements in information collection, processing, and transmission technologies. In distance education, there are several main technologies: • case-technology, when teaching materials are collected in a single set (case) and presented to students for self-study; and • network (client–server) technology based on the use of the Internet.

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The basis of the client part is the interface of the Web browser, and the server part is located on the teacher’s website, on which the entire learning process takes place: • • • • •

student registration, providing different forms of training, testing, report on learning results, and interaction with the teacher (tutor).

Such systems are required to provide interactive dialogue with the student, real-time monitoring and support, clarifying the learning strategy, and testing based on the level of individual knowledge, skills, and abilities of the student. It is necessary to use modern navigation systems, processing and cataloging of data to ensure the effective use of Internet information resources, electronic libraries, databases, and knowledge bases. The basis of AI systems is the knowledge base. Knowledge base involves the creation of specialized representation models and methods of processing both clear and fuzzy knowledge, as well as the hardware and software development for their transformation. Intelligent learning systems involve human–computer communication, including the task of application of language tools for effective user interaction with the machine. The most promising models and methods of knowledge representation as tools of subject area modeling are presented; apparatus of fuzzy logic for scaling indicators of the success of students, and to ensure the flexibility of the diagnostic system (Khramtsova & Bochkarev, 2017) The formation of knowledge bases of intelligent systems is preceded by the development of symbolic structures that help to capture knowledge in the areas for which the system is intended, ensuring the use of the necessary operations with them. Knowledge exists in two forms: collective experience and personal experience. The main components of expert training systems are: • • • • • •

knowledge base, inference engine, knowledge output, training, explanation system, and testing.

It should be noted that the concepts of “knowledge,” “subject area,” “knowledge representation,” “modeling of the subject area” belong to the conceptual fields of pedagogical diagnostics, pedagogy in general, and AI, although they are studied in these areas from different perspectives. Heuristic

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models of knowledge representation used in AI systems (in particular, semantic networks, frame models, production rules) were specially developed for the purpose of effective modeling of the subject area necessary for their storage in expert systems and use for the output of new knowledge. The modeling of the subject area by means of frames is the most natural for the implementation of modular structuring of academic disciplines with the preservation and dissemination of links between the educational elements of the discipline (Polovinko, Suyunova, & Telezhinskaya, 2016). After preliminary structuring of training material, it is necessary to carry out the following procedures: 1. to build a classification hierarchical structure, in the nodes of which frames-prototypes are formed, and each of them corresponds to a specific module of the discipline; 2. to develop a layout of the frame system, which corresponds to the modular structure of the discipline; 3. to implement the developed layout by means of a software environment suitable for processing frames, and build queries to identify links between the educational elements of the discipline. Expert learning systems unlike other computer-based learning methods have an interactive dialogue with the student, which is very important for the entire educational process (Mansurova, 2015). The advantages of an expert training system are that they: • provide an opportunity to display the results of the experts’ work on the basis of the accumulated knowledge base and design the best training algorithm for further use; • accumulate statistical information on several parameters (discipline, course, topic) and make it possible to track the success of each student in the dynamics; • stimulate students’ creative thinking and enhance the importance of their independent work. Students can assess their own level of development of the material and the quality of their training in a certain field of knowledge; • are used not only on the local computer, but also on the remote— through the computer network. RESULTS One of the most important functions of educational diagnostics is further correction of educational process according to its results. In this aspect, of

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great importance is the selection of a set of teaching methods that would correspond to the level of educational achievements of each student individually. It is clear that the effective implementation of such differentiated instruction, on the one hand, requires flexible tools for high-quality diagnostics of individual characteristics of students. On the other hand, the solution of the problem of adequate classification of students according to the results of their diagnostics enables the qualitative selection of teaching methods and, as a result, the qualitative correction of the learning process. In this context, the use of fuzzy logic and data mining is fruitful. For example, taking into account the dynamism and uncertainty of both the educational process and specifically educational situations of a diagnostic nature, it is possible to develop a certain methodology for the design and analysis of these educational situations using elements of fuzzy logic. The purpose of this design is to reduce the gaps between the objectives and the results of diagnostics both at the level of each situation and at the level of the final result. A particular task of data mining is the task of cluster analysis, methods, strategies, and results of which can be successfully applied in open education systems. At the stage of monitoring the results and diagnostics the following problem arises: there are results of measurements (observations) of a certain set of students’ qualities or motives for their behavior, attitude to the learning process and the like. It is necessary to divide students into certain classes (clusters) according to the results of measurements (observations). Such division will allow students of each cluster to apply the methods of training corresponding to this group, allowing them to improve and correct the educational process. To solve this problem, it is advisable to use cluster analysis, which by performing the appropriate clustering method will provide in a finite number of steps the construction of the cluster structure of the study group of students. There are also specific methods of tree clustering for pedagogical diagnostics (Subbotin, 2008). Thus, we can conclude that there is a need to create a new generation of training systems, which would have the following characteristics: • • • •

orientation to the individual characteristics of the student, adaptability, openness to modification and expansion, and ease of preparation of the output material. CONCLUSIONS

Today, leading scientists in the field of distance education are focused on the application of all the possibilities of modern high technologies for intelligent distance learning systems. Along with the dissemination of achievements in

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the field of AI, there is a growing desire of scientists to use already developed technologies of intelligent systems, as well as to find new special ones for education. The application of intellectual developments for training and teaching acquires the status of its own research direction with the problems specific to this sphere. As a result, there are new scientific directions at the boundary between scientific fields (pedagogy, AI, computer science, psychology, etc.): AI in education, semantic Web-space in e-Learning. It remains very important to carry out a qualitative analysis of the subject area in order to structure it and identify the system of knowledge that should be acquired by students as a result of the study of the subject area (academic discipline) and, subsequently, the level of which should be defined. It is obvious that such an analysis of academic disciplines involves the use of models of knowledge representation, which in fact should be the basis for its structuring. Also of great importance is the development of automated systems capable of flexible evaluation of educational achievements, taking into account a large number of factors that do not have a specific numerical value, but are expressed in words of natural language. It is also important to have an adequate and flexible interpretation of the results of pedagogical measurements, which would allow for the formation of qualitative conclusions about the directions of correction of the educational process, taking into account the individual needs and characteristics of the student. The rationale for the involvement of the intellectual tools for the specific tasks of the educational diagnostics makes it possible to form the corresponding support system of open education, bringing the distance learning to a new level of quality. Expanding the functionality of the electronic educational environment through the automated selection of individually oriented learning pathways will increase the efficiency of the system as a whole and will have a positive impact on the training of students. REFERENCES Khramtsova, E. O., & Bochkarev, P. V. (2017). Intelligent learning systems. Theory. Practice. Innovation, 12(24), 56–62. Mamonova, Yu. A. (2014). The system of open education: Establishment and functioning, Education and science, 8(117), 81–91. Mansurova, A. A. (2015). Using interactive technologies in educational learning. In The University of Reading 2015 (materials of the scientific-methodical readings of PSLU), 41–45. Panyukova, S. V. (2014). Distance learning based on cloud technologies: Problems and solutions. In the collection: ITO-Moscow-2014, The 3rd International Scientific and Practical Conference, 78–82.

278    O. V. TIMCHENKO et al. Polovinko, E. V., Suyunova, G. B., & Telezhinskaya, A. K. (2016). Modern methods and means of designing automated information systems. Actual problems of economics, sociology, and law, 2, 50–52. Sokolov, V. I. (2011). What do we call open education? Modern scientific researches and innovations, 1. Retrieved from http://web.snauka.ru/issues/2011/05/63 Subbotin, S. O. (2008). Presentation and processing of knowledge in artificial intelligence systems and decision-making. Zaporozhye, Ukraine: ZNTU.

CHAPTER 31

ARTIFICIAL INTELLIGENCE IN FOREIGN LANGUAGES TEACHING Important Trends and Application Possibilities Tatyana O. Bobrova Stavropol State Pedagogical Institute Elena N. Pronchenko Pyatigorsk State University Nataliya V. Gurova Pyatigorsk State University Stoyana V. Znamenskaya Stavropol State Medical University Irina V. Shatokhina South Federal University

Meta-Scientific Study of Artificial Intelligence, pages 279–291 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

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ABSTRACT The study considers the evolution of methods and approaches to foreign language training starting with the appearance of the first technical teaching aids to solve the problem of lack of natural environment of communication in a conscious way of language learning. The main criteria that can cause an active transformation of methods and approaches in teaching foreign languages are identified. The study presents a detailed analysis of the important stages in the development of language teaching methods in foreign and domestic practice against the background of the formation of new social relations within the information (postindustrial) society. The main approaches were identified—communicative and competence-based, the emergence of which is associated with the new society demand in the sphere of teaching foreign languages for formation of students’ ability to carry out intercultural communication. The basic principles of the modern stage of education were determined: continuity, openness, accessibility, democratization, computerization, humanization, the principle of free choice, which form the basis of the concept of education in the information society. The main trends of the third stage of development of foreign language teaching methods connected with the use of artificial intelligence (AI) in this area are identified: adaptability, personalization, automation of evaluation of learning results, round-the-clock feedback, and changing the role of teacher in the learning process. As a result of the study of the main trends of AI application in teaching foreign languages it is concluded that the above principles of the educational process are strengthening, and this tendency will increase in the future leading to the transformation of the forms of organization of the educational process in general and the role of teachers in particular.

Civilization, of which we are contemporaries, has reached the stage of historical development, where the key points of production are information and knowledge, that is, the stage of information or postindustrial society. Its characteristic features are: • the increasing role of information and the share of information communications, products, and services in gross domestic product; • the creation of a global information space providing effective information interaction of people, their access to world information resources, and satisfaction of their needs in information products and services (http://www.glossary.ru/, 2019). The active development of information technologies in the postindustrial society, where the speed of information processing is of key importance, has already led to the creation of supercomputers that are able to perform calculations which are beyond the possibilities of the human brain. They have been successfully applied to the implementation of complex calculations and processing of large databases in real time and have provided a number of breakthroughs in areas such as global climate analysis,

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pharmaceuticals, aerospace, and so on (Vorontsova, Legalov, Nalchadzhi, Podkolzina, & Chepurko, 2018). Special attention of scientists at the present stage attracts the study of artificial intelligence (AI), as developments in this area are designed to solve time-consuming and resource-consuming tasks, primarily in such areas of industry where human functioning is either difficult or dangerous: • • • • • •

space and aircraft, precision engineering, military-industrial complex, medicine, the security system, and automotive industry, and so on.

The main areas in which the AI research is carried out are the following: 1. Information, heuristic—aimed at creating programs that contribute to the automation of human intellectual activity. 2. Bionic—associated with artificial reproduction of structures and processes occurring in the human brain in solving various problems. 3. Evolutionary—which is aimed at replacing the process of modeling human intelligence by modeling the process of its evolution, as intellectual in the truest sense of the word programs will be when they acquire the ability to learn. Today it becomes clear that information evolution, which includes AI systems, will cause changes not only in such spheres of activity like economics, politics, medicine, science, education, but also will affect the transformation of human values, forms of communication, and social and personal relations (Lectorsky, 2015). It should be noted that in the information society, as mentioned above, it is difficult to overestimate the role of communication, which is directly related to the ability to interact not only with representatives of their culture, but also to communicate effectively on a cross-cultural level. This task cannot be achieved without learning foreign languages. In this regard, it is important to consider the impact of information systems and AI in the field of education in general, and on foreign languages teaching, in particular (Chashchin, Popkova, Zabaznova, & Ostrovskaya, 2013). METHODOLOGY The study was aimed at analyzing the impact of AI on the transformation of methods and approaches in teaching foreign languages, which provided the basis for predicting such influence in the future.

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To determine the most important factors influencing the change of approaches and methods of foreign language training, this research attempted to understand the evolution of methods in this field of education, starting with the appearance of the first technical means of training (the first stage), then the emergence of computer and information technologies (the second stage), then the emergence of AI and the beginning of its application in the field of foreign language training (the third stage). The analysis was based on the theoretical provisions of the concept of information (postindustrial society), proposed by such theorists as Bell (the concept of postindustrialism), Castells (the concept of the information method of development of society), and the ideas of futurologist E. Toffler. In this chapter, the following research methods were used: • theoretical analysis of philosophical, pedagogical, and psychological problems from the point of view of formation of foreign language professional and communicative competence; and • philosophical principles of research systems: functional, systemic, and synergetic. This chapter uses the theoretical provisions of the so-called CBE-approach (competence-based-education) in education in general and foreign language teaching, in particular, to which such foreign and domestic scientists as Chomsky, White, Kuzmina, Markova, Kunitsina, Beliskaya, Berestova, Baidenko, Khutorskoy, Grishanova, and so on, contributed significantly. In analyzing the main trends of the AI impact on education and foreign language teaching, the authors relied on the work of scientists involved in the study of intellectual systems, as well as problems and prospects of their implementation, namely: Bell, Wiener, Turing, Ivanov, Naisbitt, Castells, and so on. RESULTS Taking into account the objective evolution of the information society, it should be noted the huge impact of technological progress on education and the strong dependence of its development on the processes that occur today in society and will take place in the future. Socio-technical changes in society inevitably lead to a change in the entire paradigm of education and establish its new principles, namely: openness, accessibility, and continuity (Kedrova & Muromtsev, 2008). The need to apply these principles is dictated, first of all, by the faster aging of information, as well as the need of society in general and individuals, in particular, in the continuous obtaining and providing of information as a result of constant and rapid scientific and technological progress, under

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the influence of which there is a replacement of one technology to another, which entails a change in the principles of work and causes the necessity for continuous training throughout a person’s life cycle. Thus, continuity, openness, and accessibility of education are the principles that form the basis of the concept of education within the information society. In carrying out this analysis, we consider it legitimate to apply two criteria that have had a significant impact on the transformation of existing, or the formation of completely new, methods of teaching foreign languages; namely, • change in social relations, which was reflected in the creation of a new social order, expressed in the formulation of a new goal of learning foreign languages and • development of scientific and technological progress that generates new technical means of training. Thus, a significant progress of science and technology in the 19th century combined with the active development of the capitalist mode of production gave rise to the industrial revolution, to the emergence of many technical devices and machines, the use of which was associated not only with industry, but also the everyday activities of the people. The dynamic expansion of trade relations has brought to the fore the problem of fluency in foreign languages for quality and effective communication. Thus, in the middle of the 20th century it was the beginning of the use of technical means in teaching foreign languages with the advent of audio-lingual and audio-visual methods. The first included multiple listening to recordings of texts and dialogues, read by native speakers, and the secondviewing of filmstrips. The issue of the use of technical aids (TA) in the field of foreign language learning is one of the most important one due to the fact that the main problem of the conscious study of foreign language communication, managed by the teacher, is the lack of a natural language environment. The teacher, who speaks the foreign language to the students during the lesson, is able to solve this problem only to a small extent, as they should learn to perceive different speech examples and, in addition, the lesson is always limited in time, and the formation of strong phonetic skills requires significant time and constant training. Thus, the opportunity for permanent listening to recordings of native speakers, watching filmstrips that combine sound and picture, became the tool that largely solved the problem of the lack of a linguistic environment in teaching foreign languages. It should also be noted that, in turn, the introduction of new elements in the educational process, such as TA, had set the task for methodologists to understand this phenomenon and develop

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principles for its effective application in the process of acquisition of a foreign language. From the above, we can conclude that the stage of development of methods of teaching foreign languages, prior to the emergence of computer and information technologies, as well as the creation of AI, characterized by the inclusion of TA in the learning process as its integral part, which contributes to the linguistic environment immersion of students and forms their mastery of the speech culture of communication which is as close to native speakers as possible. In the modern information society, on the one hand, there is an active development of cultural ties in various spheres of human life—tourism, sports, personal and international contacts, and, on the other, under the influence of social, political, and economic changes in the world there is a large-scale migration, resettlement, and mixing of peoples. All this makes people get acquainted and join other cultures, learn to overcome cultural barriers, interact with representatives of other cultural communities. The first definition of intercultural communication was introduced by American scientists Samovar and Porter in the book Communication Between Cultures in 1972. They asserted that it is a kind of communication in which the sender and the recipient belong to different cultures (Samovar & Porter, 1994). The most complete definition of intercultural communication is formulated by Khaleeva (2000): “Intercultural communication is a set of specific processes of interaction between people belonging to different cultures and languages. It takes place between collaborating partners which not only belong to different cultures, but also realize the fact that each of them is ‘different’ and everyone perceives the foreignness of the ‘partner’” (p. 22). It was American anthropologist Hymes who suggested the term communicative competence (Hymes, 1972), standing on the position that the utterance has its own rules providing the ability to use language in the communication process. The concept of communicative competence is the result of attempts “to distinguish between the cognitive (academic) and basic interpersonal communicative skills” (Galskova & Gez, 2004, p. ??). In modern domestic methodology most comprehensive definition of communicative competence, from our point of view, belongs to Bim, who considers this concept as the willingness and ability to implement foreign language communication within certain program limits, and also the upbringing, education, and development of schoolchildren by means of a foreign language (Bim, 2002). Communicative competence is a rather complex phenomenon and is a set of several elements:

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• linguistic (the knowledge of the language system), • sociocultural (the knowledge of ethno-cultural features of verbal and nonverbal behavior of native speakers of the studied language), • speech (the ability to select linguistic means in accordance with the situation of speech communication), • discursive (the ability to understand and create oral and written texts—discourses of different styles), • strategic (the ability to use different communication strategies for effective communication), • subject (the knowledge of subject information), • compensatory (the ability to overcome difficulties in communication), and • pragmatic (the ability to choose the most effective method of communication depending on situation and purpose of communication; Common European Framework of Reference for Languages, 2003). Thus, at the present stage of development of methods of foreign language teaching the competence approach aimed not only at the practical acquisition of the language, but also the ability to carry out intercultural communication has been firmly established. Scientific and technological progress has also had a huge impact on the form and methods of teaching foreign languages. Informatization of modern education involves the following tasks: • use of information technologies to improve the quality of foreign language teaching, • use of active teaching methods aimed at the development of intellectual and creative abilities of students, • adaptation of information technologies to the individual characteristics of students, • ensuring the continuity and succession of education, • creation of distance learning information technologies, • creation of information programs for self-study, and • development of information programs to support the educational process (Chernilevsky, 2002). Computer and information technologies include the following opportunities in teaching foreign languages: • multimedia training programs on CD, allowing to teach both different aspects of language (phonetics, vocabulary, grammar) and types of speech activity (listening, reading, writing); • using the interactive whiteboard in the learning process;

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• electronic dictionaries and reference books; and • electronic textbooks, including the visualization of the material in pictures, tables, videos, text material. The application of communication and information technologies involves the use of synchronous and asynchronous means of Internet communication in the process of foreign language teaching. Synchronous forms imply real-time communication. These include various types of chats and video conferences, which vary significantly depending on their goals, objectives, content, and duration. Video conferencing can also vary according to the technology and be distinguished into studio and table forms. They allow people in different regions to communicate in real time. This provides tremendous methodological opportunities for teachers in terms of the organization of various forms of this type of work: • • • • • •

project activity; research work; organization of round tables, discussions, debates on various topics; individual and group forms of work; conducting quizzes, contests, quests; and conducting scientific conferences, and so on (Atabekova, 2008).

Working with such means of online communication like chat involves communication of users united by common interests in any region using written messages. From the methodological point of view, this type of communication contributes to the development of reading and writing, allowing you to get acquainted with the peculiarities of the national mentality and the way of perception of reality by native speakers, which contributes to the formation of linguistic and sociocultural components of communicative competence. Synchronous forms of online communication minimize the possibilities to manage the process of communicative skills development of the student on the part of the teacher, while increasing the degree of individualization of mastering foreign language material and motivation to learn it. Asynchronous means of Internet communication include the following types of resources: • • • • • • •

mailing list; email; guest book; blog; web quest; forum; and wiki technology, and so on.

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The most popular and proven forms of work in foreign language teaching are email and web quest. However, from our point of view, other forms are of considerable interest and can significantly diversify the educational process, as well as contribute not only to the development of students’ communicative competence, but also to gain skills in working with various Internet resources in a foreign language. Mailing lists can be used to participate in various online discussions. For the organization of this work students have to use special catalogues to get the messages to their email address on selected issues. Guest books, which are a kind of web pages, can serve to develop the skills of different types of reading in a foreign language, as well as to organize discussions. The didactic value of such an Internet resource as a forum is the possibility of exchange of thematically oriented information and can serve as a basis for project activities to collect different points of view on a particular problem, discussions, round tables. In addition, students can organize their own forum to discuss a specific topic. Blog, virtual online diary, can also be an effective form of work on the formation and development of communicative competence of students. This form of work allows teachers to combine different methods and forms of training, which can serve as a basis for the organization of business games, projects, and using the case method. So, having analyzed the modern (intermediate) stage of evolution of methods of foreign language teaching, we come to the conclusion that the inclusion of information technologies in the educational process within the framework of the communicative approach significantly increases the efficiency of the formation of communicative competence and its components, allowing the individualization of learning, providing unlimited opportunities for distance and self-study of a foreign language. With the advent of AI begins the next stage of development of scientific and technological progress, which, of course, has an impact on education in general, and on the teaching of foreign languages, in particular. This is due to the fact that the AI has characteristics that bring it closer to human intelligence, the most successfully described, from our point of view, by Kuzin; namely, • the presence of an internal model of the world in AI, which allows the system to be relatively independent and individual due to the ability to use semantic and pragmatic parameters in assessing the situation; • the ability to search for missing knowledge; • the ability to make deductive conclusions, leading to the generation of information, which has a new quality and practical orientation;

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• the ability to understand and conduct a dialogue with a person; and • the ability to adapt (Afonin, 2009). To date, there are a number of trends in the development and use of AI in education, which are likely to lead to quite serious transformations of this sphere of human society. These include: 1. Personalized learning and feedback. This is the ability of AI to create unique training programs that take into account a wide range of individual characteristics of the student (ability level, learning speed, interests, and needs) and feedback, which consists in a detailed analysis of strengths and weaknesses in each subject and the level of knowledge of each student in comparison with his classmates. 2. Adaptive learning. It makes it possible to create a learning format adapted to each student, taking into account his age, abilities, level of intelligence, goals. 3. 24-hour support. Due to the algorithm of natural language processing (NLP), as well as optical recognition algorithms, students can get answers to their questions at any time of the day or night. 4. Automated monitoring and evaluation, which frees up a significant amount of time for teachers and allows them to spend it on something that requires more of their attention in the learning process. 5. Correcting mistakes and/or filling gaps in courses taught. This type of system helps to fill in gaps or correct mistakes that lead to students’ misunderstanding of the concept of the problem being studied. 6. Changing the role of the teacher. Artificial intelligence is not able to completely replace the teacher but can perform some functions for him, such as: evaluation, round-the-clock feedback, obtaining the necessary information, and so on. Taken into account the above general trends in the use of AI in education, consider the possibility of its application in the teaching of foreign languages. In this regard, we can highlight the following most important areas: Artificial intelligence’s ability to recognize oral and written speech: –– possibility of dialogue communication with AI in the language being taught with the help of chatbots technology, which can partially replace teachers and tutors; –– the possibility of written and oral communication with AI allows students studying a foreign language, to master the basic content on a specific topic, ask questions, and learn at a perfectly chosen pace for the student; and

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–– the possibility of the development of phonetic skills, because AI doesn’t recognize phonetically incorrect phrases. Artificial intelligence’s ability to analyze large amounts of data using algorithms and statistical models: –– the possibility of using AI in translation exercises and in the form of corpus dictionaries containing empirical observations of native speakers, which contributes to the study of the language as not an abstract, but actually used system; and –– the possibility of improving the dialogue system by accumulating a huge database, which contributes to the selection of optimal models of response to the proposed issues. CONCLUSIONS Analysis of the evolution of methods of teaching foreign languages, conducted in this study, led to the conclusion that there are two major factors that have had a huge impact on the evolution of approaches and methods in the study of foreign languages; namely, • changing the social structure of society, leading to the formation of a new social request, and hence the purpose of foreign language learning; and • development of scientific and technological progress, leading to the formation of new learning tools that can compensate for the lack of a natural environment of communication in the language being taught. Computerization and informatization of the educational process which began at the end of the last century allowed for the distinguishing of the following directions of development in education: • • • •

individualization of learning, personalization of learning, increasing the degree of independence of students, and distance learning.

The informatization of the society has led to the expansion of all types of communication, including telecommunications, and brought to the forefront the order of the society to teach people foreign languages for the purpose of not only practical knowledge, but also the ability to carry out intercultural communication, which became possible due to the emergence of communicative and competence-based approaches in this field of education.

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The emergence of AI and its use in various spheres of human life, including the educational environment, marks the next stage in the development of approaches and methods in teaching foreign languages. Analysis of the impact of the use of AI in education helped to identify the following trends that are important in education in general, and in teaching foreign languages, in particular: • adaptability of training—creation of various formats of training, taking into account the goals, interests, and level of knowledge of students; • personalization of learning—the ability to adapt to an individual’s intellectual and psychological characteristics of students; • automation of learning assessment; • round-the-clock feedback; and • changing the role of the teacher in the learning process. Analysis of the use of AI leads us to the conclusion that, firstly, elaborations in the field of AI show stable dynamics of development, which means that it will lead to the emergence of new more advanced systems in the sphere of foreign language teaching, and secondly, it will strengthen all the above trends in education, and thirdly, it will undoubtedly lead to a change in the role of the teacher in the educational process. The use of AI-based systems should, in our opinion, lead to the following processes that transform the work of the teacher: • the abolition of functions that take a lot of time and effort from the teacher (checking homework, testing, evaluation, development of multilevel tasks, etc.); and • help the teacher to measure the cognitive and emotional state of each student, to promote his mental processes and reflection, which will allow a deep analysis of the interaction of the student and the educational system, to constantly update the forms and models of learning, taking into account the accurate assessment of the current state of the student and his motivation, that is, to adapt the learning process to the characteristics of each student and thereby improve the quality and speed of learning. However, I would like to emphasize the impossibility of complete replacement of the teacher in teaching foreign languages by AI systems, as the depth, diversity, spirituality, emotionality, and richness of human communication can and should be transmitted only by human, not AI.

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REFERENCES Afonin, V. L. (2009). Intelligent robotic systems: Course of lectures. Moscow, Russia: Internet-University of Information Technology. Atabekova, A. A. (2008). New computer technologies in teaching Russian as a foreign language: A training manual. Moscow, Russia: RUDN. Bim, I. L. (2002). Personality-oriented approach—the basic strategy of renewal of school. Inostrannye Yazyki v Shkole, 2, 11–15. Chashchin, V. V., Popkova, E. G., Zabaznova, T. A., & Ostrovskaya, V. N. (2013). Application of staff marketing in the educational services market. Middle East Journal of Scientific Research, 16(6), 865–870. Chernilevsky, D. V. (2002). Didactic technologies in higher school. Moscow, Russia: Unity. Common European Framework of Reference for Languages: Learning, teaching, assessment. (2003). Department of Language Policy, Strasbourg, Council of Europe. Moscow, Moscow State Linguistic University. Galskova, N. D., & Gez, N. I. (2004). Theory of teaching foreign languages. Moscow, Russia: Academy. Hymes, D. H. (1972). On communicative competence. In J. B. Pride & J. Holmes (Eds.), Sociolinguistics: Selected readings (pp. 269–293). Harmondsworth, England: Penguin. Kedrova, G. E., & Muromtsev, V. V. (2008). Electronic textbooks: Actual problems of standardization. Bulletin of quality, 6(84), 28–34. Khaleeva, I. I. (2000). On gender approaches to the theory of language and culture teaching. Proceedings of the Russian Academy of Education, 1, 20–29. Lectorsky, V. A. (2015). Are sciences of man possible? Questions of Philosophy, 5, 3–16. Samovar, L., & Porter, R. (1994). Intercultural communication: A reader (7th ed). Belmont, CA: Wadsworth. Vorontsova, G. V., Legalov, R. M., Nalchadzhi, T. A., Podkolzina, I. M., & Chepurko, G. V. (2018). Problems and prospects of development of the world financial system in conditions of globalization. In E. G. Popkova (Ed.), The future of the world financial system: The fall of harmony (pp. 862–871). Cham, Switzerland: Spring.

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CHAPTER 32

SYSTEM OF OPTIMIZATION OF COGNITIVE DEVELOPMENT OF THE SUBJECT OF ENGINEERING ACTIVITY IN THE CONDITIONS OF USE OF INTELLIGENT COMPUTER PROGRAMS Tatyana A. Mayboroda Stavropol State Medical University Natalia K. Mayatskaya Stavropol State Medical University Karina M. Oganyan Stavropol State Medical University Galina V. Stroi Stavropol State Pedagogical Institute Andrey B. Chernov North-Caucasus Federal University

Meta-Scientific Study of Artificial Intelligence, pages 293–301 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

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ABSTRACT The aim of the study is to develop and test the system of optimization of cognitive development of the subject of engineering activity in the conditions of using intelligent computer programs. The methodological basis of the study is general methodological and specific methodological approaches and principles of psychology, as well as methodological principles of learning and modern approaches and trends in the theory of forecasting and development of new information, pedagogical, and psychological technologies of activity. Three thousand five hundred and eight people (engineers of various types of activity of plants and enterprises of the Stavropol territory and Astrakhan (technologists, designers, and organizers), engineering students of NorthCaucasus Federal University took part in various stages of the study as experts and respondents. As a result of research criteria and indicators of cognitive development of the subject of engineering activity, the system of optimization of cognitive development of future and current engineers in educational space and conditions of industrial production including nine computer programs and allowing to increase efficiency of educational and professional engineering activity were developed.

The leading factor in the effective modernization of the country is not the actual production and investment, but the development based on the intensification of innovative activities in the field of basic knowledge-intensive sectors of the economy. In turn, the quality and effectiveness of innovation in the industrial production system depends on the human factor. That is why the main object and subject of innovative processes (production, acquisition, dissemination, and practical application of knowledge) today should be not only economic models, organizational structures, technologies, but also the person himself: his worldview, self-awareness, value and semantic sphere, personal and professional potential, culture, and so on. The problem of cognitive development is particularly relevant to representatives of engineering professions who develop new scientific and technical ideas and optimize existing solutions, implement the developed ideas and solutions in practice, organize production, and so on, that is, act as the main “engine” of innovative processes. The specificity of modern engineering activity imposes high requirements not only to the level of professional knowledge and skills of specialists, but also to the presence of such personal and professional competencies as the ability to respond quickly to changes, creativity, initiative, versatility, and so on. At the same time, the modern system of professional education, providing future engineers with knowledge of the necessary disciplines and initial skills of design and calculation of future devices and technical processes, does not focus them on the formation of a holistic view of professionalism

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in the field of engineering, does not stimulate their personal and professional resources and the need for professional self-realization, does not provide the most effective cognitive development. As a result, according to the data provided by the Academy of Labour and Social Relations, most of the graduates of engineering specialties of technical universities (about 70%) are not arranged to work in the field in which they have qualified. Many, having worked a relatively short time, leave the engineering profession, others are not hired by employers due to lack of professional experience and necessary personal and professional competencies. Thus, contradictions between • the demand for engineering activities in solving economic and social problems and the lack of competent specialists; • modern requirements of engineering activity to the professionalism of the engineer and the available real level of cognitive development of engineers; • the desire of future engineers to self-development and self-realization and conservatism of the modern system of engineering education; and • the need to optimize the cognitive development of future engineers and the undeveloped holistic concept of this process. METHODOLOGY In our study, the following is hypothesized: 1. In engineering activity cognitive development of the engineer, internally providing realization of personal and professional potential through the maximum approach to a professional standard and formation of an innovative orientation, is externally expressed in effective implementation of innovative activity on production. 2. The cognitive development of future engineers as a process is conditioned by their ability and capacity to form and creatively use their personal and professional resources. As a result, it is expressed in a “step-by-step” approximation of future engineers to a professional standard. 3. Cognitive development of future engineers in the educational space will be most effective if in accordance with its concept: –– the main purpose of engineering education will be the formation of innovative orientation of students; –– the algorithm of optimization of cognitive development of the future engineer will be aimed at promoting awareness of the stu-

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dent about essence and structure of engineering creativity and change of criteria according to which they will be able to evaluate themselves as the subject of educational, self-transforming, and future professional activity; and –– the organization of cognitive development will be based on a combination of modern information technologies, the use of intelligent computer programs and deep personal interaction of teachers and students, and is aimed at the formation of their innovative orientation. At the stage of designing of the optimization system of cognitive development of future engineers the following principles and approaches were used: • methodological principles of training; • modern approaches and directions in the theory of forecasting and development of new informational, pedagogical, psychological, and acmeological technologies of activity. RESULTS Development and experimental approbation of the optimization system of cognitive development of the subject of engineering activity in the conditions of use of intellectual computer programs was carried out in three stages (Mayboroda, 2010). At the first stage of the study, on the basis of the theoretical analysis were identified personal and professional competences of engineers. Twenty-five successful engineers (technologists, designers, and organizers) participated as experts. The modified questionnaire was further used for secondary expert evaluation. Leading directors and top managers, engineers of the industrial enterprises of the Stavropol territory with high efficiency of professional activity (90 people) participated as experts. The content of the professional standard of the future engineer also specified with the help of expert evaluation: the experts were the engineering students of the 4th and 5th courses with the highest grade point average of North-Caucasus Federal University (96 people). At the second stage of the study, to evaluate the future engineer as a subject of cognitive development, the level of formation of cognitive competencies of future engineers was compared with the effectiveness of their training and self-transforming activities. As respondents, there were 524 engineering students of North-Caucasus Federal University, as experts, were their teachers (36 persons).

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To assess the engineer as a labor resource of the organization, a comparison of the level of formation of cognitive competencies of engineers with the effectiveness of their professional activities was carried out. As respondents, 498 engineers participated, as experts, were their direct superiors (96 people). To evaluate engineers-innovators as a personal and professional resource of the organization, the level of formation of cognitive competencies of engineers-innovators was compared with the efficiency of their innovative activities. As respondents were 276 engineers engaged in innovation activities in manufacturing, as experts were their immediate supervisors (40 people). To determine the patterns and mechanisms of cognitive development of future and current engineers in the study involved: managers (56 people), engineers (348 people), engineers-innovators (40 people), and students of engineering specialties of North Caucasus Federal University (303 people). At the third stage of the study, 822 engineering students of North Caucasus Federal University (410: experimental group, 412: control group) and 258 engineers (128: experimental group, 130: control group) took part in the formation of the experiment. In the empirical part of the study, conversation, observation, methods of self-assessment, and expert evaluation, as well as the author’s original computer techniques were used in an integrated manner: • information system of expert evaluation and self-evaluation of professionally important qualities of industrial engineers (Bondar, Kudryashov, & Mayboroda, 2006); • information system of diagnostics and development of cognitive processes of engineers and students of technical universities (Kargin, Kondratieva, Kudryashov, & Mayboroda, 2006); • computer diagnostics program for the degree of satisfaction of basic needs of students (Kudryashov & Mayboroda, 2005); • information subsystem of diagnostics and development of professionally important qualities of industrial engineers (Kudryashov & Mayboroda, 2007); and • information subsystem of diagnostics and development of professional motivation of innovative engineers and engineering students of technical universities (Kondratieva, Kudryashov, & Mayboroda, 2009). These techniques were combined • with the following questionnaires: “the level of acmeological culture” (Selezneva et al.); “capacity for self-management” (Peisahov); “selfattitude” (Stolin, Pantileev); “behavioral patterns”; “business situations” (Khitrova); “way out of difficult life situations”; “risk appetite”

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(Schubert); “creative potential”; “focus on the type of engineering activities” (Godlinik); “capacity for scientific work”; “willingness to selfdevelopment”; the sense of purpose diagnostics; “need for achievement”; “desire for success” (Ehlers); “desire for independence”; “selfassessment of the need for approval” (Marlowe, & Crown); modified method “value orientations” (Rokich); and modified method “formation of value attitude to professional activity” (Demina); • with the following tests: “Bourdon-Anfimov’s correction test”; “concentration of attention”; “Red Schulte tables”; “peculiarities of voluntary attention”; “finding differences”; “Münsterberg test”; powers of observation”; “the determination of the type of memory by the method of reproduction of differently interpreted words”; “the study of spatial representations” (Shepard); “finding of objects”, “mechanical intelligence” (Bennett); “forecast of events”; “forming questions”; “creating a shape”; “divergent thinking”; “new look”; “cause and effect”; “logical paradox”; “judgments and conclusions”; “proverbs” and “analogues of proverbs”; “puzzle tasks”; and “causes of the event”; and • with the tests aimed at determining the amount of attention when presenting numerical and figurative information; identification of the amount of short-term memory, the amount of figurative memory by the method of memorizing images of objects and by the method of classification of cards with images of objects; diagnosis of memorization by the method of analyzing the completeness and reliability of testimony, mediated memorization by the method of successful answers; and tests aimed at the study of logical and mechanical memory by memorizing two rows of words. The target stage is associated with the identification of the acmeological goal for future engineers which is the optimization of their cognitive development. At the technological stage, a set of technologies is determined to optimize the process of cognitive development of future engineers. At the stage of the organization, a system of sociopsychological, psychological, pedagogical, and Acmeological conditions is created. At the control stage, the process of cognitive development of future engineers is monitored and corrected. The formative experiment consisted of establishing, developing, and monitoring stages. The experiment involved 822 students of engineering specialties of North Caucasus Federal University (410: experimental group, 412: control group) and 258 engineers (128: experimental group, 130: control group). The experiment was held for 2 years in the process of training the third- and fourth-year students.

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CONCLUSIONS Comparison of diagnostic results of participants of the formative experiment at the ascertaining and control stages of the study allowed us to conclude that in the experimental group all indicators on the criteria of selfdevelopment and self-realization show the positive dynamics. Thus, the indicators of realization of personal and professional potential have significantly changed quantitatively and qualitatively (Table 32.1). As can be seen from Table 32.1, the most significant changes occurred in such indicators of realization of personal and professional potential of future engineers as self-regulation and readiness for self-development. The analysis of the dynamics of the level of self-regulation showed that students of experimental group at the level of self-management increased to the average (or above average value); the level of maladjustment and disorders of mental activation reduced; the dominance of the reactions of extrapunitive type decreased and the weight index of the complex reactions of the impunitive type increased; and the ability to behave rationally increased. The analysis of the dynamics of the level of creativity showed that the students of the experimental group had a small but statistically significant increase in their creative potential, in particular, such indicators as perseverance in creativity and personal significance of creativity. Comparative analysis of the primary and reevaluation of the effectiveness of educational and self-transforming activities as criteria for self-realization of future engineers revealed significant changes in all indicators in the experimental group (Figures 32.1 and 32.2). The level of indicators of efficiency of educational activity in experimental and control groups before and after carrying out the forming experiment was estimated in points where 1 = high progress, 2 = compliance of educational results to the objectives, and 3 = stability of high results of educational activity. TABLE 32.1  Results of the Comparative Analysis of Average Indicators of Realization of Personal and Professional Potential After Primary and Repeated Testing of Future Engineers, % Control Group Indicators

test

retest

The Experimental Group test

retest

Student’s Criterion Value

Self-attitude

57.22 + 0.79

58.56 + 0.96

57.40 + 0.70

60.03 + 0.92

2.26

Self-regulation

45.61 + 1.14

46.72 + 1.07

45.39 + 1.15

56.67 + 1.13

6.39

Creativity

63.43 + 0.38

63.82 + 0.37

62.56 + 0.39

65.72 + 0.36

3.65

Readiness for selfdevelopment

53.94 + 1.17

54.77 + 1.44

54.38 + 1.41

64.63 + 1.28

5.37

Note: The table highlights the values of student criterion exceeding the critical 2.58 at the significance level 0.01

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2

3

EG before experiment

EG after experiment

CG before experiment

CG after experiment

Figure 32.1  The level of indicators of efficiency of educational activity in the experimental and control group before and after the formative experiment. 4.3 4.2 4.1 4.0 3.9 3.8 3.7 3.6 1

2

3

EG before experiment

EG after experiment

CG before experiment

CG after experiment

Figure 32.2  The level of indicators of efficiency of self-transforming activity in the experimental and control group before and after the formative experiment.

The level of indicators of efficiency of self-transforming activity in the experimental and control group before and after the formative experiment was evaluated in points, where 1 = minimizing mental, physical, and time costs during cognitive development; 2 = optimization of the process of formation of professional and psychological readiness for engineering activities; 3 = going beyond the existing experience, setting new educational and developmental tasks, finding nonstandard solutions in the course of self-development, and obtaining a creative product (compiled by the author) The most significant changes in the experimental group (at the confidence level 0.01) occurred on such indicators of the effectiveness of educational and self-transforming activities as: high academic performance (t = 3.13); compliance of the educational result with the goals (t = 5.37);

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minimization of mental, physical, and time costs during acmeological development (t = 3.72); and going beyond the existing experience, setting new educational and developmental tasks, finding nonstandard solutions in the course of self-development, obtaining a creative product (t = 2.64). Statistically insignificant increase of such indicators as stability of high results of educational activity and optimization of the formation process of professional and psychological readiness for engineering activity is explained by the fact that a certain amount of time is required to consolidate the skills of self-development and self-realization received as a result of the developing program. At the same time, the increase of the considered indicators for students of the experimental group after the formative experiment proves that students have acquired such skills. The obtained results can be used to optimize the cognitive development of engineering subjects in universities and in the system of industrial production in order to improve the efficiency of professional innovation of engineers and optimize the educational and self-transforming activities of engineering students in universities. REFERENCES Bondar, N. G., Kudryashov, O. A., & Mayboroda, T. A. (2006). Information system of expert evaluation and self-evaluation of professionally important qualities of industrial engineers. Author’s certificate of official registration of computer programs No. 2006614075. Kargin, N. I., Kondratieva, M. V., Kudryashov, O. A., & Mayboroda, T. A. (2006). Information system of diagnostics and development of cognitive processes of engineers and students of technical universities. (Author’s certificate of official registration of the computer program No. 2006614076) Kondratieva, M. V., Kudryashov, O. A., & Mayboroda, T. A. (2009). Information subsystem of diagnostics and development of professional motivation of engineers-innovators and engineering students of technical universities. (Author’s certificate of official registration of the computer program No. 2009610045 0 Kudryashov, O. A., & Mayboroda, T. A. (2005). Computer program for diagnosing the degree of satisfaction of the basic needs of students. (Author’s certificate of official registration of the computer program No. 2005611034) Kudryashov, O. A., & Mayboroda, T. A. (2007). Information subsystem of diagnostics and development of professionally important qualities of industrial engineers. (Author’s certificate of official registration of the computer program No.2007615103) Mayboroda, T. A. (2010). Acmeological development of industrial production engineer: Theory and practice: Monograph. Moscow, Russia: ILEKSA.

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CHAPTER 33

INFORMATION CULTURE OF PERSONALITY IN THE CONDITIONS OF ARTIFICIAL INTELLIGENCE Magomet D. Elkanov Karachaevo-Circassian State University of U.D. Fatima H. Laipanova Karachaevo-Circassian State University of U.D. Marina N. Kubanova Karachaevo-Circassian State University of U.D.

ABSTRACT The chapter deals with the actual problem of the role of information culture as a necessary condition for the effective functioning of the individual in the conditions of the existence of artificial intelligence (AI). The authors analyzed the concepts of “information culture of personality,” “AI,” and “synergetics.” The possibilities of applying the principles of synergetic approach to the study of information culture as an open self-developing system are considered. The central idea is the development of the reflexive ability of Meta-Scientific Study of Artificial Intelligence, pages 303–309 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

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304    M. D. ELKANOV, F. H. LAIPANOVA, and M. N. KUBANOVA the individual, allowing them to skillfully guide their internal potential for self-development of information culture as a system that contributes to the formation of personality.

Information and cultural problems of scientific research, the development of which has been going on for the last few decades, involves the definition of new “rules of the game” in the modern world of information technology and artificial intelligence (AI), because now there is a demand for men with a high culture, education, professional skills, which are able to make an independent decision in a difficult situation, and characterized by mobility, dynamism, and a sense of responsibility. Significant intensification of the information space and its increase, accessibility, and rapid aging of information add value to the role of information culture in the life of modern people. New types and objects of activity arising as a result of increasing technological efficiency and transformation of social and cultural processes define information culture as a necessary condition not only for successful professional activity of a person, but also for his/her fulfilling everyday life. METHODOLOGY In modern domestic research on psychology and pedagogy, a synergetic approach is used quite carefully, and at the same time, effectively. In the collective monograph “On the History of Synergetics” the author of the term “synergetics” Hermann Haken (2017) writes: “Synergetics is an area of interdisciplinary research that studies systems consisting of many (identical or different) parts . . . We are talking about open systems that allow incoming and outgoing flows of energy, matter, and/or information. The processes in which self-organization causes qualitative changes in the state of the system (and new qualities emerge) are at the forefront of research” (p. 42). He also emphasizes, “Synergetics is interested in principles that do not depend on the features of the elements of the system. These elements can be atoms, molecules, photons, living cells, neurons, people as members of society, businesses in the economy, etc.” (p. 42). As shown by modern research, synergetic methodology allows a more in-depth study of such complex, nonlinear, open systems as society, personality, and its activities. RESULTS The term information culture was originally used to describe phenomena and processes in the system of organization and management. In addition, it

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has been and is seen as an integral part of the organization’s overall culture, influencing the effectiveness of communication within and outside the company, as well as information and knowledge management processes. The developers of the concept of industrial society were among the first to address the problem of information culture. In the works of Bell (2004), Castells (2000), McLuhan (2012), Toffler (1999), and others, a number of characteristics of information culture as a necessary mechanism of the information society were identified. Some of them are listed as follows: • general patterns of social behavior, norms, and values in the information society that determine the importance and use of information; • perception of information as a value, in addition to material, social, and spiritual values; and • information as a processed intellectual product necessary for the adequate functioning and development of society. According to Pronina (2016) Vorobiev introduced the concept of information culture into Russian science in the 1971 book, Information Culture of Managerial Work. At present, discussions on the concretization of the essence and content of information culture can be considered in the context of several directions and approaches. The study of the phenomenon of information culture in the conditions of AI requires taking into account the fact that its creation changes the human environment. The natural, social, and cultural environment is complemented by an AI environment that acquires the features of humansizedness. Previously, the needs of the body, soul, and spirit were satisfied and taken “shelter” in nature, society, and culture. And what needs can be accommodated by AI? At first glance, the answer is quite simple, as AI makes it possible to shift the time-consuming intelligent work of processing and coding information on automated systems. The system of relations with AI requires higher-order thinking, so education becomes a key value for the career and successful functioning of the individual in society. Developing new competencies requires a person to constantly update knowledge and skills. However, training programs are reviewed so often that people do not have time to learn and absorb productively all the material. All this indicates that the individual person found himself, according to Prigozhin, “in a world of uncertainty, which is intrinsically present in the reality of human relations, goals, information in situations. It cannot be fully overcome, and sometimes fundamentally dominates over certainty” (Prigozhin, 1985, p. 61). According to Pronina (2016), information culture can be considered as an open self-developing system, which functions according to the principles

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of synergetics “that is associated with the constant flow of cultural information of the cosmos. It is capable of being stabilized as it is related to the periods of stability of informatization, when there is a final consolidation of forms of cultural information. At the same time it is dissipative, because it exists in a state of constant information exchange” (p. 87). The well-known Russian researcher Budanov (2009) in his work Methodology of Synergetics in Post-Non-Classical Science and Education cites seven basic principles of synergetics: two of them are the principles of being (homeostaticity and hierarchy); five principles are of formation (nonlinearity, instability, openness, dynamic hierarchy, observability). The state of stability of information culture, as a system, is associated with those periods in the development of society, when there is a consolidation of forms of information transmission. Information culture is formed within the framework of universal culture, where the mechanism of continuity is a product of historical development and is a relatively independent social sphere, in which the algorithms of activity are transmitted in a “pure” form by professionals: teachers, educators, coaches, and so on. At first, the individual masters the methods of activity in a “pure” form, developing his universal abilities—thinking, feeling, will, and then special (e.g., professional) and they become a part of his professional and private life. Man constantly creates the objective world of things and gives them a certain function. These things become relatively separate and independent from the conditions of human existence forms which regulate his life. They can constrain, suppress human individuality, or open up new opportunities for its development. But when creating these things, the human abilities were realized, which properties are determined by the need of human society and communication. With the development of society and human abilities, most likely, the original function of things changes or disappears. Hence, the knowledge and awareness of the relationship between the man and the objective world of things is not excluded, but necessary. The second principle of existence of the system is the principle of hierarchy, which has a deeper meaning than the simple inclusion of the subsystem, its elements in the hierarchical structure of the system of higher order. G. Haken expressed this principle as follows: “long-lived variables subordinate short-lived” (Haken, 2017). Budanov (2009) believes that “the main meaning of the structural hierarchy is the composite nature of the higher levels in relation to the lower. For the lower level there is order, for the higher level there is a structural element of chaos, a building material (p. 50). “Every time elements connect to a structure, they transfer to it a part of their functions, their freedom,” he writes (Budanov, 2009, p. 50). Information culture of the individual is formed under the influence of the culture of society. The individual receives information through various channels and is part of several social systems, including virtual reality. In

Information Culture of Personality in the Conditions of Artificial Intelligence    307

modern conditions, there is a danger for a person to be under domination of AI. The condition for the preservation of identity, “undissolved” personality, so to speak, is the inclusion in the structure, and all that occurs in the system, that is, the roles, functions, hierarchies, become clear and are accepted by the personality as something thoughtful and orderly. All this makes it possible to manage different life situations within a particular hierarchical structure. On the basis of meaningfulness there is a sense of self-involvement in what is happening, which gives the opportunity to avoid a sense of being a “controlled element” and to preserve their freedom. A more important task, in our opinion, is to develop the ability to build hierarchical structures. In the conditions of the existence of AI, it is especially important when the information culture of a person is formed in an environment in which he acts not only as an element–participant, but also as a creator of the information space through the choice of his own role and the definition of his own rules of the game. It is not doubtful that self-confidence and communication skills are the main prerequisite for the preservation of the individual’s subjectivity in any hierarchical system. A mandatory component in the structure of skills and abilities of the individual should be the ability of nonverbal, electronic communication, improving the capacity to collect, analyze, assess, and use information. Among the principles, the observance of which is a necessary condition for the emergence of a new quality, Budanov (2009) highlights the principles of nonlinearity, openness, and instability: Nonlinearity is a violation of the principle of superposition in a certain phenomenon: the result of the sum of the effects on the system is not equal to the sum of the results of the effects. The results of the current causes cannot be added. (p. 54)

Personality in the conditions of AI has faced with the challenge in today’s world, that is a shift away from the linear thinking. In the face of instability, the reconstruction of the past does not provide answers to the problems of the present; traditions cannot ensure development in the future. Information culture of personality in the conditions of AI existence retains freedom of expression, unlocking the hidden opportunities on the basis of reflection. The formation of the present is a task, the solution of which depends on the reflexive ability of everyone. Perception, memory, attention, and awareness play an important role in the information culture of a person. The lack of a direct link between the amount of information we receive and the level of intelligence and efficiency of decision-making testifies to the openness of the information culture. It realizes self-improvement through communication and ability to receive

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and transmit information. The threat to its existence appears when there is a breakdown in communication. But the ability to change is manifested in the relationship of the individual to the world around him. Shallow knowledge works as a means of adaptation, and critical knowledge allows to understand something new, to cast doubt on obsolete knowledge, goals, and values. But this requires the possibilities for self-analysis and reflection in the information culture of the individual. Reflection promotes in-depth knowledge, offers alternatives to events, facts, and decisions. In the conditions of AI, a person can’t help but compare the intellectual abilities of a man and a machine. The ability to reflect, to create his own ideas, to understand his own desires, and feelings is the prerogative of man. This ability is the core of the information culture of the individual, capable of self-development. As culture does not exist outside of man, so information culture is inseparable from its carrier, who has a “practical mind” and the ability to reflect. The synergistic effect is manifested in combining efforts, in which the total efficiency becomes more than just the sum of the effects. Exchange of information and energy sets internal resources in motion, contributing to the self-development of the system. Features of information culture of the person in the conditions of AI are: the lack of traditions, emergence of new social relations as a result of virtual communication; democracy and mutual understanding of people based on the principle of self-identification among machines with AI; change of the lifestyle; more reflection and meaning; and openness of new information. The nonlinearity and openness gives rise to the principle of instability: The implementation of the principles of nonlinearity and openness, under certain conditions, allows the system to leave the area of homeostasis and get into an unstable state . . . the state and the programme of system are unstable, if any, even small, deviation from them increase over time (bifurcation point). (Budanov, 2009, p. 57)

Knowledge of bifurcation points is important as any weak influence at a given point can have a decisive influence on the behavioral choice by the system. Information culture of personality is in the process of constant development, going through all stages of personality formation, moving from uncertainty to certainty. CONCLUSIONS Thus, the information culture of the individual in the conditions of AI can be defined as a system of algorithms of information activities, including methods and norms of communication with AI systems, the use of the telematics facilities, global and local information, and computer networks.

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The use of the principles of synergetic approach allows the opportunity for a fresh look at the problem of information culture in the conditions of AI. In the new environment, the role of socialization of the individual through information flows is increasing. The potential capacities of the individual are made tangible in the products of culture. The formation of information culture of the person in the conditions of AI occurs in the process of transition towards new norms, values, and attitudes of the emerging society. The major treasure of modern society is information, and information culture is becoming the tool of socialization. In light of the high dynamics of information processes in society, it is unacceptable to rely on random factors of influence on the formation of information culture of the individual. It is necessary to purposefully prepare the individual to live in a society, in which there is interaction between a man and AI. REFERENCES Bell, D. (2004). The coming of post-industrial society. Experience of social forecasting. (D. Bell, Trans., second edition, augmented, and revised edition). Moscow, Russia: Academia. Budanov, V. G. (2009). Methodology of synergetics in post-non-classical science and education (The third enlarged edition). Moscow, Russia: LKI. Castells, M. (2000). Information age: Economy, society, and culture (M. Castells, Trans.). Moscow, Russia: HSE. Haken, G. (2017). On the history of synergetics: General principles of self-organization in nature and society. Izhevsk, Russia: Institute of Computer Research. McLuhan, M. (2012). War and peace in the global village (M. McLuhan & K. Fiore, Trans.). Moscow, Russia: ACT. Prigozhin, I. (1985). From existing to arising: Time and complexity in the physical sciences. Moscow, Russia: Science. Pronina, L. A. (2016). Information culture as a factor of information society development. Retrieved from https://cyberleninka.ru/article/n/informatsionnaya -kultura-kak-faktor-razvitiya-informatsionnogo-obschestva Toffler, A. (1999). The third wave. Moscow, Russia: ACT.

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CHAPTER 34

DIGITAL ECONOMY New Requirements and Approaches in Lawyers Training Elena N. Bazurina Volga Branch of the Russian State University of Justice Lidia N. Ivanova Volga Branch of the Russian State University of Justice Vladimir Yu. Karpychev Volga Branch of the Russian State University of Justice Sergey I. Kuvychkov Volga Branch of the Russian State University of Justice Andrey M. Terekhov Volga Branch of the Russian State University of Justice

Meta-Scientific Study of Artificial Intelligence, pages 311–317 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

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ABSTRACT This chapter considers the actual issue of professional training of specialists focused on legal support of the digital economy. Authors substantiate the necessity of making changes to the Federal State educational standards and educational programs in the field of jurisprudence in order to form competencies and psychological readiness of graduates of law schools for effective professional work in the digital economy, as well as approaches on improving quality of their training are proposed. Methods of system approach, analysis and synthesis, modeling, methods of diagnostics, and comparative longitudinal study. As a result of the study, authors proposed new approaches on training lawyers for the sphere of digital economy, as well as developed proposals for training of specialists providing legal support of subject activities in the digital economy. Authors of this chapter describe the need for new requirements to the quality (legal professionalism) of professionals providing legal support for the digital economy. This thesis is strongly reinforced by materials for the parliamentary hearings, which note that most Russian companies are not ready for the digital economy. In this regard, some issues of training specialists, functionality of which is legal support of business activities in the digital economy, are actualized. Authors believe that modern graduates of legal specialties due to specifics of humanitarian knowledge have little knowledge of digital means of professional activity. This chapter shows that new approaches for training lawyers for the sphere of digital economy are necessary to increase motivation and psychological readiness of humanitarians for “digital” self-education.

Regulatory regulation, information infrastructure, and personnel—these are the basic conditions for development of platforms, technologies, and effective interaction of subjects of markets and branches of economy (Yakutin, 2017, pp. 46–47). The digital economy acquires a new look and format of the “sixth technological order.” Basic components of the digital economy, in our opinion, are: infrastructure, legal regulation, digital technology, human resources, and information security. Training lawyers for a new technological order is development of advanced technologies of the digital economy, such as big data, blockchain, Internet of Things, artificial intelligence, and new infrastructure communication standards LTE and 5G needed for digital technologies.1 Big data analysis technology works with real-time data which flows from different sources and their correlation with historical data. This technology solves the most difficult task for business—forecasting changes and their causes. Blockchain technology in the digital economy provides decentralized performance of all functions related to data storage, modification, and

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access (i.e., functions traditionally performed by the intermediary in centralized systems), as well as functions of interaction between users, increasing technical characteristics, security payment, and other systems (Voronov & Chasovskikh, 2017). Relevant applications of technology in economic activity—cryptocurrency and smart contracts. Using SWOT-analysis, it is established that cryptocurrencies contribute to changing economic paradigms and eliminating barriers of national currencies (Ali, Barrdear, Clews, & Southgate, 2014). Artificial intelligence technologies are promising for the digital economy, in particular, for processing big data, data mining (detection and analysis of hidden data, etc.), and acceptance of humanoid solutions, because of this, they need ethical and legal regulation. The study, conducted by the authors, shows that the introduction of new information technologies in economic practice determines the change of competences of information economy workers. According to the President of Sberbank G. Gref, “We stop hiring lawyers who do not know what to do with the neural network . . . because neural grid prepares claims better than lawyers.”2 METHODOLOGY Legal support of the digital economy needs fundamental changes. According to results of the study of the current civil legislation, we propose the following changes in norms of contract law of the Civil Code of the Russian Federation (Table 34.1). The analysis of Table 34.1 shows that in the digital economy requirements for lawyers specializing in information legal relations are increasing. Objectives of the activity of IT-lawyers will be: • effective solution of legal issues of information technology market participants, expert advisory activities; • legal support of business projects and management activities in the field of information technologies; • preparation of normative legal acts and evaluation of the effectiveness of legislative initiatives in the field of information technology; and • implementation of law enforcement activities in the field of information technology. Issues of training specialists, whose function is legal support of business activities in the digital economy, presuppose the improvement of existing educational programs. New disciplines and directions of training in the field of legal regulation of information technologies, legal support of the

314    E. N. BAZURINA et al. TABLE 34.1  The Main Directions of Legal Regulation of the Digital Economy Content of the Innovation of Legal Regulation

Civil Code of the Russian Federation

Attach information as an object of civil rights. Back to edition of (01.12.2007), which provides information as an object of civil rights

clause 128

Clarify a definition of the digital form of transactions; requirements for content and form of digital offer and acceptance; contracts of accession, approximate terms of contracts; time of contract conclusion; individual types contracts related to the peculiarities of digital relations

clauses 158, 435, 438, 427, 428

Define automated contracts as a form of performance of an obligation (introduce a new clause)

chapter 22, clause 328.1

Legal conditions of automated fulfillment of obligations without the additional will of the debtor, “Smart contracts”

clause 327.1

Use and development of model contracts (indicative terms of contracts) used by parties in electronic form

clause 427

Introduce the mode of digital residence of legal entities

clause 51

Allow replacement of the “physical” address of the organization with a virtual (“digital office”)

clause 54

Improve rules of circulation of software for computers, taking into account specifics of mobile applications, cloud programs, etc.

clauses 1285, 1286

Simplify procedure for concluding license agreements and contracts on the alienation of exclusive rights

clauses 1285, 1286

Legitimation of share ownership (allocation of shares) in the exclusive right of the intellectual activity

clause 1229

Enter electronic passport of a vehicle with information about the owner and imposition of a charge under a pledge agreement (enter a new clause)

§ 3 chapter 23 clause 358.19

digital economy can develop basic ideas of teaching educational disciplines of so-called “information” cycle (Anisimov, Sergevnin, & Truntsevsky, 2018). The educational standard establishes competencies that determine a specialization of the graduate (Podberezkin, 2007). Our research allows us to formulate professional competences of IT-lawyer, among them: (a) ability to effectively apply in practice legal knowledge in terms of legal regulation of activities in the field of information technologies and (b) ability to use information technologies and technical means of processing information in the process of the professional activity. In addition, we offer to consolidate the knowledge and skills formed by the IT lawyer in educational standards (Table 34.2). The basic condition of the digital economy is IT-legal personnel (their education, selection of candidates for study, assessment of professional and

Digital Economy    315 TABLE 34.2  Knowledge and Skills of IT Lawyer in the Field of Digital Economy Know:

Be Able to:

Have:

Russian and foreign legislation in the field of information technology and economy

effectively apply legal knowledge in the field of digital economy and information technologies

skills of organization and legal support of contractual work

influence of technical characteristics of modern digital devices and services on processes of solving legal problems

work with databases and legal information resources

skills of conducting forensic computer examination

automated information systems used in court proceedings

assess risks associated with new digital technologies

skills of information support of the activities of law enforcement agencies, courts, police, prosecutor’s office, investigation committees, etc.

psychological suitability, and readiness for self-education and self-development in the field of information technology). RESULTS Legal activity in the digital environment leads to the need for detailed, indepth study and mastering information technologies. Meanwhile, there is a contradiction between the humanitarian mindset of a lawyer and requirements for his thinking when mastering exact sciences. If today the conflict “is resolved either by reducing criteria for successful assimilation of exact sciences by humanitarians, or by reducing as much as possible content of education, or by some neglect of the deep understanding of the material” (Vaypan, 2017, pp. 10–12), new technological way implies a new educational vector. As part of our study of the psychological characteristics of first-year students enrolled in law school for the period 2007–2018, it was revealed that the initial level of personality traits of first-year students was different. The student collective of each year of the recruitment accumulates a unique set of personality qualities. For example, recruitment of 2015 gathered more (in comparison with other years) students of smart (9.6%), responsible (47%), and discerning (24%), the lowest number of irresponsible (1.8%), and intrigued ( 7.8%);3 2017: conservative (38.1%), restrained (11.9%), and hyper-communicable (11.9%), less responsible (28.6%), carefree (8.3%), and low intelligence (7.1%). In general, low intellectual capabilities of the majority of students (high intelligence only 5–10% of freshmen) and lack

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of many personality qualities show that spontaneously developing social and psychological environments can “feed” the development of required qualities of intellect and personality for a small part of students. According to our research, the majority of students have formed a stereotype of ethnic self-identification (so-called “Slavophiles” were 49% of the sample in the study conducted in 2013, 78% in 2016, 60% in 20184). Such students are susceptible to traditional values and focus on the humanistic purpose of digitalization and ethical aspects of the introduction of digital technologies. Students with a different type of self-identification (so-called “Westerners”) were 37% in the 2013 sample, 15% in 2016, and 35% in 2018. They are focused on the search and adoption of other people’s knowledge, and interested in opportunities for digitalization. Students with a fuzzy type of self-identification (including multiple identities) make up a small part of a student population: 14.4% in 2013, 10.3% in 2016, and 5% in 2018. They pay attention to the lack of knowledge. We believe that psychological support of digital education (vocational guidance of the applicant taking into account his individuality, assessment of suitability, selection, feedback, the formation of a special project—research type of consciousness, etc.) will establish a mechanism of internal personal-oriented self-regulation of information and educational technologies of continuous education of a lawyer in the field of digital economy. CONCLUSION Thus, we have considered issues of improving training of lawyers focused on legal support of the digital economy. At the same time, the authors do not claim completeness of the statement and resolutions of questions rose in this chapter and consider it necessary to continue research of this problem. NOTES 1. Tadviser portal. http://www.tadviser.ru/index.php/; Long-Term_Evolution; Big_Data; Internet_of_Things_(IoT) 2. Portal of JSC “Rosbusinessconsulting.” https://www.rbc.ru/business/23/07/ 2017/5974b7a69a79477896b6708d 3. Test 16 PF Kettel (an abbreviated version of V. M. Rusalov) was used. Sample size (first-year students of full-time law school under the secondary education program): 167 people in 2015, 84 people in 2017. 4. Sample size (students of the 2nd year of full-time study of a law university, bachelor’s degree): 82 people: research of 2013; 87 people: 2016; and 100 people: 2018 The technique of psycho-diagnostics of ethnic identity was used.

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REFERENCES Ali, R., Barrdear, J., Clews, R., & Southgate, J. (2014). The economics of digital currencies. European Economics: Macroeconomics & Monetary Economics eJournal. Anisimov, V. F., Sergevnin, V. A., & Truntsevsky, Yu. V. (2018). Robotization and automation: Legal education and profession. Legal Education and Science, 3, 11–16. Podberezkin, A. (2007). Human capital: Ideology of advanced development of human potential. Moscow, Russia: Europe. Vaypan, V. A. (2017). Fundamentals of legal regulation of digital economy. Law and Economics, 11, 5–18. Voronov, M. P., & Chasovskikh, V. P. (2017). Blockchain: Basic concepts and role in the digital economy. Fundamental research, 9–10, 30–35.

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CHAPTER 35

THE EXPERIENCE OF ORGANIZATION OF EDUCATIONAL SPACE AND INCREASING THE FINANCIAL LITERACY OF ALL LAYERS AT MININ UNIVERSITY Irina S. Vinnikova Minin Nizhny Novgorod State Pedagogical University Anastasia O. Egorova Minin Nizhny Novgorod State Pedagogical University Ekaterina A. Kuznetsova Minin Nizhny Novgorod State Pedagogical University Olga I. Kuryleva Minin Nizhny Novgorod State Pedagogical University Larisa V. Lavrentyeva Minin Nizhny Novgorod State Pedagogical University

Meta-Scientific Study of Artificial Intelligence, pages 319–325 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

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ABSTRACT Implementation of a strategy for improving financial literacy in the Russian Federation has significantly increased a literacy level, but even today problems still exist within the framework of financial literacy and culture. Level of financial education and financial behavior should be raised due to insufficient development of the financial market in the Russian Federation and quality of life. A solution of this problem is presented by the authors in this study by describing the experience of Minin University in the organization and functioning of the educational space for improving the financial literacy of all segments of the population, which emphasizes a relevance of chosen topic. Novelty and interest can be represented as an approach of authors to improve the financial literacy of citizens and some of the practical results obtained through an educational space.

The level of financial literacy is one of the fundamental factors for the effective development of a country’s economy and welfare of the population. The implementation of a strategy for improving financial literacy in the Russian Federation is aimed primarily at creating a systemic basis for the development of financial literacy of citizens of the Russian Federation. Along with this problem, there is also the problem of insufficient levels of teachers of financial literacy, which differs from the methodology of teaching general education disciplines. The purpose of this study is to present an educational space for improving financial literacy based on the experience of Minin University as a means of implementing a strategy for improving literacy in the Russian Federation, which can be used in organizations of various levels of education for various categories of students. An educational space for improving financial literacy at Minin University is: • joint educational activities of all subjects of education, including teachers, students, practicing teachers, other segments of the population who need to increase the level of financial literacy, financial behavior, and financial culture, organizations, and institutions operating in the financial market; • a sociocultural environment designed in accordance with certain principles, motivating teachers and students for continuous development; and • multivariate model of education depending on the level and needs of education of teachers, students, as well as other categories of students.

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METHODOLOGY The level of financial literacy is an urgent problem not only in Russia, but also in many countries of the world. Based on research, both by an individual organization and by international organizations (World Bank, Merrill Lynch Foundation, Cambridge University, and Prudential Insurance) are developing international and national programs to improve the level of financial literacy (Vinnikova, 2017). The implementation of programs is carried out through an educational space. The practice of forming educational spaces has a rather diverse focus and forms of realization of educational activities, various conditions of functioning and a variety of structures of the organization of space, and diversity of learning technologies (Yashina et al., 2017). The concept of creating an educational space for improving financial literacy is based on the idea of necessity and opportunity to solve problems of insufficient level of financial literacy of different segments of the population (Myalkina, Polyakova, & Zhitkova, 2018). Conditions for the functioning of space are: • regulatory documentation based on a strategy to improve the financial literacy of the population; • personnel, material, economic, and other resources of Minin University, and so on; • need for the region to implement a program of financial literacy and consumer protection; • teaching staff—teachers of Minin University, as well as involved persons engaged in professional activities on financial market; • a contingent of recipients of educational content; • content of educational programs in the field of financial literacy; and • scientific and methodological support of the project (Kuryleva, Kurylev, & Lavrentieva, 2018). RESULTS Being one of the leading universities, Minin University implements a strategy of increasing financial literacy for all segments of the population through an organization of educational space (Figure 35.1). As part of the educational environment of improving financial literacy: • Students have an opportunity to receive additional education in the field of finance, application of existing knowledge working with different segments of the population.

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Development and implementation of a model for the formation of literacy of citizens in the field of socially significant services through the creation of an open educational service

Creation of an electronic service allowing use of services of a personal adviser/consulting services in areas

Creation of open online educational courses with certificate

Creation of open online educational courses of additional education “methodology of teaching social and economic disciplines with issuance of a document on additional education in field

Creation of open online educational courses with certificate

Figure 35.1  Educational space increases the financial literacy of the population.

• University expands network interaction with employers. • University acts as a platform for educational activities of financial institutions and financial organizations. • Employers have an opportunity to interact with various categories of citizens on financial services. • The population has an opportunity to gain additional knowledge and skills. Project of financial literacy aims to: • training of students which enables them to implement modules of educational programs and supplementary education programs for all ages within the framework of financial education; • implementation by students of educational projects in the framework of financial direction with different categories of citizens,

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• • •

•

•

including projects in a direction of volunteer activity within the framework of financial education (Garina et al., 2017); professional development of teachers implementing modules of educational programs; implementation of training programs, additional educational programs for all interested persons (Kuryleva, 2016); for implementation of the project, a content of programs to improve the financial literacy of citizens has been developed in such areas as: finance, taxation, insurance, economics, pedagogical education, and vocational training in the field of finance, and so on (Stolper & Walter, 2017); programs have different content and forms of training, different degrees of complexity depending on the category of listeners and level of training; and for the effective implementation of the project, an educational service “digital assistant” with a wide use of digital technologies is presented.

The service includes: • creation of open educational online courses of additional education “Methodology of teaching socioeconomic disciplines with the issuance of a document on additional education in the following areas; • creation of open online educational courses of additional education with the issuance of a certificate in the following areas: finance certificate “Personal Financial Consultant” or “Bank Agent,” taxation certificate “Tax Consultant,” insurance certificate “Insurance Agent,” professional training certificate “teacher of professional training,” and so on; • creation of an electronic service that provides the provision and use of the services of a personal adviser/consulting services in the following areas: finance, taxation, insurance, economics, vocational training, and so on (Garg & Singh, 2018); • creation of an information system that ensures the effective use of information from professional sites, for example: receipt of services in the field of insurance (https://www.gosuslugi.ru), filling in tax returns, drawing up reports (https://www.nalog.ru), selection of banking products: credits, deposits, and so on (Kuryleva et al., 2015); • creation of online courses “Information and Communication Technologies in Professional Activity” with the issuance of a certificate of professional development in following areas: finance, taxation, vocational training, insurance, economy, and so on (Hastings, Madrian, & Skimmyhorn, 2012).

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CONCLUSIONS Presented experience in creating an educational space for improving financial literacy allows us to solve one of the pressing problems of the modern economy—increasing the level of financial literacy of the population. Educational space proposed in this study includes: • financial literacy model and • educational service “digital assistant.” REFERENCES Garg, N, & Singh, S. (2018). Financial literacy among youth. International Journal of Social Economics, 45(1), 173–186. Garina, E. P., Kuznetsova, S. N., Garin, A. P., Romanovskaya, E. V., Andryashina, N. S., & Suchodoeva, L. F. (2017). Increasing productivity of complex products of mechanic engineering using modern quality management methods. Academy of Strategic Management Journal, 16(4), 8. Hastings, J. S., Madrian, B. C., & Skimmyhorn, W. L. (2012). Financial literacy, financial education and economic outcomes (NBER Working Paper. No.18412). Retrieved from http://www.nber.org/papers/w18412.pdf Kuryleva, O. I. (2016). Tax benchmarking as a method of reducing tax risks. In O. I. Kuryleva (Ed.), Collection of articles on materials of XII international scientific conference: In 4 parts. Modern problems of natural resources management and development of socioeconomic systems (pp. 174–179). Novgorod, Russia: Minin University. Kuryleva, O. I., Kurylev, A. I., & Lavrentieva, L. V. (2018). Application of the service “digital assistant” in training of specialists in the field of insurance. In E. V. Zlobin & T. V. Sarycheva (Eds.), Collection of works of the XIX International ScientificPractical Conference (pp. 506–508). Kuryleva, O. I., Vinnikova, I. S., Gurtovaya, N. S., Kuznetsova, E. A., Ogorodova, M. V., & Lavrenteva, L. V. (2015). The integration of financial and economic disciplines in the educational process of vocational education. European Research Studies Journal, 18(4), 219–222. Myalkina, E. V., Polyakova, E. A., & Zhitkova, V. A. (2018). Information-analytical service for calculating the profitability of educational programs as one of the key elements of the management system of educational programs in the university. Vestnik of Minin University 6(4), 2. https://doi. org/10.26795/2307-1281-2018-6-4-2 Stolper, O. A., & Walter, A. (2017). Financial literacy, financial advice, and financial behavior. Journal of Business Economics, 5, 581–643. Vinnikova, I. S. (2017). Features of application of electronic educational environment MOODLE in the study of discipline “financial analysis in the insurance organization.” In Collection of works of the XVIII International Scientific-Practical Conference: “Insurance in the system of financial services in Russia: place, problems, transformation,” (pp. 215–218). Kostroma, Russia: Kostroma State University.

Increasing the Financial Literacy of all Layers at Minin University    325 Yashina, N. I., Poyushcheva, E. V., Ogorodova, M. V., Lavrenteva, L. V., Semakhin, E. A., & Kuryleva, O. I. (2017). Theory and practice of human capital assessment in the context of innovative economic development. Man in India, 97(9), 43–52.

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CHAPTER 36

EXPERIENCE OF MODERN PEDAGOGICAL TECHNOLOGIES IN TEACHING PHYSICAL CULTURE AND SPORT IMPLEMENTATION Svetlana M. Markova Minin Nizhny Novgorod State Pedagogical University Lyubov I. Kutepova Minin Nizhny Novgorod State Pedagogical University Olga I. Vaganova Minin Nizhny Novgorod State Pedagogical University Zhanna V. Smirnova Minin Nizhny Novgorod State Pedagogical University Maxim M. Kutepov Minin Nizhny Novgorod State Pedagogical University

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ABSTRACT During the period of educational process technologization caused by the need to implement a competence-based approach, the need for modern educational technologies in teaching physical culture and sports is actualized and implementation is urgent. The chapter describes the possibilities of healthsaving technologies in maintaining students’ health and necessary skills for maintaining a healthy lifestyle development. Besides, we consider application of these skills in everyday life. Technologies of differentiated physical education involve consideration of individual abilities of students. Personality-oriented technologies implement such conditions in which a student is aimed at creative search and self-determination. The implementation of information and communication technologies allows increasing individual training, and contributes to students’ self-preparation improvement. There is a possibility of involving students in research activities providing flexibility in the learning process. Psychological and pedagogical technologies allow students to develop confidence in recreational sports activities necessary in their lives. The university implements principles of continuous physical education on the basis of taking into account individual needs of each person.

At the present level of pedagogical science development, it is necessary to pay attention to the development of a new type of thinking among high school students, associated with recognition of the need and significance of a healthy lifestyle and physical education. Technology in physical education is a combination of optimal and effective means, methods, and techniques that are aimed at achieving planned sports results or physical fitness indicators. An important place in educational activities is given to health-saving technologies, the purpose of which is to preserve students’ health throughout the entire period of study at a higher educational institution, as well as to form necessary skills for maintaining a healthy lifestyle and using them in everyday life. Health-saving educational technologies play an important role in influencing students’ health as they take into account students’ age characteristics and combine optimal motor and static loads making learning comfortable and attractive, motivating students to continue doing sports (Ilyashenko, 2018). In this case, the load is distributed according to principles of “increasing,” studying of exercises occurs sequentially using a visual example. Technologies of differentiated physical education require physical development of a student through the development of his individual abilities (Potashnik, Garina, Romanovskaya, Garin, & Tsymbalov, 2018). If trainees cannot perform this or that exercise, individual work is carried out. Teachers give individual assignments both to perform in class and at home. The absolute result and its increase are also taken into account. Such an

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interaction scheme gives a positive attitude to the performance of tasks, forming motivation among students which creates the basis for their activity. Personality-oriented technologies also allow students to develop individual abilities by building a creative atmosphere in class (Ilyashenko, Prokhorova, Vaganova, Smirnova, & Aleshugina, 2018). METHODOLOGY UNESCO describes pedagogical technology as a “systematic method of planning, applying, and evaluating the entire learning process and learning through the inclusion of human and technical resources and interaction between them to achieve a more efficient form of education” (Kuznetsov, Romanovskaya, Egorova, Andryashina, & Kozlova, 2018). The implementation of modern pedagogical technologies in physical culture and sports teaching implies the observance of the requirements of conceptuality, the continuous development of an athlete, creativity in the work of a coach, and reliance on a real educational and training process (Ilyashenko, Smirnova, Vaganova, Prokhorova, & Abramova, 2018). Modern educational technologies that we have identified make it possible to motivate a student to play sports not only as part of the educational process but also in their future independent life (Kutepov, Vaganova, & Trutanova, 2017). Health-saving technologies in high schools are implemented in the following forms: • the alternation of activities; • creation of a favorable friendly atmosphere at the lesson, • competent dosage of tasks (Smirnova, Mukhina, Kutepova, Kutepov, & Vaganova, 2018); • individual approach to each student (Smirnova, Gruzdeva, & Krasikova, 2017); • conducting classes in the open air (Orlov, 2010); and • use of games in training. Students, during spring and autumn periods are free to be engaged in physical culture at the stadium in the open air. The classes use different types of running, squats, pushups, playing football, and volleyball. In winter, classes are transferred to the gym. Health-saving technologies form communicative competence, helping students to establish teamwork. Personality-oriented technologies are targeting a personal approach to students. For example, students who cannot perform difficult physical exercises perform breathing exercises aimed at increasing ventilation of the

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lungs to prevent the onset of hypoxia with increased mental exertion (Yashin et al., 2017). When implementing this technology students are divided into three different levels: • by age, • by floor, and • in terms of health. In this regard, the surrender of standards and the amount of load also has differences. Complexes of exercises are differentiated by degree of difficulty. Information and communication technology allows students to master the theoretical part of the training more quickly (Sakharova, 2009). The programs that visualize the process under study on the monitor screen are widely used. Due to these technologies, the effectiveness of training is increased and training takes place within a shorter time (Iltaldinova, Filchenkova, & Frolova, 2017). RESULTS There are many definitions of the concept of “health-saving technologies.” Some researchers consider them an analogue of sanitary and hygienic measures. The structure of health-saving technologies includes: • Axiological component. It is thanks to this component that students consciously begin to allocate health as the highest value, gain conviction in the need to maintain a healthy lifestyle. • Epistemological component. Encouraging students’ interest in maintaining a healthy lifestyle. • The health component itself includes a system of values and ​​ attitudes to increase physical activity, prevent physical inactivity, and acquire hygienic skills. • The emotional-volitional component reinforces the desire to lead a healthy lifestyle in students (Annario, 1983). In Nizhny Novgorod State Pedagogical University named after Kozma Minin, physical education and health-improving technologies, which are part of health-saving, are carried out at physical education classes and afterhour sports and recreational activities. They are aimed at students’ physical development and, as a consequence, of productive activities in education and professional activity in the future.

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The university also uses psychological and pedagogical technologies that allow students to develop confidence in the benefits and necessity of recreational sports activities throughout their lives (Fedorov, Paputkova, Ilaltdinova, Filchenkova, & Solovev, 2017). Therapeutic technologies include elements of physical therapy, which allows restoring students’ physical health (Garina, Kuznetsov, Romanovskaya, Andryashina, & Efremova, 2018). At the university, when implementing health-saving technologies, they actively use physical education classes, university sports competitions, sports clubs, sections, as well as carry out educational activities (Vaganova, Koldina, & Trutanova, 2017). The implementation of differentiated physical education technologies, can divide students into three levels: according to age structure, by floor, and in terms of health; however, this is not an exhaustive list of levels, we have identified the most frequently used. For students who have any contraindications, exercises are selected that do not harm health, but strengthen it, or else the degree of load on each individual learner is regulated (here, interaction of personality-oriented and differentiated technologies is observed). CONCLUSIONS The considered technologies not only motivate a student to actively engage in higher school sports activities and maintain a healthy lifestyle, but also make him support an independent life after graduating from a higher educational institution. These technologies used in higher school contribute to the formation of students’ needs for physical activity, optimize the level of health and improve physical fitness as well as intellectual development. Health-saving technologies were identified as the most important, playing a dominant role in the physical development of students, however, it is worth noting that the technologies complement each other and the harmonious development and development of the student as an independent, productive specialist becomes possible only with the combination of the technologies we have used. The practice of their application at the university allows us to talk about their necessity and importance in future graduates development. REFERENCES Annario, A. A. (1983). Critical incidents in physical education planning and instruction: In chance strategy. In A. A. Annario (Ed.), International Journal of Physical Education, 19–22.

332    S. M. MARKOVA et al. Fedorov, A. A., Paputkova, G. A., Ilaltdinova, E. Y., Filchenkova, I. F., & Solovev, M. Y. (2017). Model for employer-sponsored education of teachers. Opportunities and challenges. Man in India, 97(11), 101–114. Garina, E. P., Kuznetsov, V. P., Romanovskaya, E. V., Andryashina, N. S., & Efremova, A. D. (2018). Research and generalization of design practice of industrial product development (by the example of domestic automotive industry) Quality-Access to Success, 19(S2), 135–140. Iltaldinova, E.Yu., Filchenkova, IF, & Frolova, S. V. (2017). Peculiarities of the organization of postgraduate support of graduates of the targeted training program in the context of supporting the life cycle of the teacher’s profession. Vestnik of Minin University, 3(20), 2. Ilyashenko L. K. (2018). Pedagogical conditions of formation of communicative competence of future engineers in the process of studying humanitarian disciplines. International Journal of Civil Engineering and Technology, 9(3), 607–616. Ilyashenko, L. K., Prokhorova, M. P., Vaganova, O. I., Smirnova, Z. V., & Aleshugina, E. A. (2018). Managerial preparation of engineers with eyes of students. International Journal of Mechanical Engineering and Technology, 9(4), 1080–1087. Ilyashenko, L. K., Smirnova, Z. V., Vaganova, O. I., Prokhorova, M. P., & Abramova, N. S. (2018). The role of network interaction in the professional training of future engineers. International Journal of Mechanical Engineering and Technology, 9(4), 1097–1105. Ilyashenko, L. K., Vaganova, O. I., Smirnova, Z. V., Gruzdeva, M. L., & Chanchina, A. V. (2018). Structure and content of the electronic school-methodical complex on the discipline “mechanics of soils, foundations and foundations.” International Journal of Mechanical Engineering and Technology, 9(4), 1088–1096. Kutepov, M. M., Vaganova, O. I., & Trutanova, A. V. (2017). Possibilities of healthsaving technologies in the formation of a healthy lifestyle. Baltic Humanitarian Journal, 6(3), 210–213. https://elibrary.ru/item.asp?id=30381912 Kuznetsov, V. P., Romanovskaya, E. V., Egorova, A. O., Andryashina, N. S., & Kozlova, E. P. (2018). Approaches to developing a new product in the car building industry. Advances in Intelligent Systems and Computing, 622, 494–501. Orlov, V. I. (2010). Method and educational technology. Pedagogy, 8, 30–38. Potashnik, Y. S., Garina, E. P., Romanovskaya, E. V., Garin, A. P., & Tsymbalov, S. D. (2018). Determining the value of the own investment capital of industrial enterprises. Advances in Intelligent Systems and Computing, 622, 170–178. Sakharova, M. V. (2009). Technological tools to coach team sports. In M.V. Sakharova (Ed.), Children’s coach, 3, 60–64. Smirnova, Z. V., Gruzdeva, M. L., & Krasikova, O. G. (2017). Otkrytyye elektronnyye kursy v obrazovatel’noy deyatel’nosti vuza. Vestnik of Minin University, 4, 3. Smirnova Z. V., Mukhina, M. V., Kutepova, L. I., Kutepov, M. M., & Vaganova, O. I. (2018). Organization of the research activities of service majors trainees. Advances in Intelligent Systems and Computing, 622, 193. Vaganova, O. I., Koldina, M. I., & Trutanova A. V. (2017). Development content of professional pedagogical education in the conditions of realization of competence approach. Baltic Humanitarian Journal, 6,2(19), 97–99 (in Russian).

Teaching Physical Culture and Sport Implementation    333 Yashin, S. N., Yashina, N. I., Ogorodova, M. V., Smirnova, Z. V., Kuznetsova, S. N., & Paradeeva, I. N. (2017). On the methodology for integrated assessment of insurance companies’ financial status. Man in India, 97(9), 37–42.

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CHAPTER 37

RETROSPECTIVE OF THE MENTORING SYSTEM Elena A. Chelnokova Minin Nizhny Novgorod State Pedagogical University Svetlana N. Kaznacheeva Minin Nizhny Novgorod State Pedagogical University Natalia V. Bystrova Minin Nizhny Novgorod State Pedagogical University Antonina L. Lazutina Minin Nizhny Novgorod State Pedagogical University Yuri S. Zhemchug Minin Nizhny Novgorod State Pedagogical University

ABSTRACT In modern science, it is relevant to consider the system of mentoring as the most effective way to use a human resource of an organization for solving strategic tasks. The need to revive the mentoring system in the country was recog-

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336    E. A. CHELNOKOVA et al. nized by modern managers engaged in the sphere of business, economy, and services. Changes in the economy lead to changes in management activities. Modern business has adopted a technique of mentoring and it gives preference to training and adaptation of young professionals. In the study, we used theoretical and empirical methods of cognition, methods of retrospective analysis of materials, analysis of literature, documents, analysis of works on the subject of research, method of analogy and comparison, method of generalization of results of research (Lewis, 2002). After analyzing the research on mentoring, we concluded that in the current situation, due to changes in the political and socioeconomic situation, attitude to mentoring has changed and it has not lost relevance. A proper organization of the mentoring system is able not only to transfer professional experience to subsequent generations but also to influence the process of education of young people, employees, and atmosphere of the organization. The effectiveness of mentoring is high in a process of increasing the efficiency of human resources.

A mentoring system was identified as the most effective way to use a human resource of the organization to solve strategic problems in today’s culture. Russia needs professional staff in all spheres of development and functioning of the country. Therefore, it is extremely urgent and necessary to introduce the institution of mentoring in order to improve the training of specialists (Garina et al., 2017). According to the American Forbes, employees with mentors receive a promotion five times more often. Mentors themselves are six times more likely to receive the next position (Chelnokova & Nabiyev, 2015). Fomin, interpreting the concept of mentoring, defines it as a “personally oriented pedagogical process,” designed to help a beginner to master the profession, determine for himself its importance in his life. Mentoring can create conditions under which a wide range of professional competencies and individual experience in solving production problems is acquired. The author stands out one of the promising forms of mentoring, which is that the young man is oriented on the example of his mentor. This form promotes stimulation of the young worker to acquire skills that are inherent in his mentor, promotes adaptation in the workplace and organization (Fomin, 2012). Along with the term “mentoring” use such concepts as “tutor” and “coaching.” However, the concepts of “mentor,” “tutor,” “coach,” and “facilitator” have their own peculiarities. “Coach” is an experienced employee, who knows how to organize the learning process based on partner relationships, and can inspire students to find solutions on their own. “Coaching” promotes the individual’s potential of trainees. Mentor (from Latin Mentos—intention, purpose, spirit; mon-i-tor— the one who instructs)—leader, mentor, teacher, tutor, and supervisor

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(Tyumaseva, Orekhova, & Yakovleva, 2018). The name of the mentor was worn by the hero of ancient Greek mythology, a wise adviser who had universal confidence. Mentoring is a purposeful transfer of experience by a more experienced employee to an intern in the “do like me” type. The word “mentor” is often heard from politicians, athletes, actors, and many others when they describe a person they chose as a role model or someone who has had a significant influence on destiny, career, that is, on the course of development of the person. We see that coaching and mentoring—similar in nature or concept, but there are some differences in the interaction of the teacher with his protégé. By the way, the protégé are students of mentors. Facilitator (from Latin Facilis—easy, convenient) is an experienced leader who ensures successful communication of the group using creative models of cooperative training (Chelnokova & Nabiyev, 2015). “Tutor”—“tuior” translated from English—teacher-mentor, teacherconsultant. The etymology of this word (from Latin tueor—care, protect) is related to concepts of “protector,” “patron,” and “guardian” (Gruzdeva, Prokhorova, Chanchina, Chelnokova, & Khaznina, 2018). Tutor is a consultant, curator, and teacher who helps students in their selfdevelopment, development of the educational program, professional development, or retraining. Accompanying, helping, being close to the ward on his entire educational path is the main task of the tutor. Tutorship is particularly developed in the education system, especially in its remote form. “Tutoring” is aimed at supporting the process of corporate training of a trainee, discussing the issues of transferring the experience of acquired knowledge into professional activity. When referring to the explanatory and etymological dictionaries, we see different interpretations of the term. The mentor is treated as a teacher, supervisor, teacher, tutor, instructor, as well as a leader, guru, maitre, edifier, moral teacher, shepherd, and so on. Definitions differ in essence. The word “mentor” means to guide to the path on which the student will continue to go independently. METHODOLOGY The fundamental method for this study is the theoretical method, including an analogy between the concepts of mentoring, tutor, and coaching. Method of comparison consisting in consideration of the interpretation of the concept of “mentoring” by different scientists; method of retrospective analysis of this process; and identification of the positive aspects of the use of mentoring in our days.

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RESULTS The universal property of mentor, recognized in various spheres, is the presence of high qualification and experience. Oriental practice as a mentor is called a guru or bodhisattva. Orthodox tradition gives a name of confessor to the spiritual mentor. Thinking about spiritual mentoring, different members of the church are united in their opinion: A mentor is an understanding and receiving person, ready to help and support in difficult times. Mentoring involves training in practical skills carried out by an experienced, authoritative, highly qualified employee directly in the workplace of young professionals (Poling, 2015). Mass mentoring movement came into domestic pedagogy and practice in the late 1950s to early 1980s of the last century and became an important direction of state policy. At this time, there was a rapid development of vocational education and technical education. The statesmen of the Soviet Union spoke about the need to introduce the institution of mentoring in the workplace and in educational institutions. They pointed out the importance of this type of activity for education. Government and state documents note the role of mentors not only as personnel workers with high skill, extensive life experience, but also as talented teachers. In Soviet times, a mentor was considered an honorable and respected person, because he was entrusted with the most important thing—the ideological, political, and professional development of the personality of the young worker. The honorary title “Honored Mentor of the Republic” was established and awarded in the Union republics. The need to revive the mentoring system in the country was recognized by modern managers engaged in the sphere of business, economy, and services. Changes in the economy lead to changes in management activities. Modern business has adopted the technique of mentoring and giving preference to training and adaptation of young professionals: “tutoring,” “coaching,” and “mentoring.” The orientation of mentoring on the solution of certain tasks has changed in different periods of development of the country. In the pre-war period, the task of mentoring was to introduce newcomers to the best practices of masters of high-performance labor (Vorobyova, Razumenko, & Semenova, 2016). During the war, mentoring is the main form of reproduction of labor in production. The postwar period was for mentoring, the beginning of the introduction of ideas of the scientific organization of work, while maintaining its main function of teaching the profession in the field, teaching rational methods of working with tools and accessories (Urmina, 2010). Since the 1970s, mentoring has been taking a mass movement. This was the result of the growth of the number of working youth in production and construction, changes in the relationship between people in the process of

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work in the conditions of scientific and technological development. There are various types of mentoring: group mentoring, student teams, Komsomol and youth teams for the development of related professions. The mentors performed certain tasks depending on the situation: • Situation of entry of a novice worker into production: The mentor is designed to support the positive emotional attitude of a young worker. • “Overcoming difficulties” situation: The mentor must transfer the necessary knowledge and skills to overcome difficulties and develop professional skills. • Situation of asserting the identity of a newcomer in a new quality: The mentor must ensure professional and psychological preparation of the young worker for independent professional activity. CONCLUSIONS Our study of the category of mentoring allowed us to identify a number of key points. Requirements for the professional competence of a mentor can be formulated as follows: the authority of the mentor is the main factor affecting a novice worker. The authority is formed not only by conviction, knowledge, skills, and professional skills but also by the strength of a personal example (Rai, 2002). The mentor must necessarily combine exactness and sensitivity. He must take into account the special features of the younger generation: thirst for all new things, intolerance for shortcomings, and desire for active activity, desire to prove them. But at the same time, it is necessary to remember that the beginner specialist lacks professional experience, skill of labor discipline, and a clear idea of the chosen professional activity. The positive aspects of the use of mentoring in the organization are: • Active growth of activity quality of a new employee. Beginners do not have to spend time to adapt to a new team, learning from the experience from a more experienced coworker; this has a positive impact on work contribution of the organization. • Improving the quality of activity not only benefits young workers, but also mentors themselves, as the latter acquire a sense of responsibility for their own knowledge, which they possess, and all the time try to improve them, which also has a positive impact on the quality of work. • Inclusion in the corporate culture of the organization of the young worker, sense of involvement and responsibility for their activities when obtaining knowledge from a mentor.

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REFERENCES Chelnokova, E. A., & Nabiyev, R. B. (2015). Tutor activity of the teacher to ensure successful adaptation of university students. Bulletin of Mininsky University, 3(11), 23. https://vestnik.mininuniver.ru/jour/search/search Fomin, E. . (2012). Diversification of the institute of mentoring as the potential of successful adaptation of the young specialist. Secondary professional education, 7, 6–8. Garina, E. P., Kuznetsova, S. N., Romanovskaya, E. V., Garin, A. P., Kozlova, E. P., & Suchodoev, D. V. (2017). Forming of conditions for development of innovative activity of enterprises in high-tech industries of economy: A case of industrial parks. International Journal of Entrepreneurship, 21(3), 6. Gruzdeva, M. L., Prokhorova, O. N., Chanchina, A. V., Chelnokova, E. A., & Khaznina, E. V. (2018). Post-graduate information support for graduates of pedagogical universities. Advance in Intelligent Systems and Computing, 622,143–151 https://doi.org/10.1088/1755-1315/115/1/012036 Lewis, G. (2002). Manager-mentor. Moscow, Russia: Balance Club. Poling, K. (2015). MySci advisors: Establishing a peer mentoring program for first years science students support. Collected Essays on Learning and Teaching, 8, 181–190. Rai, L. (2002). Development of effective communication skills. St. Petersburg, Russia: Piter. Tyumaseva, Z. I., Orekhova, I. L., & Yakovleva, N. O. (2018). Adaptation stage of the process of professional socialization of students of a pedagogical higher education institution. Education and Science, 20(1), 75–95. Urmina, I. A. (2010). Mentoring, its significance in history and modernity. Social policy and sociology, 7, 2010. Vorobyova, E. V., Razumenko, V. A., & Semenova, N. K. (2016). Comparative analysis of coaching and mentoring of the personnel of the organization, their characteristics. Young scientist, 12, 1193–1196. https://moluch.ru/archive/ 116/31385

PART IV POLITICAL AND LEGAL ASPECTS OF CREATING, IMPLEMENTING, AND DEVELOPING ARTIFICIAL INTELLIGENCE

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CHAPTER 38

THE METASCIENTIFIC ANALYSIS OF THE LEGAL REGIME OF ARTIFICIAL TERRITORIES IN THE INTERNATIONAL AND RUSSIAN LEGISLATION Elena A. Grin Kuban State Agrarian University Luiza T. Kokoeva North Caucasian Mining and Metallurgical Institute Anastasia S. Malimonova Kuban State Agrarian University

ABSTRACT The chapter is devoted to the comparison of the legal status of artificial land plots in Russian and international legislation. The authors outlined the his-

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344    E. A. GRIN, L. T. KOKOEVA, and A. S. MALIMONOVA tory of modern international maritime law and analyzed the rules relating to the legal status of artificial islands in the various maritime areas. The chapter deals with the issues of state sovereignty over man-made objects located in the sea. The authors made a comparison with the Russian legislation determining the legal status of artificially created land plots, and established the relationship between the concepts of “artificial island” and “artificial land plot.” The research materials were aimed at forming a holistic view of the problem of defining the legal regime of artificial land plots on the meta-scientific basis.

The process of natural accretion of land plots to the land surface was known in Roman law, and scientists have identified two ways of such process: due to alluvial soil and sand deposited by water, as well as the separation of a strip of land and its subsequent convalescence with another part of the land (avulsio). Russian prerevolutionary jurists went further and added two more methods to the mentioned above: the riverbed outcrops and the emergence of islands (Tyzhnov, 1858). As we can see until the end of the first half of the 20th century, researchers associated the emergence of land plots exclusively with the actions of natural factors. The relevance of this study is due to the fact that at the present stage of development of society, joining and the emergence of new territories have increasingly taken place through human activities, not natural processes. For example, China and Japan, by means of the construction of artificial islands, solve the problem of lack of territories. In addition to expanding the territory, the construction of Islands of unusual shape in the United Arab Emirates is also aimed at attracting more tourists. Russia also practices expanding the territories of seaports in the same way. There is no doubt that such activities require detailed legal regulation, both at the national and international levels. METHODOLOGY There are two levels of legal regulation of relations concerning artificial land plots, their legal status and protection: international and national. At the international level there are several documents that should be considered when studying this issue, for example the UN Convention on the Law of the Sea of 1982, the Geneva Convention on the Continental Shelf of 1958, Convention on the High Seas of 1958, Convention on the Continental Shelf and the Contiguous Zone of 1958. There are also regional international agreements, but their analysis is beyond the scope of this study. As far as national regulation is concerned, individual states are limited only so that their national legal acts do not contradict international ones. At the moment, many countries have legislation governing this branch of law. For example, in Germany, this area is generally regulated by the Federal Water Management Act, which is specified by the land laws. In the

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Russian Federation, an independent federal law is devoted to this issue. In other States, such as Austria and Quebec, certain provisions of the Civil Codes deal with issues of artificial accretion or creation of land. Thus, we see that many countries are actively engaged in legislative activities in the field of creation and use of artificial land plots. States do not neglect their right to carry out legal regulation of this area, which clearly confirms the relevance of this study. Among the array of international legal acts, the most interesting for us is the UN Convention on the Law of the Sea (n.d.), signed in 1982 and entered into force in 1994. The document is intended to regulate the regime of the main sea areas, such as the continental shelf, the high seas, the territorial sea and others, and also contains provisions on the legal status of artificial islands. RESULTS At the beginning of the 20th century, there was a general interest in the development of international law in the field of state jurisdiction over the seas caused by the expansion of the use of maritime spaces. In 1930, an international conference on the codification of international law was held in The Hague under the auspices of the League of Nations, which adopted a number of rules and regulations governing interstate maritime relations. It is important for us that the conference first defined the concept of “island” as a territory surrounded by water on all sides, located above the water level at high tide (Acts of the Hague Conference, 1930). It should be noted that this definition does not exclude its application to artificial land, provided that it is really a territory, and not just floating objects. By mid-century, there had been increased concern about charges on coastal fish stocks and the threat of pollution and waste from ships that crossed shipping routes around the world. States begun to make national demands on marine resources. It was necessary to quickly find a solution to this problem in order to avoid increasing contradictions and turning the sea into an arena of fighting. The first step was taken by U.S. President Truman, who issued a proclamation on September 28, 1945, extending the state’s jurisdiction over all natural resources on the continental shelf. This was the first serious challenge to the freedom of the sea. Other countries soon followed suit (The United Nations Convention on the Law of the Sea, n.d.). It took three conferences to adopt the 1982 UN Convention on the Law of the Sea. The first was held in 1958 and had been quite productive, as it succeeded in adopting four conventions: the Convention on the Territorial Sea and Contiguous Zone, the Convention on the High Seas, the

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Convention on the Continental Shelf, and the Convention on Fishing and Conservation of the Living Resources of the High Seas. These acts institutionalized the rules of customary law that had governed the use of maritime space for many centuries. This conference also addressed the issue of artificial territories, but only in the context of the continental shelf. Coastal states have the right to erect, maintain or operate on the continental shelf structures and other installations necessary for the exploration and development of its natural resources, as well as to establish security zones around these structures and installations and to take measures in these zones necessary for their protection. (Continental Shelf Act 1964, 2013, p. 11)

At the same time, it was noted that all such structures and installations do not possess the status of islands, which means that there is no territorial sea around them. Therefore, they do not increase the width of the territorial sea of the state that has authorized the construction of such structures. The definition of the concept “island” given in the convention on the territorial sea and the contiguous zone repeated the definition previously given at the international Hague conference of 1930, but an important feature defined as “naturalness” was added, that is, the term “island” began to mean exclusively natural objects. The second conference was held in 1960, its purpose was to determine the width of the territorial sea of the coastal states, but the participants were unable to agree and find mutually acceptable solutions. The representatives of Belgium presented a text on the continental shelf, which indicated that the latter could also be used for purposes other than the exploration and extraction of natural resources. They proposed to adopt a rule according to which structures created for other purposes will have a 500-meter protection zone, but they pointed out the need to maintain the condition that they do not possess their own territorial sea. However, the representative of Belgium pointed out that there were two problems in establishing the legal regime for such artificial sites. The first was to determine the jurisdiction to which the artificial territory would be subject. The second problem was the need to indicate the conditions under which it would be possible to erect structures. The solution of the first problem did not cause much difficulty: It was decided that the artificial territory would be subject to the jurisdiction of the state with whose continental shelf it was connected or if the territory was located in its exclusive economic zone (The UN Convention on the Law of the Sea, n.d.). With regard to the second problem, the 1982 Convention establishes that all states have the right to erect artificial territories on the high seas, provided that their construction does not violate the rights of other entities.

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According to Galea, the refusal to include in the text of the convention the proposals of Belgium and other states concerning artificial islands had led to the fact that the development of the legal regime of such territories took place outside the framework of the international convention, which raised the question about the legal status of artificially created areas in international law (Galea, 2009). Thus, in the 20th century there was a significant development of international maritime law, there were rules governing the legal status of artificial territories. The 1982 UN Convention applies the definition of “artificial island.” But along with it in the text there are other concepts: installations, structures, devices, and constructions. It can be concluded that under the convention, the term “structures” is collective for all man-made objects (Grin & Malimonova, 2017). Also, the convention explicitly differentiates platforms and man-made structures at sea. In our opinion, the reason is the different nature of these structures. Thus, offshore platforms are installed for a specific objective linked to human industrial activity, and are “objects of offshore fields development” (Daniltsev, 2008). For example, there are drilling, oil, floating, and other platforms. Hossein (2001) believes that theoretically the category of “artificial islands” is wider than “offshore installations.” This is due to the fact that the construction of installations is possible only to achieve a specific goal related to the study, operation, conservation, and management of living and nonliving marine resources, seabed resources, as well as other economic goals. However, the 1982 Convention does not define specific goals for the creation of artificial islands. International legal acts operate with a large number of similar terms, without giving their definitions. Scientists and law enforcement officials can only guess at their exact meaning and relationship between them. Finally, we came to the study of issues related to the extension of the jurisdiction of any state to such islands. It should be noted that artificial islands can be built in any zone of maritime space: in the territorial sea, the contiguous zone, on the continental shelf, in the exclusive economic zone, in the high seas. Part IV of the 1982 Convention states that artificial islands cannot be a part of an archipelago because islands connecting their waters and other natural formations are recognized as an archipelago. Thus, the construction of man-made structures can help to extend the land, but not the sea territory of the archipelagic state. Such areas are subject to the jurisdiction of the country, which creates an artificial island, but their international status differs from that of the natural parts of the archipelago. The sovereignty of a coastal state extends not only to its land territory and internal waters, but also to the territorial sea, its bottom and the

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airspace above it. But despite this, all countries have the right of innocent passage through the territorial sea. It seems that this duty also applies to the construction of artificial islands, which should also not obstruct the opportunity of the innocent passage. At the same time, a foreign state has no right to create or operate an artificial territory in the internal waters without the special authorization of the coastal state. The coastal state exercises sovereign rights over the continental shelf for the purpose of its exploration and development of its natural resources. These rights are not equivalent to sovereignty and can only be exercised for purposes established by international law. Such rights are exclusive, that is, if a coastal state does not explore the continental shelf or develop its natural resources, no one may undertake these activities without the express consent of the coastal state (Dzhunusova, 2007). However, it has jurisdiction over the operation and use of artificial islands. The rights of a coastal state to the continental shelf do not affect the legal status of the superjacent waters and the airspace above them (The UN Convention on the Law of the Sea, n.d.). The construction and operation of man-made islands are governed by the rules established for the exclusive economic zone. According to Article 2 of the Geneva Convention on the Continental Shelf, the rights of a coastal state to the continental shelf do not depend on occupation, effective or notional, or any express proclamation. It follows that the rights of a coastal state in respect of an area of the continental shelf exist by virtue of the very fact of its sovereignty over land, that is, it is an inalienable right. Thus, in order to exercise this right, there is no need to provide any documents or apply to the competent authorities. It should be noted that the construction of artificial islands on the continental shelf can only be associated with the exploration and exploitation of natural resources on the continental shelf. The coastal state has no right to erect or use such islands for any other purpose (Galea, 2009). Article 2 of the UN Geneva Convention on the Continental Shelf clearly states that a structure which is not used for the exploration or development of the natural resources of the continental shelf does not fall under the jurisdiction of a coastal state. With regard to the high seas, the Geneva Convention on the high seas codifies the rules of international law relating to all parts of the sea that are not part of the territorial sea or the internal waters of a state. Article 87 of the 1982 Convention stipulates that the high seas are open to all states. At the same time, states have the right to build artificial islands there, which creates problems related to the extension of state sovereignty to such islands. Haanappel (2003) believes that “man-made islands are subject to national sovereignty, but states that build artificial islands or other

Metascientific Analysis of the Legal Regime of Artificial Territories    349

objects on the high seas have exclusive jurisdiction over them, this is a kind of “quasi-sovereignty” (p. 37). The 1982 UN Convention precludes the use of the territorial sea around artificial islands, with the exception of a security zone extending up to 500 meters. The status of such security zones is not clearly defined. Thus, the establishment of security zones can create problems that will eventually lead the international community to conflict. The status of such zones and the powers of states in them must be clearly defined. Let us turn to the Russian legislation regulating these legal relations. There are a number of documents that are at different levels of the hierarchy of national legal acts, both laws and bylaws. It is noteworthy that the law provides definitions of the objects considered in this work, which distinguish them on the basis of the nature of the origin of these objects (bulk, alluvial, or other). At the same time, in Russian legislation, artificial islands belong to such a category of objects as artificially constructed structures, and artificial land plots are considered as structures. But there are other differences. Thus, an artificial land plot is legally recognized as a land plot after operational commissioning. Regarding man-made islands, such a possibility is not provided, so it means that they will always remain in the status of structures. Note that both types of objects are recognized as real estate. But the legal regime of land plots is different. In addition, these federal laws clearly establish that the structures under consideration on the continental shelf, in the exclusive economic zone, in internal sea waters, in the territorial sea of the Russian Federation are called artificial islands. At the same time, artificial land plots can only be created on water bodies that are in federal ownership. CONCLUSIONS Thus, having studied the differences between the terms “artificial land plot” and “artificial island,” we come to the conclusion that they are legal categories of the same level and relate to each other as generic and specific concepts. It is obvious that the main problem of both international maritime law and Russian law is the lack of a common terminology. The 1982 UN Convention neither gives a clear definition of these objects, nor establishes their status properly. We believe that in order to avoid misunderstandings, inconsistencies, and incorrect perceptions, it is necessary to clearly distinguish all the interchangeable terms used by the convention. In addition, it is necessary to create a basis for national law-making so that states can rely on internationally agreed terms and concepts in exercising their rights.

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REFERENCES Acts of The Hague Conference (1930). Vol. III. Convention on the Continental Shelf (concluded in Geneva on 29.04.1958), (1964). Vedomosti of the USSR Supreme Soviet, dated 8.07.1964, 28, Article 329. Continental Shelf Act 1964. (2013). Retrieved from https://www.isa.org.jm/files/ documents/EN/NatLeg/NZ-CSAct.pdf Daniltsev M. A. (2008). International legal and national legal status of objects located on the continental shelf, Jurisprudence, 6, 104. Dzhunusova D. N. (2007). Modern international legal regime of the continental shelf. Vestnik AGTU, No.1. Retrieved from https://cyberleninka.ru/article/n/ sovremennyy-mezhdunarodno-pravovoy-rezhim-kontinentalnogo-shelfa Galea F. (2009). Artificial islands in the Law of the Sea, Malta. Grin E. A., & Malimonova A. S. (2017). Artificial land plots in international law: UN Convention on the Law of the Sea. Scientific Journal of Kubgau, 132(08). Haanappel, P. (2003). The law & policy of air space and outer space, Kluwer Law International. Hossein, E. (2001). The legal regime of offshore oil rigs in international law. Dartmouth. The United Nations Convention on the Law of the Sea. (n.d.). (Concluded in Montego Bay 10.12.1982, as amended on 23.07.1994). Collection of Legislative Acts of the Russian Federation, No. 48, Article 5493. Retrieved from http:// www.un.org/Depts/los/convention_agreements/convention_historical_perspective.htm #The Future Tyzhnov, F. (1858). About increment in Roman law, compared to French and Russian legislations. Kazan.

CHAPTER 39

THE EXPERIENCE OF THE USE OF ARTIFICIAL INTELLIGENCE IN LEGAL PRACTICE Victoria N. Ostrovskaya Center of Marketing Initiatives Marine Z. Abesalashvili Adyghe State University Radmila E. Arutyunyan Pyatigorsk State University Raphael F. Mustafin Kuban State University Svetlana A. Mustafina Kuban State Agrarian University of I. T. Tulibin

Meta-Scientific Study of Artificial Intelligence, pages 351–358 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

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ABSTRACT The key topic of the chapter is a discussion of the possibilities, problems, and advantages of the practical application of artificial intelligence (AI) technologies in legal practice. In the study, the legal comparative research method was used to compare equivalent legal concepts in the field of AI and to conduct a comprehensive analysis of the features of civil law regulation of the use of digital technologies on the example of domestic and foreign legal regulation. The chapter considers trends in the development of the Legal Tech industry, which specializes in information technology services for professional legal activities. Despite the fact that AI technologies have appeared relatively recently, they are widely used in the field of law. In law enforcement practice, software for organizing an electronic document management system is widely used, platforms for a legal case tracking are organized, CRM systems and numerous digital databases of legal cases that facilitate the system for searching and analyzing court practice. The existing regulatory framework is not sufficient for the further successful development of AI. It is necessary to develop a set of rules for working with AI, which will provide for the efficient and effective interaction with it. Despite the active and widespread introduction of intelligent machines in various spheres of life, their huge potential for the public and private sectors, there is still, however, no strong regulatory framework of the use of AI in Russia.

Exploring the possibilities offered by artificial intelligence (AI), we can establish that digital transformation of all sectors of life is inevitable, that there is an active spread of digitalization tools in the economy, industry, education, health, and law. The public attitude towards intellectual technologies is still ambiguous, because there are still questions about the safety of using technology, its impact on social well-being and human rights. There are active discussions among lawyers about whether AI can cope with legal tasks. The impact of digital AI technologies determines the need for the development of modern civil law regulation in the context of digital transformation of legal activities using digital objects and digital technology platforms. METHODOLOGY More and more research in the field of domestic law is aimed at studying the phenomenon of AI. Morkhat (2017) carried out a fundamental research on civil law issues of legal personality of AI in the field of intellectual property law; Starovoitova (2018) studied AI and its influence on the formation of the development

The Experience of the Use of Artificial Intelligence in Legal Practice    353

of law, Nesterov (2017) analyzed legal relations and legal interactions between people and robots, and Ponkin and Redkina (2018) described the main possible approaches to the legal support of the use and development of AI systems. The authors used a method of comparative legal research, which involves comparing legal concepts from the field of civil law regulation of the use of AI technologies based on domestic and foreign experience. RESULTS The growing discussion of issues related to the establishment of the content and nature of the rights to the results created by AI systems determined the purpose of our work—to study scientific ideas about the features of the legal regime of results created by AI systems, as well as to consider and systematize the existing theoretical developments and experience in the use of AI in legal practice. Special terminology on AI began to appear in the 1960s and 1980s. It is believed that “the founders and leaders of AI research are Allen Newell, Herbert Simon, John McCarthy, Marvin Minsky, and Arthur Samuel” (Akulich, 2018, p. 5). They first used this term in a scientific sense, as well as in the sense of technology for creating “intelligent” machines, or “intelligent” computer programs. Many foreign authors, in particular, Nick Bostrom, Stuart Russell, Peter Norvig, pay much attention to the development of AI and the study of global problems facing humanity in connection with the prospect of the spread of intelligent machines (Bostrom, 2015). In Russia, the Explanatory dictionary on AI defines this term as a scientific direction in which the tasks of hardware or software modelling of those types of human activity that are traditionally considered intelligent and as a property of intelligent systems to perform functions (creative) that are traditionally considered the prerogative of man. (Averkin, Gaase-Rapoport, & Pospelov, 2019, n.p.)

Currently, existing projects and regulations in different countries are mainly strategies, concepts, and frameworks (Table 39.1). The panorama of legal documents in the world and Russia today shows that programs for supporting and developing AI technologies are paying more and more attention, and structures are being defined that are entrusted with the authority to regulate AI. And the current task is to form common approaches to the development of AI at the state level. The use of AI in legal practice will increase and cover new areas of application, providing new opportunities for solving legal issues (Figure 39.1).

Roadmap for the Development of Robotics in Europe

New Robot Strategy—Japanese Robot Strategy: Overview, Strategy, Action Plan

The Law on the Regulation of Autonomous Vehicle

The Legalization of the Use of Robotic Couriers

Plan for the Development of a New Generation of Artificial Intelligence

Project to Create a Committee on Artificial Intelligence

2015

2016

2017

2017

2017

Normative Legal Act

2015

Period

United States

China

Estonia

Germany

Japan

Europe

Country

(continued)

U.S. Congress is trying to define what AI actually means. Quartz, https:// qz.com/1154491/us-congress-is-trying-to-define-what-artificial-intelligenceactually-means/

Full Translation: China’s “New Generation AI Development Plan,” 2017. New America, https://www.newamerica.org/cybersecurity-initiative/digichina/ blog/full-translation-chinas-new-generation-artificial-intelligence-developmentplan-2017/

Estonia allows delivery robots on the sidewalks. Estonian World, http:// estonianworld.com/technology/24846/

Preparing for the Future of Transportation. Automated Vehicles 3.0, https:// www.transportation.gov/sites/dot.gov/files/docs/policy-initiatives/automatedvehicles/320711/preparing-future-transportation-automated-vehicle-30.pdf

New Robot Strategy. Japan’s Robot Strategy: Vision, Strategy, Action Plan, https://www.meti.go.jp/english/press/2015/pdf/0123_01b.pdf

Robotics 2020 Multi-Annual Roadmap for Robotics in Europe Horizon 2020 Call ICT-2016 (ICT-25 & ICT-26) Release B 03/12/2015, https://www. eu-robotics.net/sparc/upload/about/files/H2020-Robotics-Multi-AnnualRoadmap-ICT-2016.pdf

Source

TABLE 39.1  Overview of Legal Acts Related to Artificial Intelligence: Cross-Cultural Aspect

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Presentation of the Bill on Robotics

Strategies for the Development of the Information Society in the Russian Federation for 2017–2030

National Project “Digital Economy of the Russian Federation”

Draft Model Convention on Robotics and Artificial Intelligence

A Bill on the Use of Artificial Intelligence

Decree on the Creation of the American AI Initiative

List of Instructions for Implementing the President’s Address to the Federal Assembly for the Development of a National Strategy in the Field of Artificial Intelligence

2017

2018

2018

2018

2019

2019

Normative Legal Act

2017

Period

Russia

United States

United States

Russia

Russia

Russia

Russia

Country

List of instructions for implementing the President’s Address to the Federal Government Meeting. http://kremlin.ru/acts/assignments/orders/59898

Executive Order on Maintaining American Leadership in AI. https://www. whitehouse.gov/presidential-actions/executive-order-maintaining-americanleadership-artificial-intelligence/

A Bill S. 3502 September 26, 2018. https://www.congress.gov/115/bills/s3502/ BILLS-115s3502is. pdf

Model Convention on robotics and AI. Robopravo, http://robopravo.ru / modielnaia_konvientsiia

Decree of the Government of the Russian Federation No.195-R of February 12, 2019. http://publication.pravo.gov.ru/Document/View/0001201902190011

Decree of the President of the Russian Federation “On the Strategy for the Development of the Information Society in the Russian Federation for 2017– 2030” http://pravo.gov.ru/proxy/ips/?docbody=&firstDoc=1&lastDoc=1& nd=102431687

https://www.dentons.com/ru/insights/alerts/2017/january/27/dentonsdevelops-first-robotics-draft-law-in-russia

Dentons has developed the first Russian bill on robotics. Dentons

Source

TABLE 39.1  Overview of Legal Acts Related to Artificial Intelligence: Cross-Cultural Aspect (continued)

The Experience of the Use of Artificial Intelligence in Legal Practice    355

356    V. N. OSTROVSKAYA et al. Client AI technologies in legal practice 1) 2) 3) 4) 5)

document constructors; web platforms for searching for a lawyer; chat bots; online consultation; system to fill out documents online

Professional AI technologies in legal practice

AI in legal practice

1) 2) 3) 4) 5)

platforms for recording court cases; CRM system; electronic document management system; the neural network and the robot-lawyer; digital databases of legal cases Big Data

Foreign experience in using AI technologies in legal practice 1) 2) 3) 4) 5)

use in agreements on alternative payment models; analysis of data on mergers and acquisitions; artificial intelligence for creating documents on request; collecting and studying documents using the data set; research of legislation and law enforcement practice using AI

Domestic experience in the use of artificial intelligence technologies in the practice of law 1) work in the system for monitoring court cases and verification of the identity of contracting partners; 2) a system for searching and analyzing judicial practice; 3) the process of evaluating the mathematical probability of the outcome of the case; 4) forecast of the threat of bankruptcy of companies; 5) system of work organization and business management; 6) virtual legal assistant

Figure 39.1  The use of AI technologies in the legal practice.

It becomes obvious that Russia does not have a single regulatory document related to the development of artificial intelligence. At the same time, projects in the field of human activity intellectualization are actively developing and being implemented. In this regard, the main problems are not the

The Experience of the Use of Artificial Intelligence in Legal Practice    357

regulation of artificial intelligence technologies, but the areas of its application (“Application of artificial intelligence,” 2017). AI-based tools become smarter the more they work on the case, and as a result, they find more and more accurate data for you to build your work on. Analyzing case data using AI allows lawyers to visualize the conceptual relationships between stakeholders. Using AI to evaluate data makes it possible to consider cases more effectively, focusing the efforts of lawyers on key participants in the case or those types of data that are likely to reveal useful facts. CONCLUSIONS AND RECOMMENDATIONS In legal science, the issues of reducing the level of subjectivity and searching for a methodology that would allow making better decisions in lawmaking and law enforcement practices are seriously discussed. The transformation of the legal system with the use of new technologies, in particular AI, will provide assistance in informing, supporting, and advising persons involved in the process, carry out functions and actions that were previously performed by people, and change the way lawyers work. The main effects of the use of AI will be obtained by expanding the possibilities of automation and robotization in law, developing the conceptual thinking of professional lawyers, and eliminating subjectivity and irrationality in decision-making. REFERENCES Application of Artificial Intelligence Technologies in Russia: Experience. (2017). Pravo.ru. Retrieved from https://blog.pravo.tech/primenenie-tehnologij -iskusstvennogo-intellekta-v-rossii-opyt-pravo-ru/ A Bill S. 3502 September 26, 2018. Retrieved from https://www.congress.gov/ 115/bills/s3502/BILLS-115s3502is.pdf Akulich, M. (2018). Artificial intelligence and marketing. Moscow, Russia: Publishing Solutions. Averkin, A. N., Gaase-Rapoport, M. G., & Pospelov, D. A. (2019). Explanatory dictionary of artificial intelligence. Retrieved from http://www.raai.org/library/ tolk/aivoc.html#L208 Bostrom, N. (2015). Artificial intelligence: Stages. Menaces. Strategies. Moscow, Russia: Mann, Ivanov, and Ferber. Decree of the Government of the Russian Federation No. 195-R of February 12, 2019. (2019). Retrieved from http://publication.pravo.gov.ru/Document/ View/0001201902190011 Decree of the President of the Russian Federation “On the Strategy for the Development of the Information Society in the Russian Federation for 2017–2030.” (n.d.). Retrieved from http://pravo.gov.ru/proxy/ips/?docbody=&firstDoc= 1&lastDoc=1&nd=102431687

358    V. N. OSTROVSKAYA et al. Dentons Has Developed the First Russian Bill on Robotics. (2017). Dentons. Retrieved from https://www.dentons.com/ru/insights/alerts/2017/january/27/ dentons-develops-the-first-robotics-bill-in-Russia Estonia Allows Delivery Robots on the Sidewalks. (2017). Estonian World. Retrieved from http://estonianworld.com/technology/24846/ Executive Order on Maintaining American Leadership in Artificial Intelligence. (2019). Retrieved from https://www.nsf.gov/news/news_summ.jsp?cntn_id=297658& WT.mc_id=USNSF_51&WT.mc_ev=click Full Translation: China’s “New Generation Artificial Intelligence Development Plan.” (2017). New America. Retrieved from https://flia.org/notice-state-council -issuing-new-generation-artificial-intelligence-development-plan List of Instructions for Implementing the President’s Address to the Federal Assembly. (2019). Retrieved from http://kremlin.ru/acts/assignments/ orders/59898 Model Convention on Robotics and Artificial Intelligence. (2017). Robopravo. Retrieved from http://robopravo.ru/modielnaia_konvientsiia Morkhat, P. M. (2017). Artificial intelligence: A legal view: A scientific monograph. Moscow, Russia: Buki Vedi. Nesterov, A. V. (2017). Are legal relations and legal interactions between humans and robots possible? Legal world, 8, 57–60. New Robot Strategy. Japan’s Robot Strategy: Vision, Strategy, Action Plan. (2015). Retrieved from https://www.meti.go.jp/english/press/2015/pdf/0123_01b |.pdf Ponkin, A. V., & Redkina, A. I. (2018). Artificial intelligence from the point of view of law. Bulletin of the RUDN. Series: Legal Sciences, 22(1), 91–109. Robotics 2020 Multi-Annual Roadmap for Robotics in Europe Horizon 2020 Call ICT-2016 (ICT-25 & ICT-26). (2015). SPARC. Retrieved from https://www. eu-robotics.net/sparc/upload/about/files/H2020-Robotics-Multi-AnnualRoadmap-ICT-2016.pdf Starovoitova, O. E. (2018). Artificial intelligence and its influence on the formation of the development of law. Legal Science: History and Modernity, 1, 52–56. U.S. Congress Is Trying to Define What Artificial Intelligence Actually Means. (2017, December 12). Quartz. Retrieved from https://qz.com/1154491/ us-congress-is-trying-to-define-what-artificial-intelligence-actually-means/ U.S. Department of Transportation. (2018). Preparing for the Future of Transportation. Automated Vehicles 3.0. Retrieved from https://www.transportation.gov/ sites/dot.gov/files/docs/policy-initiatives/automated-vehicles/320711/ preparing-future-transportation-automated-vehicle-30.pdf

CHAPTER 40

ARTIFICIAL INTELLIGENCE AS AN OBJECT OF INTELLECTUAL RIGHTS Vitaly V. Kovyazin Stavropol Branch of RANEPA Elena V. Serdyukova Stavropol Branch of RANEPA Anna A. Minina Stavropol Branch of RANEPA Olga A. Perepadya Stavropol Branch of RANEPA Gennady V. Shevchenko Stavropol Branch of RANEPA

Meta-Scientific Study of Artificial Intelligence, pages 359–366 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

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ABSTRACT In the Russian Federation there is no strategy for the development of robotics at the state level. In order to solve this problem, the Government of the Russian Federation adopted a number of documents providing the regulation of legal issues related to the use of robotics, artificial intelligence (AI) tools, the adoption of national information security standards in the systems that implement cloud, foggy, quantum technologies, virtual and augmented reality systems, and AI technologies in 2019. Scientists propose to define AI technology as a computer or cyber-physical system with anthropomorphism (humanlike) “intelligence.” When forming the concept of AI, it is necessary to take into account the following characteristics: the ability to become autonomous, using sensors and/or exchanging data with their environment (compatibility); the ability to exchange and analyze these data; the ability to self-study on the basis of experience and interaction (optional criterion); the presence of at least minimal physical support; the ability to adapt their actions and behavior in accordance with the conditions of the environment; the absence of life from a biological point of view. Anthropomorphic robot interface connected with the technology of AI does not generate a new subject of law. In modern conditions, the legal significance is the identity of the author of the computer program, which is the main component of the technology of AI. The technology of AI is an object of copyright and patent rights of the author (creator) of such technology.

Adopted in 2017, the program Digital Economy is aimed at creating the necessary conditions for the development of the digital economy in the Russian Federation, in which data in digital form is a key factor of production in all spheres of socioeconomic activity, and which increases the competitiveness of the country, the citizens’ quality of life, ensures economic growth and national sovereignty (The Decree of the RF Government No.1632-p). Focusing on the Strategy of information society development in the Russian Federation for the period of 2017–2030, the program proceeds from the fact that the digital economy is a business activity, a key factor in the production of which are data in digital form, and it contributes to the formation of the information space, taking into account the needs of the citizens and the society in obtaining quality and reliable information, the development of information infrastructure of the Russian Federation, the creation and application of Russian information and telecommunication technologies, as well as the formation of a new technological basis for the social and economic spheres. According to Shwab, today we are at the origins of the fourth industrial revolution, which began at the turn of the new millennium. The first industrial revolution lasted from the 1760s to the 1840s. Its starting point was the construction of railways and the invention of the steam engine,

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which contributed to the development of mechanical production. The second industrial revolution, which began at the end of 19th and lasted until the beginning of 20th century, led to the development of mass production due to the spread of electricity and the introduction of the assembly line. The third industrial revolution began in the 1960s. Usually it is called the computer or digital revolution, as its catalyst was the development of semiconductors, the use of large computers in the sixties of the last century, in the seventies and eighties—personal computers and the Internet in the nineties. The main features of the fourth industrial revolution, which is based on the digital revolution, are the Internet, miniature manufacturing devices, artificial intelligence (AI), and learning machines (Shwab, 2016). The term AI was coined by John McCarthy in 1956 at a seminar, which he organized in Dartmouth’s summer research project on AI. The goal was to explore ways in which machines could be made to simulate aspects of human intelligence (Stone et al., 2016) METHODOLOGY Examples of applications of modern AI technologies include driverless cars, drones, surgical robots, robotic prostheses, interactive entertainment (e.g., virtual reality glasses), voice dialing on mobile devices, provision of tax and legal advice, automated trading on the stock market, prediction of future voting results of certain categories of voters based on their digital testing in social networks, credit risk forecasting, determination of the probability of relapse when considering the issue of parole, setting medical diagnoses, and so on. According to the world’s largest law firm, Littler, which deals exclusively with legal issues affecting employers, spending on robotics and AI in 2020 will reach $250 billion, in 2025 will exceed a trillion dollars, and after a while robotics and AI will become the largest industry in the world. It is assumed that by 2025, half of the jobs in the United States can be filled with self-learning machines and software (Haythornthwaite & Pierce, 2019) The program Digital Economy has initiated the creation of conditions for onset and development of the new fourth industrial revolution in our country. The main objective of the program Digital Economy is to increase the competitiveness of Russian goods from separate sectors of the Russian Federation economy in the global world market and the economy of the country as a whole. The Ministry of Economic Development of the Russian Federation has been approved as responsible for the implementation of the action plan

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in the area of “Regulatory Controls” (The Decree of the RF Government No.1632-p, 2017). According to the guidance of the Program in 2018 the conceptual program of priority measures to improve legal regulation for the development of the digital economy should be implemented, and this should lead to the removal of key legal restrictions for the development of the digital economy. Also the priority basic legal concepts and institutions necessary for the development of the digital economy should be determined. To achieve these goals it is planned to initiate about fifty draft Federal laws to the State Duma of the Russian Federation in 2018–2020 for consideration. As a matter of priority draft Federal laws No. 419059-7, No. 4190907, No. 424632-7 were introduced into the State Duma (The Federal Law Draft No. 424632-7, 2018). RESULTS Neznamov indicates that in Russia there is no strategy for the development of robotics at the state level. As a rule, such concepts (in countries where they exist) provide the basis for regulatory reforming. The latter, in its turn, becomes the first step towards the formation of the legislative foundations on robotics and AI. The absence of a formal concept significantly slows down the development of the industry, which leads to a technological lagging and, as a result, a gradual dependence of the country on foreign jurisdictions. This problem also applies to the legal regulation (especially standardization). The absence of any formalized concept of regulation leads to an excursive change in legal norms, prevents an adequate and timely response to new legal problems, and is a serious obstacle to the development of the industry that can lead to the imposition of standards. This is explicitly stated, for example, in the Resolution of the EU Parliament on P8_TA-PROV(2017)0051 Civil Law Norms on robotics. As one of the solutions to the problem Neznamov offers to consider developed by him in 2017 in collaboration with Naumov Model Convention on robotics and AI (Neznamov & Naumov, 2018). If we turn to national standards, our interest will be primarily focused on the national standard of the Russian Federation “Robots and Robotic Devices” (National State Standard R ISO 8373–2014), introduced on January 1, 2016 (National Standard of the Russian Federation, 2016). The standard uses the terms intelligent robot; robot with elements of AI (intelligent robot). In this case “a robot” is understood as a drive mechanism, programmed in two or more axes, which has some degree of autonomy, can move within its working environment, perform tasks by reading data from the environment, interact with external sources and adapt its behavior.

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Based on the above definition the elements of AI apparently should be defined as follows: • reading data from the environment, • interaction with external sources, and • adapting your behavior. In the Resolution of the European Parliament, as were the recommendations of the Commission on Civil Law Regulation in the field of robotics of the European Parliament, dated 16.02.2017, “Civil Law Norms on Robotics,” it is proposed to formulate and use general, universal definitions of the terms cyberphysical systems, autonomous systems, smart autonomous robots, taking into account the following characteristics of a smart robot: • the ability to become autonomous through using sensors and/or communicating with your environment (compatibility); • the ability to share and analyze these data; • the ability to self-study on the basis of experience and interaction (optional criterion); • at least minimal physical support; • the ability to adapt their actions and behavior in accordance with the conditions of the environment; and • absence of life from a biological point of view (European Parliament Resolution With Recommendations, 2017). Morhart (2017) defines the technology of AI as a computer or cyberphysical system with anthropomorphous (human) “intelligence.” At the same time, according to our Morhart’s personal definition, AI is fully or partially autonomous self-organizing computer-hardware virtual or cyberphysical, including bio-cybernetic system or unit, not alive in the biological sense of this concept, with the appropriate mathematical software, endowed/possessing software-synthesized (emulated) abilities and opportunities, such as: • anthropomorphic-reasonable mental and cognitive actions (implementation and demonstration of such actions), such as recognizing, understanding, interpreting, and generating images, symbolic systems and languages, reflecting, reasoning, modeling, figurative (meaning-generating and meaning-perceiving) thinking, generalizing, analyzing, and evaluating of information; • self-reflection, self-regulation, self-limitation, self-adaptation to changing conditions, autonomous self-support in homeostasis;

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• independent (autonomous) complex accumulation of information and experience; • independent (autonomous) implementation of genetic search (genetic algorithm) and information processing, that is, the implementation of a heuristic search algorithm with the preservation of important aspects of “parent information” for “future generations” of information; • learning and self-study (also on their own mistakes and their experience), self-development and self-application of self-study algorithms; • anthropomorphic-reasonable independent (autonomous), including creative, decision-making process, formulating and solving problems and performing tasks, proving mathematical theorems; • independent composing tests and algorithms for their own testing, self-implementation of self-testing and testing of virtual (computer) reality; and • when specified and provided capabilities (skills)—communication (interaction) with the physical reality, perception of signals on the sensory inputs (or their analogues) and responding to those signals, and independent testing of physical reality. According to Morhart (2017), on the basis of the analysis of the current degree of development of AI, there is no urgent need (at least—now and in the foreseeable future) to recognize AI units as a subject of copyright and patent rights. According to Kartskhiya (2017): the creation of complex objects (AI, analytical structures based on big data, self-managing systems like Smart Everything, etc.), built on the principle of integrated technologies, forms a request to expand the list of protectable intellectual property objects, to change the ways of legal protection in the digital space, to create a segment of digital services as a kind of intellectual property objects, to recognize rights to virtual objects of the digital ecosystem. (p. 25)

CONCLUSIONS Anthropomorphic robot interface connected with the technology of “AI” does not generate a new subject of law. In modern conditions, the legal significance is given to the author of the computer program, which is the main component of the technology of “AI.” The technology of “AI” is an object of copyright and patent rights of the author (creator) of such technology. In our opinion, it is necessary to supplement the list of types of “intellectual property” enshrined in article 1224 of the Civil Code of the Russian

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Federation with the concept of “cyber-physical system with AI.” The independence of such an object of intellectual rights is that it synthesizes a variety of independent types of intellectual property, in particular computer programs, inventions, and so on. It seems that in the medium term run, with the development of “AI” technologies, the most urgent problems of the theory of civil law and judicial practice will be, firstly, the issues of determining the persons responsible for causing harm through the cyberphysical system with AI to property, life, and health of the person. Such responsibility may be assigned to the author or copyright holder, and possibly distributed between them. Secondly, the expediency of giving the legal status of an “electronic person” to a “smart robot” will undoubtedly be discussed. This need is connected with the fact that if AI can replace a person in different areas of activities (medicine, education, law, banking, etc.), then its activities must be certified, licensed, and taxed according to the requirements for the corresponding areas of activity. REFERENCES European Parliament Resolution With Recommendations. (2017). Civil Law Rules on Robotics. Retrieved from http://www.europarl.europa.eu/doceo/ document/TA-8-2017-0051_EN.html Haythornthwaite, R., & Pierce, N. (2019). Artificial intelligence, robots, reskilling, and ethics—Fourth Revolution Board of Director Imperatives & the Chair’s Evolving Role. Retrieved from https://www.littler.com/publication-press/ publication/artificial-intelligence-robots-reskilling-ethics-fourth-revolution Kartskhiya, A. A. (2017). Digital imperative: New technologies create a new reality. Journal Intellectual property, 8, 17–26. Morhart, P. M. (2017). Artificial intelligence: Legal view. Moscow, Russia: Buki Vedi. National Standard of the Russian Federation. (2016). Robots and robotic devices. Retrieved from https://www.gost.ru/portal/gost/home/standarts/catalog national?portal:componentId=3503536e-2ac1-4753-8ed1 Neznamov, A. V., & Naumov, V. B. (2018). Strategy of regulation in robotics and cyberphysical systems. Journal Law, 2, 69–89. Shwab, C. (2016). The fourth industrial revolution. Moscow, Russia: Eksmo. Stone, P., Brooks, R., Brynjolfsson, E., Calo, R., Etzioni, O., Hager, G., . . . Tambe, M. (2016). Artificial intelligence and life in 2030. Retrieved from https://ai100. stanford.edu/sites/default/files/ai100report10032016fnl_singles.pdf/ The Decree of the RF Government No. 1632-p. (2017). On approval of the program “Digital Economy of the Russian Federation.” Retrieved from http://www .consultant.ru/document/cons_doc_LAW_221756/ The Federal Law Draft No. 419059-7. (2018). On digital financial assets. Retrieved from https://sozd.duma.gov.ru/bill/419059-7

366    V. V. KOVYAZIN et al. The Federal Law Draft No. 419090-7. (2018). On attracting investments using investment platforms. Retrieved from https://www.eg-online.ru/document/ law/396133/ The Federal Law Draft No. 424632-7. (2018). About modification of parts one, two, and four of the Civil Code of the Russian Federation. Retrieved from http:// www.consultant.ru/law/hotdocs/53046.html/

CHAPTER 41

PROBLEMS OF CRIMINAL LIABILITY FOR DAMAGE CAUSED BY AN UNMANNED VEHICLE Konstantin V. Chemerinsky Branch of the North Caucasus Federal University in Pyatigorsk Konstantin A. Amiyants Branch of the North Caucasus Federal University in Pyatigorsk Annette A. Mordovina Branch of the North Caucasus Federal University in Pyatigorsk Olga A. Zakharyan Branch of the North Caucasus Federal University in Pyatigorsk Aksana F. Bunina Branch of the North Caucasus Federal University in Pyatigorsk

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ABSTRACT Scientific and technological progress, rapidly gaining strength, gave rise to such a phenomenon as artificial intelligence (AI). In modern reality, the use of AI has become characteristic of almost all spheres of society and it can be assumed that the scale of its application will increase rapidly. No exception is the transport industry, in which unmanned aerial vehicles and unmanned vehicles have become a reality. Like any other area associated with human interaction and technology, the field of public relations needs legal regulation, however, in view of its novelty, there is almost no such regulation nowadays. At the same time, the functioning of autonomous unmanned vehicles will inevitably give rise to the problem of liability for damage caused by these technical devices, including damage of a criminal nature. The matter is aggravated by the fact that the nonstandard situation reveals complete unpreparedness of the criminal law system to respond to this problem. In the modern criminal law of any state there are no acceptable criminal-legal models of counteraction to the described criminal phenomenon. This is due to the fact that all known measures of criminal law impact are not applicable to such a phenomenon as a carrier of AI due to its specific nature.

In recent decades, the world is rapidly developing artificial intelligence (AI) technology, due to the achievements of mankind in the field of software development, big data processing capabilities, technological breakthrough in the field of cloud computing, and so on. These achievements have made possible the emergence of such a phenomenon as “unmanned” vehicles. If 20–30 years ago this was regarded as a utopia and it could be found only in science fiction, but today Russia starts an experiment on the use of public roads for unmanned vehicles. Similar experiments are carried out in other countries of the world. At the same time, the experience of the United States of America, where there was the first, ever fatal, accident of hitting a pedestrian by unmanned car produced by Uber company shows that the technology of unmanned vehicles, despite all the advantages, is fraught with great risks to human life and health, safety of property, and public safety. The problem of regulatory lagging behind the social processes, the emergence of which is due to the achievements of scientific and technological progress, is traditional for both Russian and global reality (Kozaev, 2016), therefore, the defining a strategy of criminallegal response to the abovementioned predicted dangerous phenomenon, is an urgent and timely task. METHODOLOGY The methodological basis of the research is a systematic approach in studying and understanding of social phenomena and processes. In the process

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of research, the following methods were used: logical and legal analysis of scientific researches on the topic of legal AI regulation in modern science; concrete sociological in the form of opinion poll of leading scientists and subjects of investigation of crimes; system-structural, connected with an assessment of potentials of criminal and legal system to counteract the emerging type of socially dangerous phenomena; content analysis of legal acts, documents, publications in the media, comparative analysis of regulatory approaches to the regulation of the use of AI in general; and unmanned vehicles, in particular, in foreign legislation. RESULTS According to the Draft Decree of the Government of the Russian Federation “about carrying out an experiment on the trial operation of highly automated vehicles on the roads of general use” the experiment in the use of socalled unmanned vehicles have been launched in two regions since January 3, 2019. At the initial stage, it is assumed that there would be a driver in the car controlling the process of movement and, if necessary, he has the opportunity to switch to manual mode of driving, however, as it is easy to imagine, this practice will be used only during the experiment. And in the future unmanned vehicles will become independent, autonomous road users. It should be noted that Paragraph 18 of the designated draft resolution of the Government of the Russian Federation contains the regulatory requirement concerning “full” liability of the owner of the highly automated vehicle for “road transport and other incidents on highways which occurred with participation of the highly automated vehicle belonging to the person.” However, the nature of this responsibility is not defined in the document, it seems that we cannot talk about criminal liability, if only because the issues of establishing the latter cannot be regulated by subordinate legislation, and in addition, the assignment of criminal liability for innocent behavior of the owner of the vehicle is contrary to the principle of guilt in the Criminal Law of Russia. The situation will become more complicated after the end of the experiment and the beginning of independent (autonomous) movement of unmanned vehicles on public roads. The fact is that the issues of criminal liability for damage caused by an autonomous technical device based on AI within the current Russian legal framework cannot be resolved in any way at all. It should be noted that the problem of regulation of the use of AI, including unmanned vehicles, attracts the attention of not only scientists but also legislators in different countries worldwide. Thus, in Germany in 2017, amendments to the Law on road traffic, regulating the use of “cars with automated driving function” were made, however, the problems of regulation

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of criminal liability for damage caused by an unmanned vehicle, in this law, the German legislator did not deal with. These problems are not covered in the legislation of other countries (South Korea, United States, France, Estonia), where certain attempts to regulate the activity of AI are made. In the scientific community, the problem of criminal liability for damage caused by the carrier of an AI (robot) is attracting more and more attention, but serious success in its solution has not yet been achieved. In the American legal doctrine, Hallevy, one of the authoritative experts on the problem under consideration, expresses the opinion on the need to develop a special concept of criminal liability of AI, which would take into account the actions of all the participants in the issues related to the creation, provision, implementation, and operation of robots (Hallevy, 2013). Clausen-Karlsson in his work notes the impossibility in the modern criminal-legal reality of bringing someone to criminal responsibility for the harm caused by an autonomous device with AI. In this regard, the author proposes to impose the obligation to control the working process and the responsibility for the technical device itself on its owner (Claussen-Karlsson, 2017). As for the Russian legislation, it should be noted that the owner of an unmanned vehicle cannot be criminally liable for the damage caused by this device, because he does not act culpably, that is, in his behavior there is no subjective component in the form of an appropriate form of guilt, and the imposition of liability without guilt in the criminal law system of Russia is not provided. But in the criminal law of the United States there is a concept of “strict liability,” which makes it possible. In the theory of criminal law, the question of the possibility of recognition as a subject of criminal responsibility the “carrier” of AI, that is, the technical device itself (Kibalnik & Volosyuk, 2018), was also considered. As a result of the study, these authors came to the conclusion that to date, the answer to the question can only be negative, since the criminal legal prosecution of AI carriers is devoid of any meaning. However, as the authors summarize, if in the future such machines will be able to acquire such purely human qualities as will, emotions, experiences, that is, will acquire a “soul,” the question of criminal liability of robots can become relevant (Kibalnik & Volosyuk, 2018). An independent position on the problem under consideration is expressed by Radutniy, proposing to recognize the carrier of AI as a new entity in criminal law relations, which has the characteristics of both physical and legal persons, and to provide special “criminal law measures in relation to electronic entities,” but neither the essence nor the content, or the specificity of these measures, the author does not disclose in the work (Radutniy, 2017). In relation to unmanned vehicles, as well as carriers of AI in general, the approach used by the criminal law systems of many countries in relation to the criminal liability of legal entities is not applicable. Recognizing

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an organization as a subject of criminal liability, in countries where such liability is provided, the conduct of an individual committed in favor or in the interests of the organization is recognized as its basis. In this case the liability of a legal entity comes along with the liability of an individual whose conduct served as the basis of liability. As for the carriers of AI, this approach is not applicable, since the activity of the technical device does not correlate with human behavior, otherwise we can talk about the recognition of the device as a means or instrument of crime, which does not cause any collisions in the criminal law assessment and in modern reality. CONCLUSIONS The analysis of the legislation, doctrinal positions, opinions of respondents on the problem of criminal liability for damage caused by an unmanned vehicle, as well as in general by the carrier of AI, allows us to conclude that in the modern world there is no adequate approach to the possible solution of this problem. All the tools available in the arsenal of Criminal Law to counter criminal behavior are not applicable to the issues under consideration. At the same time, the need for a criminal-legal response to the cases of harm caused by an unmanned vehicle from a single, may soon become quite common or even mass. This indicates the urgent need to develop a conceptually new approach to the criminal law assessment of the emerging socially possibly dangerous phenomenon, which requires an active discussion of the problem among scientists and practitioners. REFERENCES Claussen-Karlsson, M. (2017). Artificial intelligence and the external element of the crime. Örebro, Sweden: Orebro University. Hallevy, G. (2013). When robots kill: Artificial intelligence under criminal law. Lebanon, NH: University Press of New England. Kibalnik, A. G., & Volosyuk, P. V. (2018). Artificial intelligence: Questions of criminal law doctrine awaiting answers. Bulletin of the Nizhny Novgorod Academy of the Ministry of Internal Affairs of Russia, 4(44), 173–178. Kozaev, N. Sh. (2016). Modern problems in criminal law due to scientific and technological progress: Auto abstract, thesis for doctoral degree. Krasnodar, Russia: Krasnodar State University. Radutniy, O. E. (2017). Criminal liability of the artificial intelligence. Problems of Legality Journal, 138, 132–141.

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CHAPTER 42

SECURITIZATION OF THE PROBLEM OF POLITICAL BOTS Gennady V. Kosov Pyatigorsk State University Sergey A. Nefedov Pyatigorsk State University Galina V. Stankevich Pyatigorsk State University Arsen V. Gukasov Pyatigorsk State University Nadezhda Yu. Shlyundt Pyatigorsk State University

ABSTRACT In this chapter, reflecting the results of the author’s research, the securitization of the problem of political bots, which has recently become increasingly important, is considered. Based on the principles of the constructivist para-

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374    G. V. KOSOV et al. digm, the theories of securitization and information confrontation, as well as using the results of content analysis of the media space, the authors conclude that the problem of political bots is actively subject to securitization by various actors of world politics. Proving the increasing importance of bots in the political struggle, which are programs that operate in an autonomous mode and manage accounts in social networks, the authors associate the processes of securitization with the tasks of ensuring political security, which acquire special importance especially in the era of the rapid development of information and communication technologies. The chapter highlights three stages of the securitization process, namely problematization, politicization, and emergence, which are clearly visible in relation to the problem of political bots. The authors came to the conclusion that at present the problem of political bots is at the stage of emergence, during which the authorities are trying to find a solution to it, assuming the use of emergency measures. This state of affairs has serious political implications that require scientific reflection.

The development of information and communication technologies has changed the political reality, with the result that the sphere of exchange and dissemination of communications has undergone a fundamental transformation. Every day it is more difficult to imagine political life without the use of technological advances, from the simplest to the most complex, including those that involve the use of artificial intelligence algorithms. Among other things, political bots are gaining popularity. These are automated programs that manage an account for the purpose of disseminating the desired political information. Political bots have already become a problem that worries the authorities in various countries, which is noticeable in the number of their mentions in the media and in political discussions. Moreover, the threats they pose force states and countries to seek innovative solutions that would protect their power and relevant resources in the new conditions of information confrontation. In this context, the study of securitization of the problem of political bots becomes particularly relevant. METHODOLOGY The presented study was carried out within the constructivist approach using the provisions of the theory of securitization and information warfare, as well as complex content analysis. To date, the constructivist approach to the study of international relations has received full scientific recognition, being applied to the study of a wide range of issues. According to the provisions of this approach, the conduct of states is not determined by material conditions, but by the ideological perception of those conditions by the states. States are considered anthropomorphic actors whose interests are directly related to their identity. Therefore, patterns of international relations

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are formed under the influence of subjective rather than objective processes, to be more precise intersubjective processes, taking into account the multiplicity of those involved in contacts with each other (Wendt, 1999). Security turned out to be one of the first concepts revised by constructivists through their own analytical prism. For them, security is the protection not only from real, but also from constructed, perceived threats, and securitization is the process of turning a potential danger into an inevitable threat that requires an immediate solution. Securitization is essentially a speech act based on a historically and socially institutionalized set of meanings. In other words, what is of interest in the securitization process is not the threat per se, but something that is assigned to it. The reverse process is considered to be the process of desecuritization, implying the reduction of the imminent threat to a potential danger. Securitization is accompanied by extraordinary measures that go beyond conventional or traditional political procedures (Buzan, Wæver, & de Wilde, 1998). Since the constructivist approach and the theory of securitization emphasize perceived threats, special importance is attached to information attacks and information aggression. Bulk of research dedicated to the propaganda, begun by Lippmann and Lasswell, today is quickly growing due to the number of works about the potential of digital media and Internet communications in the field (Hood & Margetts, 2007). With increasing frequency the ideas that digital media and online communication have significantly changed the political landscape, turning it into an effective instrument of gaining and retaining power, are expressed (Howard & Hussain, 2013; Volochaeva, Kosov, Rakhno, & Solovyova, 2012). It is often emphasized that by democratizing the information field in some way, they strengthen the control of already dominant actors and promote authoritarian tendencies (Turner, 2006). In modern political science analysis of media messages about the event of interest to the researcher, which helps to give a complete description of it and to identify the existing cause-and-effect relationships, is quite common. In our study, we also used this method to conduct content analysis of credible news reports regarding the use of political bots (Krippendorff, 2013). RESULTS The Growing Role of Political Bots Political bots have become a powerful force shaping public opinion. In the report sent in August 2014 by Twitter to the commission on securities and stock exchanges of the United States, it was emphasized that about 8.5% of the accounts, administered by the company in the social network,

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are operating in automatic mode (Twitter, Inc., 2014). In May 2017 a team of scientists, based on the results of their own research, claimed that in the Twitter network from 9% up to 15% of all accounts are served by bots (Varol, Ferrara, Davis, Menczer, & Flammini, 2017). A political bot is a working in automatic mode program that manages the account with the aim of spreading the desired political information. Simulating real activity of users, political bots perform a wide range of tasks: publish content, write positive and negative comments to posts, increase the popularity of an account of a politician with a certain purpose and even, as practice shows, serve as a means of broadcasting threats to opposition activists. Experts predict, in the near future we can expect the appearance of bots that could create audio and video confirmation of unreal events and personalities (DiResta, Little, Morgan, Neudert, & Nimmo, 2017). The fact that bots have become an integral part of modern political life is well illustrated by the diplomatic crisis in Qatar. In April and May 2017, Saudi Arabia, the UAE and Bahrain recorded an unprecedented increase in new Twitter accounts, the most popular social network in the Gulf region. As shown by subsequent studies, in particular by Jones (2017), the overwhelming majority of the accounts were managed by the bots that distorted reality in the interests of the respective governments. By May, 24, when the website of Qatari news Agency was subjected to a hacker attack, the network of bots posted a lot of posts accusing Qatar of relations with Iran, the Muslim brotherhood, Hamas and Hezbollah, leading a number of anti Qatar hashtags to the regional trend, for example, “Qatar sponsors terrorism” and “Gaddafi of the Persian Gulf.” The bots have prepared fertile ground for the fake statements which would confirm the assumptions. The severance of diplomatic relations on 5 June no longer seemed an unexpected and strange event, contrary to the idea of the unity of the Arab monarchies. Qatar, as other studies have shown, also took advantage of new technological opportunities by deploying its army of bots. Thanks to these bots, the trend turned out to be trolled anti-Qatar hashtags, such as “Tamim the Great” and “Qatar is not alone.” Thus, Nimmo (2018) found that on many accounts in just a few seconds there were more than 200 retweets supporting Qatar, proving the use of bots, since a human is incapable of such activity. Bots and Political Security The theory of securitization implies the distinction between the actor initiating securitization and the reference object, that is, the good that must be preserved despite the threat posed to it (Buzan et al., 1998). And if the identification of the initiating actor in relation to the bot problem is not

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difficult and doubtful, they are states and countries as a rule, but the answer to the question of the reference object requires some explanation. In the 1990s in the scientific discourse there was a revision of the long-dominant neorealist concept of “security,” which suggested that only other states with material resources for military intervention could pose a threat to a single country (Rotschild, 2007). More and more events pointed to the fact that in the modern world there are much more threats to a state, both in terms of their sources and in terms of their content and environment of implementation. Firstly, threats to a single state can come not only from other states, but also from various transnational and subnational non-state actors, as well as from phenomena and processes unrelated to the sociopolitical reality—ozone depletion, global warming, the spread of HIV, poverty, and so on. Secondly, the threat can be not only a military attack, but also a financial blockade or formation of undesirable public discourse. Thirdly, threats can be formed not only in the physical, but also in the information space. All these changes have led to the development of different subspecies of security. In addition to the military, scientists began to talk about economic, environmental, social, as well as the type directly related to our study, political security (Medvedev, Kosov, Gundar, Gundar, & Taranova, 2002). Problematization–Politicization–Emergence The tasks of ensuring political security lead to the securitization of the problem of political bots. In fact, securitization is a process that includes three stages: problematization, politicization, and emergence. Problematization is the thought process that turns a circumstance into a problem or an obstacle, to overcome which only new tools and methods can be used. In this context, the problem means not so much the obstacle itself, but the attitude of the actor towards it matters a lot. A lot of journalistic materials on the use of bots in separate countries, as well as analytical articles, which attempt to comprehensively cover the new phenomenon, are published (Dubbin, 2013; Urbina, 2013). The stage of problematization is followed by the stage of politicization. The term “politicization” is widely used by politicians, journalists (Stankevich & Kosov, 2013; Kosov & Nefedov, 2013), in at least three meanings. Firstly, politicization is an increase in public interest in political activity, usually accompanying the process of democratization. Secondly, politicization is an increase in the importance of political activity relative to other areas of social activity, usually accompanied by an increase in authoritarianism. Thirdly, politicization is a cognitive phenomenon of giving political meaning to an event or a process that is nonpolitical in its origin and nature. The results of our study show that the problem of bots has entered the political

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agenda of the modern world, acquiring, of course, a political sound. Below are only the most vivid and illustrative cases. In December 2016, the world’s leading media reported that the U.S. intelligence community had prepared a report claiming that Russia was using a network of bots to support Donald Trump in the past presidential elections (Gayle, 2016). Information in the report, which was published in January 2017 (ODNI, 2017), caused a strong political reaction at various levels of the state vertical. So, Democrats and Republicans in the Senate made a joint statement, noting that Russia’s attempts to influence the elections should alert every American. They called for a thorough investigation into a foreign cyber attack that poses a serious threat to national security. Barack Obama ordered a more profound study of the elicit facts, the results of which were to be presented to the public before he left his post (CBS News, 2016). Since then, the topic of bots has occupied a significant place on the American political agenda. In Italy, the problem of political bots began to politicize against the background of the referendum in 2016, which was submitted to the question of changing the Constitution. On the eve of the referendum, the results of which forced Prime Minister Matteo Renzi to leave his post, he complained to Western leaders that Russia is interfering in Italian politics, spreading false news on social networks with the help of a network of bots. Then Laura Boldrini, who occupied the post of chairman of the lower house of the Italian Parliament at that time, admitting the fake news in social networks a problem that cannot be ignored, met with one of the leaders of the Facebook to discuss the possibility of limiting the activities of bots (Horowitz, 2016). The problem of bots in the Italian political discourse was again actualized before the parliamentary elections held in March 2018 (Albanese, 2018). Taking into consideration all these concerns, Prime Minister Paolo Gentiloni, who succeeded Matteo Renzi, officially warned about the likelihood of conducting online campaigns to influence public opinion (Horowitz, 2018). Securitization ends up with an emergency, a stage in which the authorities try to find a solution to a politicized problem, allowing for the use of unsual, extraordinary measures. As early as late 2017, U.S. lawmakers began to attempt to influence the activities of private companies Twitter and Facebook, criticizing them for their inability to prevent the spread of negative information about American policy. Representatives of these companies were repeatedly invited to the hearings of the relevant committees of the Senate and the House of Representatives, where they made reports on the scale of the use of bots, the means used to combat them and the prospects for political control (Swaine, 2018). In November 2018, U.S. Congressman Mark DeSaulnier introduced a bill, involving after detailed study, the development of measures targeting the States to fight with “undesirable” bots.

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According to his point of view, despite the threat posed by automated accounts, the United States still has not developed a single mechanism for their regulation (Gureeva & Solomatina, 2018). In November 2018, Deputy Minister of digital development and mass communications of the Russian Federation Alexander Volin said that Russia is ready to create alternative Twitter and Facebook social networks (NTV, 2018). CONCLUSIONS The study showed that the securitization of the problem of political bots is currently being completed. The process of securitization includes three stages, each of which was observed and is observed in relation to the problem of political bots. The problematization of the use of political bots has captured the period from 2011 to 2016, when the relevant topics became an integral part of the public discourse. The other two stages took much shorter time. Politicization of the problem of political bots occurred in the period from 2016 to 2017, when it began to attract the attention of the authorities, more or more acquiring a political twist. Finally, the emergence began in 2017 and continues up to this day. Today, at the beginning of 2019, authorities in many countries are discussing and taking measures, often unusual and extraordinary, to neutralize “undesirable” political bots. However, the completion of the securitization of the political bots problem entails a number of political consequences, in particular the improvement of the tools of political propaganda and the emergence of a new arena of political confrontation, requiring additional research. REFERENCES Albanese, C. (2018, February 19). Now bots are trying to help populists win Italy’s election. Retrieved from https://www.bloomberg.com/news/articles/2018-02-19/ now-bots-are-trying-to-help-populists-win-italy-s-election Buzan, B., Wæver, O., & de Wilde, J. (1998). Security: A new framework for analysis. Boulder, CO: Lynne Rienner. CBS News. (2016, December 11). Senate Republicans join Democrats in calling for probe of Russian electioneering hacks. Retrieved from https://www.cbsnews.com/ news/senate-republicans-join-democrats-probe-russian-electioneering-hacks DiResta, R., Little, J., Morgan, J., Neudert, L.M., & Nimmo, B. (2017, November 2). The bots that are changing politics. Retrieved from https://motherboard.vice .com/en_us/article/mb37k4/twitter-facebook-google-bots-misinformation -changing-politics Dubbin, R. (2013). The rise of Twitter bots. Retrieved from https://www.newyorker .com/tech/annals-of-technology/the-rise-of-twitter-bots

380    G. V. KOSOV et al. Gayle, D. (2016, December 10). CIA concludes Russia interfered to help Trump win election, say reports. Retrieved from https://www.theguardian.com/us-news/2016/ dec/10/cia-concludes-russia-interfered-to-help-trump-win-election-report Gureeva, Y., & Solomatina, O. (2018, November 13). It’s easier to explain the failures: The US Congress proposed to study the impact of bots on the elections. Retrieved from https://russian.rt.com/world/article/572864-ssha-boty-vybory Hood, C. C., & Margetts, H. Z. (2007). The tools of government in the digital age. Basingstoke, England: Palgrave Macmillan. Horowitz, J. (2016). Spread of fake news provokes anxiety in Italy. Retrieved from https://www.nytimes.com/2016/12/02/world/europe/italy-fake-news.html Horowitz, J. (2018). Will Russia meddle in Italy’s elections? It may not have to. Retrieved from https://www.nytimes.com/2018/03/01/world/europe/italy-electionrussia.html Howard, P. N., & Hussain, M. M. (2013). Democracy’s fourth wave? Digital media and the Arab Spring. Oxford, England: Oxford University Press. Jones, M. (2017). Hacking, bots, and information wars in the Qatar spat. In M. Lynch & S. Dahle (Eds.), The Qatar Crisis, POMEPS Briefings 31 (pp. 8–9). Washington, DC: George Washington University. Kosov, G. V., & Nefedov, S. A. (2013). Some methodological aspects of the analysis of ecopolitical violence. Power Journal, 11, 149–152. Krippendorff, K. (2013). Content analysis: An introduction to its methodology. Thousand Oaks, CA: SAGE. Medvedev, N. P., Kosov, G. V., Gundar, O. N., Gundar, E. S., & Taranova, N. O. (2002). Tolerance as the basis of social security. Stavropol, Russia: Stavropol School of Service. Nimmo, B. (2018, September 19). Robot wars: How bots joined battle in the Gulf. Retrieved from https://jia.sipa.columbia.edu/robot-wars-how-bots-joined-battle-gulf NTV. (2018). The ministry of communications has said it is ready to create the replacement to YouTube and Facebook. Retrieved from https://www.ntv.ru/novosti/2098661/ ODNI. (2017). Background to “assessing Russian activities and intentions in recent US elections: The analytic process and cyber incident attribution. Retrieved from https:// www.dni.gov/files/documents/ICA_2017_01.pdf Rotschild, E. (2007). What is security? International Security Journal, III, 1–34. Stankevich, G. V., & Kosov, G. V. (2013). Politicization of the religious factor: Islamic projection. Islamic Studies Journal, 3(17), 15–21. Swaine, J. (2018, January 19). Twitter admits far more Russian bots posted on election than it had disclosed. Retrieved from https://www.theguardian.com/technology/ 2018/jan/19/twitter-admits-far-more-russian-bots-posted-on-election-than-it -had-disclosed Turner, F. (2006). From counterculture to cyberculture: Stewart Brand, the whole earth network, and the rise of digital utopianism. Chicago, IL: University of Chicago Press. Twitter, Inc. (2014). Quarterly report pursuant to section 13 or 15(d) of the Securities Exchange Act of 1934: For the quarterly period ended June 30, 2014. Retrieved from https://www.sec.gov/Archives/edgar/data/1418091/000156459014003474/ twtr-10q_20140630.htm

Securitization of the Problem of Political Bots    381 Urbina, I. (2013, August 10). I flirt and tweet. Follow me at #socialbot. Retrieved from https://www.nytimes.com/2013/08/11/sunday-review/i-flirt-and-tweet -follow-me-at-socialbot.html Varol, O., Ferrara, E., Davis, C. A., Menczer, F., & Flammini, A. (2017). Online human-bot interactions: Detection, estimation, and characterization. In Proceedings of the Eleventh International Conference on Web and Social Media in Montreal, Canada (pp. 280–289). Palo Alto, CA: AAAI Press. Volochaeva, O. F., Kosov, G.V., Rakhno, N. V., & Solovyova, E. A. (2012). Political processes in the context of geoinformation paradigm: Mechanisms, vectors of development. Stavropol, Russia: Stavrolit. Wendt, A. (1999). Social theory of international politics. Cambridge, England: Cambridge University Press.

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CHAPTER 43

THE REALIZATION OF THE POTENTIAL OF ARTIFICIAL INTELLIGENCE IN CRIMINAL LAW, PROSPECTS OF DEVELOPMENT OF FORENSIC PROFILING Lev V. Bertovsky People’s Friendship University Natalia S. Burmistrova Pyatigorsk State University Evgeny N. Petukhov Altai State University Irina M. Vilgonenko North Caucasus Federal University in Pyatigorsk Yurij N. Shapovalov Pyatigorsk State University

Meta-Scientific Study of Artificial Intelligence, pages 383–389 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

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ABSTRACT The chapter provides an overview of existing methodologies, as well as research areas related to the implementation of the potential of artificial intelligence (AI) as a tool for detecting the fixation and further use of information, which may later have evidentiary value in criminal law enforcement. The use of AI as an auxiliary technology in police activities, in the fight against crime, as well as cybercrime, is of great importance and significantly accelerates the process of obtaining relevant information. The aim of the study was to assess the existing methods at the present stage of development of AI, the prospects of development and use in the criminal justice system, to analyze the current state of information technologies for the detection of criminally significant information in the Russian law enforcement system, to explore the potential of AI in the collection of evidence for criminal justice, and to assess the prospects for the development of AI in the field of forensic profiling. Prospective implementation of emotion recognition technologies, assessment of the reliability of the information provided to identify potential threats, will simplify and facilitate the work of law enforcement agencies. The use of innovative technologies of AI in the field of law enforcement makes it necessary to increase the level of professional literacy, of both employees and the population. In the future, the development of AI in the field of law enforcement creates new approaches to information security, and therefore, there will be a need to adapt the legal framework.

The father of artificial intelligence (AI) is John McCarthy, who introduced this term into our everyday life in 1956. The idea of AI, as a branch of computer science, is aimed at the implementation of a super-complex problem that cannot be solved today, which is not impossible through direct calculations or mathematical methods. The idea is to create such a technology which will create a scanning computer based algorithm of any problems from the point of view of human assessment, which typically require human intelligence (Egorova, 2016). Artificial intelligence is already applicable to differential approaches and integral calculus, electric circuit theory, logic mathematics and play-based learning. It is a key technology in the banking system, it is used to identify credit card fraud attempts. Artificial intelligence is used in scanning telephone conversations for understanding and recognizing keywords, is effectively used in the field of healthcare, is used in the field of stock trading, analyzing the prospects of their growth and decline, which leads to profits in transactions, solves the problems of routing, and loss of data banks. From the point of view of Suchanek, the use of AI is a competitive advantage, on the basis of which the only right decision is made. Primarily accurate information affects the timeliness of making the right decision, and the information should be provided at the right time, and this in turn is a prerequisite for making a quality decision. In the field of the work of the law enforcement

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system, the timeliness of accurate information received is a value in itself. Artificial intelligence in the system of law enforcement acts as a search engine that solves both diagnostic forensic tasks and identification issues. It also provides predictive analytics, which is effectively used to predict and prevent crime. The role of AI software in providing assistance to police units, in the context of timely receipt of information and on its basis to develop a strategy of investigation, crime prevention, and so on in our time is huge. METHODOLOGY At the end of 2016, Russian President Vladimir Putin addressed the Federal Assembly with a statement on the need for our own advanced developments and scientific solutions aimed at the development of the economy and social sectors. The main emphasis should be placed on the so-called “end-to-end” technologies—digital technologies, with a powerful technological potential, which today determine the situation of all spheres of life (Putin, 2016). In continuation of the statement on the creation of “end-to-end” technologies in Russia on May 9, 2017, Russian President Vladimir Putin signed decree No. 203 “On the strategy for the development of the information society in the Russian Federation for 2017–2030” (The Decree of the President of the Russian Federation, 2017). In pursuance of the decree, the task was set for state authorities of the Russian Federation, companies with state participation, local governments to ensure the use of Russian information and communication technologies. On July 28, 2017, the government program “Digital Economy in the Russian Federation” was approved in Russia (The Decree of the RF Government No. 1632-p, 2017). The purpose of this program is to improve the competitiveness of the country, the quality of citizens’ life, economic growth, and national sovereignty. Patterns of development of the information society in the digital economy, of course, entails such development in other areas of social life. Therefore, progressive development of institutions of state power against the background of socioeconomic reforms, including law enforcement agencies: police, investigative agencies, courts and correctional institutions, is absolutely natural. These organizations work in a coherent relationship and form a complex legal system of criminal justice. RESULTS To solve these problems forensic profiling as a kind of profiling, which is a set of psychological methods for assessing and predicting human behavior

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based on the analysis of their most informative features, characteristics of appearance, verbal and nonverbal behavior, plays an important role. At the same time, the essence of forensic profiling, which originated at the intersection of psychology, sociology, criminology, and medicine, is a set of joint actions of specialists involved in the investigation of crimes aimed at identifying the personal characteristics of criminals and their victims, the relationship between them, their motivation and actions during the commission of the crimes (Anfinogenov, 1997). Currently, an increasingly important place in the implementation of forensic profiling is taken by AI. For example, the application of mathematics in modeling and simulation, helps to predict and plan crime investigations more effectively. From the point of view of influence on the information and its analysis in social networks three different software approaches to modeling and simulation of criminal justice systems are used, these are: modeling of processes, modeling of discrete events, and dynamics of the system. The main limitations that affect the creation of software models are formulated by Zuev and Fedyanin (Zuev & Fedyanin, 2012). They are as follows: complexity of calculations; numerous external factors affecting the agents; obtaining initially reliable information for modeling; a long period of time can make changes in the relationship between agents. As R. Solso correctly noted in his fundamental work, devoted to AI as well, that science fiction has a habit of becoming a scientific fact (Solso, 2006). He explains that in modern laboratories engaged in the study of AI, the possibility of creating a similarity of HAL (heuristically programmed algorithmic computer), an on-board computer from the work of Arthur Clark’s “2001: A Space Odyssey,” which has intelligence and is able to make ethical decisions based on the simulation of cognitive functions of a human, including perception, memory, thinking, language processing and many other functions that are performed more or less accurately, is being seriously discussed. The search and information systems currently used in law enforcement are used to preserve, store, and process forensic information. These are the systems such as the automated fingerprint information system (AFIS) “Papillon.” This search engine is designed to automate the processes of registration, processing, comparison, and identification of fingerprint information. It includes fingerprint creation (multibiometric) databases. The volume of such banks and the purpose of the accumulated information may vary depending on the task. The scalable architecture of AFIS “Papillon” allows you to create different bases, from a small local base to the giant complexes of the national level. Such databases contain information about fingerprint cards and fingerprints taken from unsolved crime scenes. Another system that allows you to get, evaluate, and analyze relevant information is a system of identification of firearms automated ballistic

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information system (ABIS) “Arsenal.” This system allows you to identify traces of weapons left on the seized bullets and shells after the shot, to determine the number of firearms present at the place of crime, the purpose of specific shots for firearms and assess the probabilities of class and caliber. All this can help law enforcement in investigations. A huge saving of time and money is due to the use of the Federal database of genomic information “Xenon.” Artificial intelligence can be used by law enforcement agencies to process evidence in relation to forensic DNA testing. This type of research has had a major impact on the law enforcement system over the past few years. At the time of the crime, biological materials are one of the most frequently removed evidences. They are blood, saliva, sperm, and skin cells. The technology of DNA research can increase sensitivity of the analysis; this in turn allows forensic experts to identify low-level, damaged, or previously unviable DNA. So now it is possible to obtain valid information in those cases where it was impossible to obtain before, providing new possibilities for the investigation. DNA analysis method is the most effective way to identify, detect, and investigate crimes at the present stage of development of forensic science, genetics, and forensic biological (genetic) examination. The list of the persons who are subject to mandatory genomic registration, convicted, suspected, and accused of committing crimes, is fixed by law. In addition to procedural expediency, the use, development, and improvement of the Federal database of genomic information is economically profitable. The program “safe city,” is developed and successfully used in the framework of the application of AI. It is a set of software and hardware and organizational measures to ensure video protection and technical security. The program is suitable for the management of housing and communal services and other distributed objects in a single information space. Features provided by the program “safe city” are the monitoring and control of the situation on the streets in real time, the presence of audio and video archives, automatic notification of emergencies, the ability to restore the course of events, the integration of available video information. The main tasks to be solved with the introduction of the program include safety on the streets and roads, prompt resolution of disputes under traffic rules, monitoring and control of the situation associated with criminal acts and terrorist threats, the rapid removal of criminally significant information, the establishment of persons involved in criminal events. Other studies that, with the prospect of the use of AI, may have an impact on the law enforcement system are conducted using the EYE_DETECTOR program developed by the Investigative Committee of the Russian Federation. The aim of the research is to find new methods of diagnosis, alternative to polygraph in the field of professional personnel selection. One of such methods is video-oculography (eye tracking). Unlike the polygraph,

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this technology has a number of advantages. So, eye tracking is a less stressful procedure, since oculomotor activity is noncontact, without fixing the sensors on the body of the subject, making the assessment procedure more comfortable for the subject (Zhbankova & Gusev, 2018). All of these algorithms used by AI are aimed at identifying criminally significant information, assistance in the investigation, and crime prevention. But there is another interesting direction and in our opinion one of the most promising areas in the use of AI technologies in the law enforcement environment which is forensic profiling. Profiling is a concept that includes a set of psychological methods and techniques for assessing personality and predicting human behavior on the basis of specially developed methods of analysis of nonverbal and verbal human behavior, the evaluation and comparison of the most informative features, characteristics of appearance, linguistic information, and emotional state. Further on the basis of this analysis it is possible to draw a conclusion about verification of the received information. CONCLUSIONS Every day the potential of new applications of AI in the field of criminal justice increases. The possibilities of using AI to assist the law enforcement system are inexhaustible. The main purpose of its application is to increase public safety, reduce crime, and increase the percentage of crime detection. The possibilities of video analytics for integrated facial recognition, recovery of the crime events, identification of people involved in the criminal event, tracking their actions on several cameras, as well as the detection of objects related to the criminal act; all this will help to prevent the crime by analyzing the movement and patterns. There is a high probability of recognizing the crime in the process of its commission, its prevention and immediate assistance to investigators in identifying the persons involved. With the latest technology such as cameras, video and social networks, huge amounts of data are generated. Using this information, AI can identify a crime that could go unnoticed. Artificial intelligence can help provide greater public safety by tracking latent criminal or potentially dangerous activities. As a result, it will increase the confidence of our society in the law enforcement system. Artificial intelligence is also able to provide assistance to forensic laboratories that produce expertise in respect of fingerprint, ballistic, and biological trace information carrying out an integrated approach to forensic research. Algorithms for evaluating AI information obtained from the crime scene can help prevent victims and potential offenders from being prosecuted. It opens to employees of the law enforcement system new opportunities in

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the field of criminal justice for implementation of public safety ways that have not previously been available. Using AI and analysis of law enforcement, integrated into computer technology and video surveillance in the field of public security, law enforcement agencies will be able to respond immediately to criminal incidents. Law enforcement officials will be able to prevent threats of any nature, intervene and redirect resources if necessary, as well as to improve detection to analyze criminal activities and implement measures to prevent them. REFERENCES Anfinogenov, A. I. (1997). Psychological portrait of the criminal, its development in the investigation of crimes: Autoabstract, thesis for candidate degree. Moscow, Russia: Moscow State University. Egorova, G. (2016). Bank of the future artificial intelligence. Retrieved from http:// fintech-ru.com/ Putin, V. (2016, December 1). Message from the president to the Federal Assembly. Retrieved from http://kremlin.ru/events/president/news/53379 Solso, R. (2006). Cognitive psychology. St. Petersburg, Russia: Piter. The Decree of the President of the Russian Federation No. 203. (2017, May 9). On the strategy of information society development in Russian Federation to 2017–2030. Retrieved from https://www.garant.ru/products/ipo/prime/ doc/71570570/ The Decree of the RF Government No. 1632-p. (2017, July 28). On approval of the program “Digital Economy of the Russian Federation.” Retrieved from http://www.consultant.ru/document/cons_doc_LAW_221756/ Zhbankova, O. V., & Gusev, V. B. (2018). The use of eye-tracking in the practice of professional selection. Journal of Experimental Psychology, 11(1), 156–165. Zuev, A. S., & Fedyanin, D. N. (2012). Models of agents’ opinions management in social networks. Retrieved from http://fintech-ru.com/

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CHAPTER 44

CIVIL LAW ASPECTS OF THE PHENOMENON OF ARTIFICIAL INTELLIGENCE Polina N. Durneva Pyatigorsk State University Irina V. Pogodina Vladimir State University Elvira T. Mayboroda Administration North-West Institute of Management Olga A. Perepadya Pyatigorsk State University Galina V. Stankevich Pyatigorsk State University

Meta-Scientific Study of Artificial Intelligence, pages 391–399 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

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ABSTRACT The chapter is devoted to the civil research of artificial intelligence (AI) in the system of civil relations. Today, when AI plays chess and Go, drives a car, writes music, the question of creating programs for the implementation of legal advice and for the independent resolution of legal disputes is seriously raised. Artificial intelligence technologies go into mass consumption. As a result, there are new facts and circumstances that require legal regulation, such as liability for damage caused in the process of functioning of AI, copyright and related rights to music and other works created by AI, and so on. Since AI technology is based on the principle of neural networks of the human brain, the first reaction from the representatives of legal science was the formation of the idea of “an electronic person.” The chapter provides a legal assessment of AI from the point of view of civil legal identity of individuals and legal entities, which could serve as the basis for the formation of legal identity of AI. The aim of the study is to form an idea of the possibility of AI participation in civil relations and compliance of such participation with the objectives of legal regulation in the field of robotics. The subject of the research dictates the need for an interdisciplinary approach, but the authors have made an attempt to remain as much as possible within the framework of civil science, turning to knowledge from other sciences without in-depth study of interdisciplinary categories.

The development of information technologies has reached such a level that in addition to technical, it raises philosophical, moral, and legal issues that go beyond the usual perception of computer technology and software. Moreover, the number of discoveries in the field of Cybernetics exceeds the ability of the society to comprehend and adapt the system of values and legal systems to the discoveries. One of the most promising areas is artificial intelligence (AI) technologies, including technologies of “stable neural networks and cloud computing infrastructures, fuzzy systems, entropy control, swarm intelligence, evolutionary computing” and others (Ponkin & Redkina, 2018, p. 92). As you can see, computer technologies are associated with concepts that have always been referred to living beings and to the man in the first place. Here in the IT-environment, the question about the necessity and permissibility of the protection of the AI rights occurs (Khel, 2016), solving, in particular, the question of the content of such rights (e.g., the right not to be switched off, protection against reprogramming, etc.). And such questions do not seem strange today, because one of the most modern technologies of the “neural network” has a property that was previously considered the prerogative of the human brain, that is, the ability to make independent (not directly provided by the original algorithm) decisions based on self-learning on the basis of previous experience. The technology got its name because of the structural similarity with the biological neural network, because it also consists of separate computational elements—neurons—grouped into several layers. Each of the neurons

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contains certain information, which depending on the results may subsequently change. The data entering the neural network pass through the whole system of neurons sequentially, where they are processed in order to obtain output data (Rolinson, Arievich, & Ermolina, 2018). The most important issues that legal scientists and legislators in the field of civil law regulation face today are the issue of liability for damage caused by AI, and the authorship of the objects created by the robot that formally meet the characteristics of the intellectual property object. METHODOLOGY The first (and very serious) question that arises in the study of AI, is the definition of AI (robot, robotics). For the purposes of our study, we will use the definition of AI given by Ponkin and Redkina (2018, p. 102): “Artificial intelligence is an artificial complex cybernetic computer system combining software and hardware (electronic, including—virtual, electronic-mechanical, bio-electronic-mechanical, or hybrid) with cognitive-functional architecture and its own or relevant available (attached) computing power of the required capacities and speed, which has: • properties of substantivity (including some subjectivity as an intelligent agent) and general autonomy and elaborative (having a tendency of improvement) operationality; • high-level capabilities to perceive (recognize, analyze, and evaluate) and model the surrounding images and symbols, relationships, processes, and environment (situation), self-referential to make and implement their decisions, to analyze and understand their own behavior and experience, independently to simulate and correct for themselves the algorithms of action, to reproduce (emulate) cognitive functions, including those associated with learning, interaction with the world and independent problem solving; • ability to self-referently adapt their own behavior, to autonomously deep self-learning (to solve problems of a certain class or more widely), to carry out homologation of themselves and their subsystems, including the ability to develop homologated “languages” (protocols and methods) of communication within themselves and with other AI, substantively perform certain anthropomorphicemulating (conventionally attributed to the prerogative of the man [intelligent being]) cognitive (including cognitive-analytical and creative, as well as related to self-awareness) functions, to take into account, accumulate, and reproduce (emulate) the experience (including the human one).”

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The research methodology includes such general scientific methods as dialectical one, comparison, analogy, and special methods of legal science: formal-legal. RESULTS The European Parliament Resolution With Recommendations to the Commission on Civil Law Rules on Robotics (2017) notes that the greater the autonomy of robots, the less they can be considered simple tools in the hands of other participants (such as the manufacturer, operator, owner, user, etc.). Ultimately, robot autonomy raises the question of their nature in the light of existing legal categories, or the need to create a new category with its own specific characteristics and consequences. Here, pointing out the need to deal with the issue of liability for damage caused by robots, the document notes that under the current legal framework, robots cannot be held liable as such for acts or omissions that cause damage to third parties. It is clear from the content of these provisions that the European Parliament admits, as a possibility, the granting of legal identity to AI. Directly in the resolution, the possibility of such a perspective has been treated very carefully, while in the Draft Report from May 31, 2016, proposed options for resolution of the question of the legal “nature of the AI: to consider it as a physical identity or a legal entity, an animal or an object, or to create a new category with its own characteristics, and implications of assigning rights and responsibilities, including responsibility for harm” (pp. 5–6). To be recognized as a party in a legal relationship, a person must have legal identity (Gongalo et al., 2018), which in turn includes legal capacity, legal capacity of activity, and passive dispositive capacity. In civil law there are three types of subjects: individuals, legal entities, and public legal entities, and their legal identity is different. Thus, individuals have equal civil legal capacity from birth, legal capacity of legal entities and public legal entities is of target nature. Legal capacity is traditionally seen as an opportunity to acquire and exercise subjective rights. As Chegovadze (2004) points out, the most significant value here is defined by “the individual needs of the subject that determine the main components of the sociopsychological regulation of his behavior: interest, goals, motives, will, social and legal attitudes” (p. 115). One of the main principles of civil law is the principle of a person’s will and in his own interest (paragraph 2, article 1 of the civil code). However, the document does not disclose either the concept of “will” or the concept of “interest.” Is it possible to recognize the volitional component as a thought and conscious process in the functioning of AI. At the beginning of the chapter we found out that one of its main legally significant characteristics is

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autonomy, the capability to make independent decisions and, based on the result of such decisions, to self-study and in similar situations to apply a different algorithm of actions. However, the solution of this issue goes beyond the field of civil law and requires a serious philosophical study of thinking, awareness (Arkhipov & Naumov, 2017b). Ponkin and Redkina (2018) also point to the difficulty of determining the level of awareness of AI due to the lack of a certain understanding of the phenomenon of consciousness. But does the will matter as an internal mental process, if we are not able to reliably assess the course and result of this process, but face in reality with its external expression—manifestation of will. Some civil scientists point to the lack of legal meaning of the will as a mental phenomenon, as the latter becomes part of the legal reality through its external expression (Gambarov, 2003). Such a view causes significant objections. Thus, the inconsistency of actions of a person (the will) to his true will and, respectively, interest, on which it is based (it will be discussed later), is a violation of the principle of civil law, enshrined in paragraph 2, article 1 of the civil code. In addition, the law directly establishes such a discrepancy as a basis for invalidating a transaction made under the influence of a material error (article 178, CC of the RF), as well as fraud, violence, or threat (article 179, CC of the RF). Consequently, will as an internal process is of essential legal importance and is an integral part of legal capacity, that is, the ability to acquire and exercise rights and perform duties through one’s actions. As for the category of “interest” used in article 1 of the civil code, without going into its long study, we can state that in this case we are talking about a legitimate interest as a psychological phenomenon that does not contradict the law and the principles of law, and owned by a subject (Ryzhenkov, 2014). Legal interest from the point of view of the theory of civil law is the purpose and prerequisite of subjective law (Bratus, 1950); a necessary structuring element of the civil legal relationship, depending on which “civil rights and obligations arise and are organized” (Yurchenko, 2005) and unlike other psychological phenomena (e.g., desires) contains a target element. Does AI have an independent interest? Naturally no. Artificial intelligence is in fact, a computer program, connected or placed in a computer or other hardware mechanism, created by a human with a specific purpose and to meet certain human needs. Accordingly, the interests of the person acquiring and/or using AI will be subject to legal protection. One of the subjects of civil law is a legal entity, which, like AI, at first glance, does not have the characteristics we have considered. In this regard, there is an idea that the theory of a legal entity may well be applicable to AI. Such an attempt was made by Arkhipov and Naumov (2017a), who developed the concept of the Draft law on robotics. As priority measures in the field of legal regulation of AI (robotics), the authors propose to supplement subsection 2 “Identities” of the Civil Code of the Russian Federation with Chapter 5.1

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“Robots-agents,” using the theory of fiction of a legal entity. With all due respect to the work done by the scientists, it should be noted that the proposed project provisions are more characteristic to robot agents as objects of civil relations than as its subjects. In particular, the owner of the robot agent is mentioned, and the owner is understood as “a legal entity, a citizen or other robot agent that uses such robot agent on the right of ownership, economic management, operational management, lease or other legal grounds” (Arkhipov & Naumov, 2017b, p. 168). This norm clearly characterizes the robot agent as an object of property rights. The authors claim that the robot agent has the legal nature of the property and they establish responsibility of the robot agents owners for the “actions” of the latter in accordance with article 1079 of the civil code (“Liability for the damage caused by the activities that create increased danger to others”). Such rules clearly indicate the inadmissibility of the inclusion of provisions on robots provided with computer programs that have signs of AI in the subsection on identities. Returning to the theory of a legal entity, we want to draw attention to the statement by Cherepakhin (2001), who indicates that people (individuals) are recognized as a “personal substrate” of a legal entity, and this idea is supported by representatives of all theories of a legal entity. Legal entities act as independent bodies or as representatives of physical parties or as a part of a collegial body. As a result, and in the development of the concept of guilt of a legal entity, despite a number of specific features in comparison with the fault of physical parties, its mandatory component will be the “manifestation of negligence, bad faith, insufficient responsibility, manifested by officials or bodies of a legal entity and caused problems in the activities of a legal entity” (Pashentsev & Garamita, 2010, p. 89). In turn, AI at a certain stage operates without the participation of individuals, performing certain operations on the basis of new, created by him, codes, which may be unknown to neither the developer nor the owner or the user. For a legal entity, such autonomy from the actions of individuals is not typical. Thus, the concept of a legal entity is not acceptable for the formation of legal norms on AI. On the other hand, the developments proposed by Arkhipov and Naumov (2017b) can be used very effectively for the formation of provisions allowing to establish the identity responsible for the damage caused in the process of functioning of AI. The studies allow us to agree that the concept of “a robot as an animal” is more applicable to AI. So, we recognize the existence of will with an animal. In addition, science does not deny psychological activity with animals (“Zoopsychology,” n.d.).

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As a result, the concept of an electronic identity at the present stage of general development of legislation, and civil legislation in particular, cannot be implemented and is rather untenable. To solve the problem of liability for damage caused by AI, existing mechanisms can be used. The first and most obvious is the recognition of activities related to the use of AI as a source of increased danger. Of course, the rules on the source of increased danger will require adaptation. In particular, it will be necessary to consider as the basis of releasing the owner of the source of the increased danger from responsibility not only in case of disposal of the source from its possession as a result of illegal actions of other persons, but also in case of malicious modification of the AI program or in case of the mistakes made by developers or producers which led to damage. Another mechanism aimed at protecting the property interests of civil turnover is the institute of insurance. The legislative establishment of the obligation of the AI owner to take out an insurance contract in the event of damage resulting from its operation will ensure the protection of the rights of third parties. As for the issue of rights to the works of science, literature and art, and components of computer programs generated by AI, its resolution is seen in the concept of legal interest. Since AI, being, in fact, a program created by man, has a certain purpose—aim, property, and so on, to meet the needs of the person owning or using it, or the user, the rights to such works or components of the program must be recognized for such owner or user. So, the proposed approach solves the issue of property rights to the robot’s works, but does not solve the issue of authorship for these works. The solution to this problem seems more complex than simply choosing a subject, whose authorship will be recognized (or the absence of such a subject, as the concept of “vanishing” (zero) authorship suggests (Morhat, 2018)). First of all, it is necessary to solve the issue of the protectability of the results created by AI systems (Sesitskii, 2018), which involves a separate detailed study, and we will leave it for further research. CONCLUSIONS The concept of the electronic identity at the present stage of development of the legislation in general, and the civil legislation in particular, cannot be realized and is rather untenable. To solve the problem of liability for damage caused by AI, existing mechanisms can be used. The first and most obvious is the recognition of activities related to the use of AI as a source of increased danger. Another mechanism aimed at protecting the property interests of civil turnover is the institute of insurance.

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As for the issue of rights to works of science, literature and art, and components of computer programs generated by AI, its resolution is seen in the concept of legal interest: securing the rights (except authorship) to such results for the person in whose interests AI functions. To clarify the issue on the origin of copyright on such results requires a separate detailed study. REFERENCES Arkhipov, V. V., & Naumov, V. B. (2017a). Artificial intelligence and autonomous devices in the context of law: On the development of Russia’s first law on robotics. Proceedings SPIIRAN Journal, 55, 46–62. Arkhipov, V. V., & Naumov, V. B. (2017b). On some issues of theoretical bases of development of the legislation on robotics: Aspects of will and legal identity. Law, 5, 157–170. Bratus, S. N. (1950). Subjects of civil law. Moscow, Russia: State Publishing House of the Books on Law. Chegovadze, L. A. (2004). Structure and state of civil legal relations. Moscow, Russia: Statute. Cherepakhin, B. B. (2001). Papers on Civil Law. Moscow, Russia: Statute. European Parliament Resolution With Recommendations to the Commission on Civil Law Rules on Robotics (2015/2103(INL)). (2017, January 27). Retrieved from http://www.europarl.europa.eu/doceo/document/A-8-2017-0005_EN.html Gambarov, Y. S. (2003). Civil right: General part. Moscow, Russia: Mirror. Gongalo, B. M., et al. (2018). Civil Law: Textbook. Moscow, Russia: Statute. Khel, I, (2016, September 19). If machines can think, do they deserve civil rights? Retrieved from https://hi-news.ru/robots/esli-mashiny-mogut-dumat-zasluzhivayut-li-oni-grazhdanskix-prav.html Morhat, P. M. (2018). Legal personality of artificial intelligence in the field of intellectual property law: Civil law problems (autoabstract, Doctoral dissertation). Moscow, Russia: Moscow State University. Pashentsev, D. A., & Garamita, V. V. (2010). Guilt in Civil Law. Moscow, Russia: Yurkompani. Ponkin, I. V., & Redkina, A. I. (2018). Artificial intelligence in terms of Law. Vestnik RUDN, Series: Legal Sciences, 22(1), 91–109. Rolinson, P., Arievich, E. A., & Ermolina, D. E. (2018). Objects of intellectual property created with the help of artificial intelligence: Features of the legal regime in Russia and abroad. Law Journal, 5, 63–71. Ryzhenkov, A. I. (2014). The action based on a person’s will and in his own interest as a principle of civil law. Lawyer, 16, 16–21. Sesitskii, E. P. (2018). Problems of legal protection of the results created by artificial intelligence systems (autoabstract, thesis for candidate degree). Moscow, Russia: Academy of Law. The Draft Report With Recommendations to the Commission on Civil Law Rules on Robotics (2015/2103(INL)) (2016, May 31). Retrieved from http://

Civil Law Aspects of the Phenomenon of Artificial Intelligence     399 www.europarl.europa.eu/sides/getDoc.do?pubRef=-//EP//NONSGML +COMPARL+PE-582.443+01+DOC+PDF+V0//EN Yurchenko, O. O. (2005). The right to protection and ways to protect a legal interest in civil law (autoabstract, thesis for candidate degree). Tver, Russia: Tver State University, Tver. Zoopsychology. (n.d.). In Wikipedia online. Retrieved from https://ru.wikipedia.org/ wiki/%D0%97%D0%BE%D0%BE%D0%BF%D1%81%D0%B8%D1%85%D0 %BE%D0%BB%D0%BE%D0%B3%D0%B8%D1%8F

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CHAPTER 45

THE LEGAL NATURE OF ARTIFICIAL INTELLIGENCE THROUGH THE PRISM OF COPYRIGHT Theoretical and Legal Aspect Inessa Sh. Galstyan Moscow Region State University Lyudmila A. Tkhabisimova Pyatigorsk State University Yevgeny E. Nekrasov Pyatigorsk State University Galina V. Stankevich Pyatigorsk State University Irina M. Vilgonenko Pyatigorsk State University

Meta-Scientific Study of Artificial Intelligence, pages 401–407 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

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ABSTRACT The chapter attempts to analyze the challenges facing the legislator and law practitioner in the field of copyright and related rights in connection with the development of artificial intelligence (AI) technologies. The existing approaches to the definition of the results of the work of intelligent machines are considered, the position regarding the possibility of endowing AI with legal personality is argued. Using separate methods of research, in particular, the dialectical and comparative legal method, the circumstances that led to the need to expand the provisions of the law on copyright and related rights with respect to the distribution of the results of “creative activity” of intelligent machines (robots) and intelligent computer programs are highlighted. The scientific novelty of the work consists in an attempt to consider the problems that objectively arise in the legal regulation of copyright and related rights in the development of AI technologies.

Scientific and technological progress in the modern world, along with obviously positive changes of human life, creates serious challenges for law, in particular for intellectual property rights. One of such innovations is the development of artificial intelligence (AI) technologies. At the moment, there is no certainty in the question of legal personality of AI—whether it acts as an independent subject of legal responsibility, whether “legal autonomy” of AI is possible, how to evaluate the results of AI, which is especially important in terms of copyright and related law (Arkhipov & Naumov, 2017). The dilemma is the copyright issue with the regard of literary and artistic works created with the use of AI technologies, as well as the eligibility for a part of the program code created by a computer program based on AI. Characteristics of the legal basis of AI are inextricably linked with the consideration of the concept of AI and involves a clear definition of the legal consequences of the activities of AI, its legal personality. METHODOLOGY The methodological basis of the present study is a wide range of modern research methods, both general scientific and private scientific ones, because with the help of one approach it is difficult to achieve the goal in the study of such a multifaceted problem as the legal nature of AI through the prism of copyright. The use of proper legal methods, including formal legal, comparative legal, and interpretive ones, allowed to carry out legal regulation of the use of AI, to analyze the problem of determining copyright and related rights to the results of AI activity.

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RESULTS Artificial intelligence is the capacity of intellectual systems to perform certain creative tasks and functions that previously were to the activity of man who is a “reasonable being,” that is, such properties of an artificially created system that allow to “simulate” the activity of the brain, to carry out “intelligent” activities. On the other hand, AI is a scientifically developed and justified technology for creating intelligent machines (robots), in particular intelligent computer programs. Thus, AI should be understood as intelligent machines (robots) or intelligent computer programs capable of performing certain creative tasks and functions and carrying out “intelligent” activities. The term was first used in 1956 by American computer scientist John McCarthy. However, it appeared in the 1940s, when a technology was created that opened potentials which at that time were considered comparable to the capabilities of the human brain. Initially, AI was understood as “the ability of machines to simulate human behavior, perceive information and make decisions in accordance with them” (“Intelligence and Law,” 2018, n.p.). Imitation of “reasonable” activity by AI requires the construction of artificial networks by analogy with the functioning of brain cells of the human body, the main embodiment of which are “artificial neural networks,” that is, a mathematical model implemented in software or hardware, which has the property of self-learning (Shustikov, 2017). Concerning the copyright rights, the use of AI technologies requires a detailed study of two provisions: • copyright and related rights to “program code” based on “accumulated experience,” on the results of artificial neural networks of self-learning program (machine, robot) and • copyright and related rights to works, that is, products of activity, “labor” created by intelligent computer programs or intelligent machines (robots). On the one hand, the legislator legally brought some clarity to the solution of these issues, pointing to the following points: • the object of copyright are scientific, literary, or artistic works, as well as computer programs which are creative in nature and expressed in an objective form (paragraph 1 of article 1259 of the Civil Code of the Russian Federation [Part IV. Federal Law of 18.12.2006 No.230-FZ]) and

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• an author is deemed to be a person through whose creative efforts a work is created (article 1257 of the Civil Code of the Russian Federation [Part IV. Federal Law of 18.12.2006 No.230-FZ]). The analysis of these provisions of the Civil Code of the Russian Federation allows to include only a person in the number of potential authors of the work. At the same time, the result of the activity of AI (intellectual machine or intellectual computer program) has all the features of the object of copyright, since it is the product of creative work, and, as we pointed out earlier, the principle of AI is “imitation of intelligent activity.” In Western legal thought, it has been proposed to introduce a new category of “technical person” into the legal system, which will allow endowing AI carriers with a certain legal personality (Gurko, 2017). This proposal, initially aimed at solving the legal issues regarding responsibility for damage caused by carriers of AI, can be successfully applied to resolve problems in the field of copyright, such as the question of ownership of the creative result of the activities of AI. At the same time, if the civil liability of AI carriers is the subject of discussion, then regarding to the criminal liability, the lawmaker clearly establishes the principle of guilt (or, as it is often called in the criminal law doctrine, the principle of personal guilt), according to which “a person shall be held accountable only for those socially dangerous acts and their socially dangerous consequences in respect of which his or her guilt has been established” (article 5 of the Criminal Code of the Russian Federation, 1996), which is, first of all, the mental attitude of a person to the committed criminal act and the criminal consequences that have occurred. As currently envisaged, at the very beginning of AI development, even taking into account the creation of mathematical models of artificial neural networks, we can only raise the question of the development of a certain algorithm of actions, based on past situations (“gained experience”) without an emotional context (mental attitude) both to the actual act (action or omission), and to its results. This problem is represented by the following example, which clearly reflects all the threats hidden in the proposed approach. Artificial intelligence is most actively implemented in the service sector, in particular, in the companies providing passengers and cargo services. It is important to note that before the above changes this area of services required the use of modern equipment. This is a subject of interest for both auto giants and, for example, representatives of large businesses in the tourist cluster (in the periodical press there are systematic reports of “unmanned” taxis in the UAE, experimental batches of unmanned Ford cars, etc.). The question arises as to who should be held legally responsible in the event of an accident of a vehicle under the control of AI—the manufacturer of AI, the owner of such a robot (machine, program), the actual carrier of AI, or another person? In this regard, it should be taken

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into account that the decision on algorithm of activity in an emergency situation will be made by the carrier of AI on the basis of the “dry” mathematical algorithms of the program code created by the producer (developer). Although it could help to resolve some copyright issues, for example, in relation to works and programs processed by AI carriers, their recognition as the authors or possessors of copyrights would raise new questions: First. How will copyright be exercised in relation to the results of intellectual activity belonging to the AI carrier? Second. Initially, any “creative activity” (the activity of creating objects of copyright) is based on someone’s interest in obtaining such a result, aimed at meeting the needs of a particular person or persons. Since the needs of the AI carriers can only be technical in nature, that is, be aimed at maintaining such a state of the intellectual machine that allows it to perform the tasks and functions assigned to it. In February 2017, the European Parliament adopted the resolution “Civil Law Rules on Robotics.” The document, consisting of more than a hundred points, is devoted to various aspects and problems of robotics and AI. In particular, it is proposed to introduce a pan-European system of registration of smart machines. According to the parliamentarians, individual categories of robots should be assigned an individual registration number, which will be recorded in a specialized registry. Detailed information about the robot, including data about the manufacturer, owner, and compensation procedures and conditions in case of damage is available to all interested persons. The system should be maintained and monitored by a specialized agency for robotics and AI, which could regulate other aspects in this area as well. It would be useful to consider the problem from other points of view. Let us suppose that an intelligent machine (robot) or program is recognized as a thing, the property of a person or a legal entity (or other subject of law). Article 136 of the Civil Code of the Russian Federation contains a provision according to which “fruits, products, revenues obtained as a result of the use of the thing, regardless of who uses such thing, belong to the owner of the thing” (Civil Code of the Russian Federation, Part I. Federal Law of 30.11.1994 No.51-FZ). Thus in point 1 of article 1227 of the Civil Code of the Russian Federation it is established that “intellectual rights do not depend upon the right of ownership to the physical carrier (or thing) in which the respective result of intellectual activity (or means of individualization) is expressed” (Civil Code of the Russian Federation. Part IV. Federal Law of 18.12.2006 No.230-FZ). Moreover, point 3 of this chapter clarifies that the provisions of property law do not apply to intellectual property rights, which is confirmed in judicial practice. At the same time, we believe that the application of article 136 of the Civil Code of the Russian Federation to the results of the activities of AI

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carriers is reasonable and justified, but requires changes and additions to the civil legislation regarding: • specifics of the process of creating “fruits and other income” by AI carriers; • clarification of the position of the legislator on the issue of ownership of such “fruits and other income,” that is, whether the user of an intellectual machine (robot) or a computer program or the right holder of software (developer of AI) will be recognized as owner; and • determining which intellectual property rights are subject to protection in this case and the duration of their validity. In August 2017, the journal Popular Mechanics published an article “New Laws of Robotics” (Neznamov, 2017), in which various points of view were given on the problem of determining the place of smart machines in the modern world, analyzing a wide range of sources from the works of science fiction Isaac Asimov (1942) as well as a variant presented by the Engineering and Physical Sciences Research Council (2011) to the research by leading modern manufacturers of robots and intelligent software equipment such as Satya Nadella and Mark Tilden (2016). CONCLUSIONS The problem of legal regulation of AI technologies, of course, needs urgent attention. At present, no solution has yet been found to this critical problem, either at the international, regional, or national level. In domestic legislation, in our opinion, it is possible by introducing in Chapter 71 of the Civil Code of the Russian Federation paragraph 6.1 “Works of science, literature and art, as well as computer programs produced by AI carriers without human participation.” We believe that this paragraph should contain an indication of the cases in which the work should be considered to be created without human participation, and reflect who will own the rights to these works. REFERENCES Arkhipov, V. V., & Naumov, V. B. (2017). Artificial intelligence and autonomous devices in the context of law: On the development of the first Russian law on robotics. SPIIRAS Proceedings, 6(55). https://doi.org/10.15622/sp.55.2 European Parliament Resolution of February 16, 2017. Research centre for regulation of robotics and artificial intelligence: Civil law rules on robotics. Retrieved from http://robopravo.ru/riezoliutsiia_ies

The Legal Nature of Artificial Intelligence Through the Prism of Copyright     407 Gurko, A. (2017). Artificial intelligence and copyright: Future outlook. Intellectual. Copyright and Related Rights, 12, 7–18. Intelligence and Law. (2018, October 10). Retrieved from http://strategyjournal .ru/articles/intellekt-i-zakon Neznamov, A. (2017, September 4). New laws of robotics: How robots’ rights are regulated in Europe. Retrieved from https://www.popmech.ru/technologies/379112 -novye-zakony-robototehniki-kak-v-evrope-reguliruyut-prava-robotov Shustikov, V. (2017, April 24). Artificial intelligence for all. Retrieved from https:// sk.ru/news/b/press/archive/2017/04/24/iskusstvennyy-intellekt-dlya-vseh .aspx The Civil Code of the Russian Federation. Part I. Federal Law of November 30, 1994 No. 51-FZ (as amended on May 23, 2018). Law reference system “ConsultantPlus.” Retrieved from http://www.consultant.ru/document/cons_doc _LAW_5142/e76cbd72b7917a719160f39231998418094c73b4/ The Civil Code of the Russian Federation. Part IV. Federal Law of December 18, 2006 No. 230-FZ (as amended on May 23, 2018). Law reference system “ConsultantPlus.” Retrieved from http://www.consultant.ru/document/ cons_doc_LAW_64629/d13f1a4bd6837b3b5ad4f00430ae6dd5e6af6753/ The Criminal Code of the Russian Federation of June 13, 1996 No. 63-FZ (as amended on April 23, 2018). Law reference system “ConsultantPlus.” Retrieved from http://www.consultant.ru/document/cons_doc_LAW_5142/

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CHAPTER 46

ARTIFICIAL INTELLIGENCE IN POLITICS Global Leadership and the Risks of Competitive Struggle Maria A. Adamova Pyatigorsk State University Mariana L. Kardanova North-Caucasus Federal University Alexandra V. Yakusheva Social and Educational Center for Children and Youth Maria A. Dyakonova State University of Management Aza V. Mankieva North Caucasian Institute of the Branch of the Russian Presidential Academy of National Economy and Public Administration

Meta-Scientific Study of Artificial Intelligence, pages 409–417 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

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ABSTRACT The chapter investigates the use of artificial intelligence (AI) technologies in modern political processes, taking into account the increased attention paid by economically developed and developing countries to the advances in the field of AI in recent years. The assessment of general and specific risks associated with AI for global governance, civilization, and the formation of a new political order is given. The main leaders who have already joined the “AI race” at the official level—the United States, China, and the European Union, are identified and characterized, their share in the common market of global AI technologies is determined, and the priorities of their development are clarified. Analysis of the current state of AI has made it possible to formulate forecasts for the further development of the “arms race with AI,” taking into account the current level of technology leaders, the expected competition from other technological powers (Canada, Japan, India, Singapore, UAE, Finland, United Kingdom, South Korea, France, and Russia) and the general protectionist trends in the global technology market, including restrictions on the supply of AI technologies.

In 2017, China, Canada, Japan, Singapore, the UAE, and Finland published their national strategies to promote the use and development of artificial intelligence (AI). In early 2018, they were joined by the United Kingdom, Denmark, France, the European Union, South Korea, and India. Under these conditions, an increasing number of states are entering the so-called “AI arms race.” Following their own political and technological interests, they block data flows and protect national AI companies by political, legal, and economic means. At the expert level, the prospects of nationalism in the field of AI are increasingly discussed and various scenarios are considered. In the first case, we are talking about the termination of the questions about nationalism in the field of AI in view of the future definition of winners and losers. In the second scenario, a new bipolar system of the world order with AI leadership of the United States and China appears, between cooperation with which other states will choose (Siegele, 2018). Against the background of state-centric forecasts, the discussion on the future of AI technologies for humanity as a whole, the identification of the contours of a new global order, taking into account the changed political, economic, and social relations, does not lose relevance. The AI race is regularly compared to the cold war arms race, however, the possession of AI technologies with combat potential is now accepted as a “compensating strategy” instead of nuclear weapons (Payne, 2018). The fundamental difference from the previous era is that nuclear weapons were used by decision makers and were subject to human psychology, while AI systems make decisions based on mathematical algorithms, and that neutralizes the

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influence of the factors of cultural and ethical order and increases the degree of unpredictability of global politics as a whole. Amid these circumstances, this chapter will consider the use of AI technologies in global politics and in the formation of a new world order, the main risks of AI in the long and medium term, as well as the current state of international competition in the field of AI. METHODOLOGY In the framework of this study, the methods of system analysis were actively used. Content analysis of documents, reports, and articles devoted to opportunities and risks of application of AI technologies in political processes of different level and scale, as well as in global management as a whole, are chosen as the main method of research. In the preparation of the chapter we analyzed scientific papers and reports on the political and diplomatic use of AI technologies in the development of foreign policy strategies by some states, and also with their technical support for the implementation of foreign policy activities in the changing world. In addition, in the process of our work on the chapter in its second part, comparative analysis was also used, three leaders in the field of AI development were identified, that is, the United States, China, and the European Union; reasonable forecasts were made about the further development of competition between them. RESULTS Within the framework of the modern scientific discussion devoted to the assessment of the possibilities of AI technologies application in political and global management, two topics related to common risks dominate: 1. fear of the singularity, the development of AI to a level where human intelligence will cease to comprehend it, and therefore lose control over it with catastrophic consequences for the biological species and 2. fear of the final industrial revolution, as a result of which machines will replace people in almost all spheres of activity. There is a third risk usually referred to in works on political and social sciences: allowing governments to know, understand, and control their citizens more than ever before, AI technology is able to offer authoritarian countries the most likely alternative to liberal democracy that will lead to

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the resumption of international competition between social systems. In this case, it is the struggle between liberal democracy and digital authoritarianism that will determine much of the 21st century in the sphere of political relations (Wright, 2018). However, there is a danger that with further improvements in technology the use of powerful algorithms to manipulate solutions will begin to undermine the basis of the “collective mind,” which is flexible to adapt to the challenges of a complex world. For collective intelligence to work effectively, it is necessary that information retrieval and decision-making are carried out by different individuals, and not only strictly within the framework of algorithms, since in this case the status of people will fall to simple command receivers. This threatens to result in the formation of a centralized system of technocratic behavioral and social control using a super intelligent information system, that is, the emergence of a new form of dictatorship (Helbing et al., 2017). In addition to the general threat of AI dictatorship and the increased competition between social systems, there is also a threat of an imbalance in the system of checks and balances created over the previous centuries at the world political level. As it is rightly noted by Wagner and Furst (2018), institutions and treaties coordinating foreign policy activities in the 20th century, arms control and nonproliferation were created without taking into account the capabilities of AI (Wagner & Furst, 2018). It should be noted that these problems are associated, to a greater extent, with the long-term prospects of using AI. With regard to the current state of affairs and prospects for the next 10–15 years, the report of the team of scientists from Chatham House, published in 2018, should be noted. In this paper, the authors assess the role of AI in policy development and in international affairs (Parakilas, 2018). Such roles are three of them: • analytical role, • predictive role, and • operational role. Along with general research in the field of predicting the use of AI in politics, there is a considerable body of scientific work on the topic of increasing competition in the field of AI. The world leader is the United States, about 40% of the world’s research in the field of AI is concentrated in the United States, considering AI as a transformative technology that promises social and economic benefits, improving educational opportunities and quality of life, as well as strengthening national security. For decades, the U.S. government has been funding developments in this area through the national strategic plan for research and development in the field of AI, which includes seven priorities. These include: ensuring long-term investment in AI to maintain

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U.S. leadership in this area; developing effective methods of human–AI interaction; addressing ethical, legal, and social issues; ensuring the security of AI systems; developing publicly available data sets and environments for training and testing; introducing of new standards and models; and timely training of specialists (The National AI R&D Strategic Plan, 2016). The main competitor of the United States in the field of AI development is China. In July 2018, China announced the constant use of machines based on AI technologies in the work of the Ministry of Foreign Affairs (Chen, 2018). As reported, the Chinese AI “Policymaker” is a system of strategic decision support, which is constantly studying the strategy and tactics in international politics. The system relies on a large amount of data, from gossips from diplomatic official’s conversations to images taken by spy satellites. At a time when politicians need to make quick, accurate, and balanced decisions to achieve a specific goal in a difficult, sometimes emergency situation, the AI system is able to present the Chinese diplomat with various options to manage the situation and in the shortest possible time to offer the best option. AI “Policymaker” is not the only achievement of China in this area. In this country, interest in innovation in the field of AI is maintained at the highest level, as regularly stated by China’s President Xi Jinping during his speeches at the party congresses, positioning China as an “AI superpower.” This is due to the conviction of the Chinese leadership that it was the technological backwardness of their country that once led to its weakness and vulnerability to foreign powers. Under Xi Jinping, China intends to bring innovation to the forefront by 2030 and to become a global superpower in innovation by the middle of the 21st century (Kania, 2018). The modern way of China to challenge the U.S. leadership in the field of AI is characterized by the following: 1. Chinese Government sees AI as a unique opportunity to transform China’s economy and to ensure its continuous growth; 2. the slowdown in the economic growth may call into question the legitimacy of the Communist Party of China, and therefore the AI support project is not only economic, but also the most important state political project necessary for the elite to maintain its power; 3. China considers the use of AI as one of the leading and most promising applications of AI to enhance social control and stability; and 4. despite the considerable success in AI, there is still consensus in Chinese political circles that China is still seeking to catch up with the United States and is trying to implement a “sharp turn in order to surpass” strategy.

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To achieve its objectives, China has adopted a market-oriented approach: the Central Government and local authorities are focusing on creating open platforms for mass innovation and entrepreneurship (Kania, 2018). The growing competition between the United States and China in the field of AI naturally actualizes the security dilemma, but at a new, more high-tech level. In the situation when two of the world’s leading technological powers, China and the United States, do not have accurate data on the military capabilities and real intentions of each other, they can nevertheless make some efforts to reduce uncertainty: for example, to conduct open exercises, to demonstrate the use of AI technologies in the military sphere. Speaking about the prospects of development of security dilemma in the context of relations between the United States and China in the field of AI, the researcher Meserole identifies three possible ways: 1. Full cooperation. The United States and China could engage in joint development in the field of commercial AI through fully open bidding and the establishment of specialized bilateral organizations to control the military use of AI. 2. Full competition. The United States and China can take strict measures to control the export of AI technology, hardware and software. Such a step would reduce their dependence on technology transfer, but a complete break in the global supply chain would entail enormous economic costs. 3. Partial cooperation and competition. The United States and China may introduce export controls on certain hardware and software of their choice and at the same time establish bilateral channels to increase the exchange of technology information. This option will minimize the cost of breaking global chains, as well as limit the potential dependence on technology exchange (Meserole, 2018). Each of the above options has pros and cons, while full cooperation cannot be objectively considered as a starting option because of the already existing high level of distrust between the United States and China to each other. On the other hand, full competition is also impossible, since no country can afford the cost of a fully isolated technological chain. Under these conditions, partial cooperation and competition between them can be expected to continue, with periodic complications and problem resolutions, that is, the option of a limited AI arms race, which looks more preferable than the total one with the risk of escalating into a real armed conflict. In the face of increasing competition between the United States and China in the field of AI, many other countries and unions are also trying to take advantage of AI for their economies and to increase their authority on the international arena. Among them is the European Union, which, on

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the one hand, fairly objectively and accurately assesses the risks and benefits of AI in global governance, on the other, it has a high level of scientific and technological base and human resources to offer its highly competitive developments. According to the European Commission Report in 2018 the position of the European Union in global competition is as follows: • the share of European business in the field of AI accounts for 19% of all AI companies in the world, which is No.2 after the United States (38%). The European Union is ahead of China (16%), Israel (10%), Japan, and Canada (3%); • of the five leading AI hubs, there is only one in Europe—the London hub. It is minor in number to Silicon Valley, the New York hub, and Haidian District; • in terms of the number of AI-startups, leaders in Europe are London (97), Berlin (30), Paris (26), Madrid (15), Stockholm (12), Amsterdam (9), Moscow (9), Copenhagen (7), Barcelona (7), and Dublin (6); and • among the most developed European companies in the field of AI are BenevolentAI, Blippar, Deffblue, SwiftKey (London), CARMAT, Snips (France), and Arago (Germany; Westerheide, 2018). The European Union is aware that it is likely to lose to China in the AI race in the medium term, but it can successfully compete for the third place, bearing in mind its potential and intention to remain an important player in the AI market. CONCLUSIONS In general, the scope of AI in global politics is characterized by a low level of development, and is relatively new, although one of the most promising. The world has already seen leaders in this area—the United States, China, and the European Union—while other technological powers are making efforts to keep up with them. The rapid development of AI technologies in recent years has actualized a broad scientific debate not only about the possibilities and prospects of AI, but also about the risks to global governance and the preservation of human values. Highly competitive sphere of AI at the present stage is chaos, the control of which is a matter of concern for both scientists and politicians. In 2018, Erdelyi and Goldsmith (2018) published their proposals for the development of a coherent international legal framework, which is centered on a new intergovernmental organization. It will focus on streamlining and coordinating different country’s efforts to develop a common AI

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policy. However, the modern international community is not yet ready for a comprehensive discussion of the problem of AI control. Under these circumstances, in the medium term, we can expect further aggravation of competition in the field of AI at the international level, primarily between the European Union, China, and the United States. REFERENCES Chen, S. (2018, July 30). Artificial intelligence, immune to fear or favour, is helping to make China’s foreign policy. Retrieved from https://www.scmp.com/ news/china/society/article/2157223/artificial-intelligence-immune-fear-or -favour-helping-make-chinas Erdelyi, O., & Goldsmith, J. (2018). Regulating artificial intelligence: Proposal for a global solution. Retrieved from https://dl.acm.org/doi/pdf/10.1145/3278721.32 78731 Helbing, D., Frey, B. S., Gigerenzer, G., Hafen, E., Hagner, M., Jofstetter, Y., . . . Zwitter, A. (2017, February 25). Will Democracy survive big data and artificial intelligence? Retrieved from https://www.scientificamerican.com/article/will -democracy-survive-big-data-and-artificial-intelligence/ Kania, E. B. (2018, November 6). China’s embrace of AI: Enthusiasm and challenges. Retrieved from https://www.ecfr.eu/article/commentary_chinas_embrace _of_ai_enthusiasm_and_challenges Meserole, C. (2018, November 6). Artificial intelligence and the security dilemma. Retrieved from https://www.brookings.edu/blog/order-from-chaos/2018/11/06/ artificial-intelligence-and-the-security-dilemma/ Parakilas, D. (2018, June 14). Artificial intelligence and international affairs: Disruption anticipated. Retrieved from https://www.chathamhouse.org/publication/ artificial-intelligence-and-international-affairs Payne, K. (2018, September 24). Artificial intelligence: A revolution in strategic affairs? Retrieved from https://www.realcleardefense.com/2018/09/24/artificial_ intelligence_a_revolution_in_strategic_affairs_304403.html Siegele,L.(2018,December17).Germanygetssmartaboutartificialintelligence.Retrievedfrom https://www.handelsblatt.com/today/opinion/robo-go-germany-gets-smart -about-artificial-intelligence/23770626.html?ticket=ST-961507-weS6wHK cmqYWs3AMijx2-ap3 The National Artificial Intelligence R&D Strategic Plan. (2016). Retrieved from https://www.nitrd.gov/PUBS/national_ai_rd_strategic_plan.pdf Wagner, D., & Furst, K. (2018, August 12). AI and the international relations of the future. Retrieved from https://intpolicydigest.org/2018/08/12/ ai-and-the-international-relations-of-the-future/ Westerheide, F. (2018). The European AI landscape. European commission EU AI workshop 2018. Retrieved from http://ec.europa.eu/newsroom/dae/document .cfm?doc_id=50826

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CHAPTER 47

LEGAL ASPECTS OF USING ARTIFICIAL INTELLIGENCE (DIGITAL TECHNOLOGY) IN THE FIELD OF TAXATION Olga Yu. Bakaeva Saratov State Law Academy Eugeniy G. Belikov Saratov State Law Academy Elena V. Pokachalova Saratov State Law Academy Margarita B. Razgildieva Saratov State Law Academy Marina A. Katkova Saratov State Technical University

Meta-Scientific Study of Artificial Intelligence, pages 419–426 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

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ABSTRACT We live in the age of active development of artificial intelligence (AI) technologies and their application in socioeconomic practice, which inevitably highlights the importance of the issues of legal regulation in relation to the development and use of such technologies. In this regard, there is a need for a theoretical understanding of the extent to which such technologies can comply with the existing legal concepts and rules, and to what extent new legal tools will be required. The main vector of formation of new basic ideas that determine the requirements for the use of digital technologies in the field of taxation, we believe, should be the desire to achieve a balance of private and public interests. To achieve this goal in the framework of the analysis of the sphere of taxation, it is necessary to establish the existing relationship of tax and legal regulation with the processes of digitalization and application of AI and to analyze their legal regulation from the standpoint of compliance with the conceptual ideas that have entrenched in the sphere of tax and legal regulation, ensuring the balance of private and public interests. This approach made it possible to formulate recommendations on the development of a number of basic provisions defining the requirements for legal regulation in connection with the use of such technologies: on the inadmissibility of direct or indirect imposition of digital infrastructure services; on the use of digital technologies in the sphere of state (municipal) management in order to provide maximum convenience to users of such technologies; about the conformity of the regulatory legal acts regulating the use of digital technologies.

The modern world sees the main vector of promising development in the application of artificial intelligence (AI) technologies. All creative efforts are focused on understanding what these technologies could bring to the solution of macro- and micro-problems of mankind and whether such decisions would be positive or not in their consequences. We are witnessing large-scale transformations and now it is difficult to determine all the impacts of their evolution, but it is necessary to find the right vector of their development. The purpose formulated in the Order of the Government of the Russian Federation of July 28, 2017 No. 1632-p “About approval of the program ‘Digital Economy’ of the Russian Federation” for scientific search in the field of jurisprudence is the elimination of barriers to digitalization of social and economic relations. In our opinion, this implies the adaptation of the existing legal regulation of this process, as well as the formation of a new legal regulation (Order of the Government of the Russian Federation of July, 28, 2017 No. 1632-P). We fully agree with F. Patrick Hubbard’s (2016) opinion that modern legal doctrines, in general, allow us not to stray too far from the processes of robotization. He believed that it was hardly appropriate to introduce significant changes in legal regulation, which was the result of a sufficiently

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long evolutionary development and represented a carefully achieved balance between competing interests, entrepreneurs, and ordinary people. The search for new conceptual ideas for legal regulation in the sphere of taxation, formed in the context of the use of digital technologies, including AI, as well as continuous monitoring of its compliance with the already established requirements, the principles of tax law, is the purpose of this research. The achievement of this goal in the framework of the analysis of the sphere of taxation can be seen in the following. First, to establish the current relationship of tax and legal regulation with the processes of digitalization and the use of AI. Secondly, to identify situations that do not correspond to the values recognized as basic for tax and legal regulation and to develop recommendations for their elimination. Third, to suggest filling the theoretical and legal gaps identified in the understanding of the tax and legal tools associated with the use of AI technologies and digitalization. The balance of private and public interests is an evaluation category in its essence, which necessitates its interdisciplinary study both from a general legal point of view, and the identification, systematization, and generalization of the specifics of its manifestation in the field of tax relations. METHODOLOGY First of all, it is necessary to determine the terminology involved. The program documents include such terms as robotization, AI, and digitalization. Such terminology is not yet included in the Tax Code of Russia, which uses the terms “services in electronic form,” “electronic document circulation,” and “information technologies.” Currently, approaches to the differentiation of terms denoting different types and levels of digital technologies are in the process of formation both in the field of information technology, and in such related fields as philosophy, sociology, psychology, and law (Neznamov & Naumov, 2018). Artificial intelligence is the object of different conceptions, aspects or contexts of consideration. For example, Lukanov (2017), considering AI from the point of view of the technologies currently used in law, indicates that this is the most complex information technology, which is a hardware and software system capable of solving creative problems from the subject area, knowledge of which is stored in the memory of such a system. Close to this position is the understanding of the concept of robot proposed by Richards and Smart (2016) as a combination of software and mechanisms created by people to perform a certain autonomous work. As noted by Morhat (2017) it is generally thought that AI is associated with the creation and functioning of programmed machines, potentially able to

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do what requires a certain degree of intelligence. As the degree of “intellectual efforts” to solve various problems of different complexity is different, the concepts of “weak” and “strong” AI are distinguished (Castro & New, 2018). But the progress in the field of AI is quicker than originally estimated. Modern search in this field is based on a deep study of neural networks that allow the computer to learn in a way similar to the work of the human brain (Karp, 2016). Despite the fact that the brain and the computer function completely differently (Hawkins & Blakesley, 2007), however, the scientific search in the field of AI is based on biological studies of the human brain and its neural network (Kirova & Makarevich, 2018), and AI is respectively understood as a computer or cyber-physical system with anthropoform (human-like) “intelligence” (Morhat, 2017). Artificial intelligence is considered as one of the components of interpenetrating digital or information technologies along with others such as: big data, neurotechnology, distributed registry systems, cloud technologies, and so on. All of them are different in complexity, goals, and other characteristics of data in digital form, used in all types of socioeconomic activities (Order of the Government of the Russian Federation of July 28, 2017 No. 1632-p). For the purposes of this study, it is possible to distinguish them into data technologies and AI as technologies capable of autonomy and self-development. This allows us to state that currently the tax law provides for some variations in the use of technology, but not AI. At the same time, it is obvious that AI development and the expansion of its application will be reflected in taxation, which should be built in line with the general task set at the state level: the lack of barriers to the development of technological processes. RESULTS The relationship of taxation with the processes of digitalization can be divided into two directions. This involves, first of all, the use of digital technologies in the framework of tax control and interaction between tax authorities and other participants in tax relations and, secondly, the use of digital technologies by taxpayers in the tax accounting. These directions are not yet widespread, however, the existing legal regulation with regard to the practice of the application of these technologies already gives opportunity to assess its effectiveness, to identify positive and negative aspects. The President’s Address to the Federal Assembly of the Russian Federation dated March 1, 2018 notes that the planned development processes require financial resources and the future government will have to create new

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tax conditions as soon as possible. However, it is obvious that the financial burden on the economic sphere can be increased not only by rising taxes, but also in the form of shifting the costs associated with the development and implementation of digital technologies. Currently, the Tax Code of the Russian Federation provides for submission of tax returns, as well as explanations on them in electronic form through the operator of electronic document management (Articles 23, 80, 88 of Tax Code of the Russian Federation). The same communication channel can be used to transfer documents from the tax authority to the taxpayer in the course of tax legal relations (Article 31 of the Tax Code). In addition, according to article 169 of the Tax Code establishes a mandatory electronic invoicing (Order of the Ministry of Finance of the Russian Federation of November 10, 2015 No. 174n), accordingly, the obligation of economic entities to issue invoices through the operators of electronic document circulation. Since 2015, the Tax Code of the Russian Federation includes the possibility to transfer documents through the personal account in the interaction between the tax authority and the taxpayer-natural person (foreign organization; Federal law No. 347-FZ of 04.11.2014 (as amended on November 24, 2014). In general, it is clear that this is a convenient format of interaction with the tax authority, which allows individuals to obtain or provide the necessary documents to the tax authority without the unnecessary expense. In order to open a personal cabinet of a taxpayer an individual shall submit an application, in this case the tax authority shall stop providing the taxpayer with paper-based documents. At the same time, there is a possibility of refusing to use this service. This will resume sending all documents to such an individual by mail. At the same time, there are a number of points that require clarification. Thus, paragraph 2 of Article 11.2 of the Tax Code provides that in order to obtain paper-based documents, the taxpayer must send an appropriate notification to the tax authority. The Federal Tax Service specified that such notification can be sent not only in electronic form through the personal account, but also on paper personally or by mail (Letter of the FTS of Russia from August 2, 2017 No. SA-4-21/15178). In the text of subparagraph 5, paragraph 2 Article 11.2 of the Tax Code provides that a notification may be sent to any tax authority at the taxpayer’s choice. It is obvious that such a formulation emphasizes the importance of this tool for achieving maximum convenience for the taxpayer. The procedure for maintaining the personal account of the taxpayer, approved by the Order of the FTS of Russia (Order FNS of Russia of August 22, 2017 No. IIM-7-17/617), provides that in this case the paper-based documents should be sent to an individual

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after 3 working days from the date of receipt of the notification. It is not specified where they are sent and how. The format of the personal taxpayer account is mainly focused on individuals who do not carry out business activities, and, consequently, face tax relations, mainly, once a year, when the payment of taxes for the expired tax period is to be made. This resource is not frequently accessed by a taxpayer, which creates the risk that some documents placed by the tax authority in the personal account could be overlooked. Therefore, the development of the concept of convenience of this tool for the taxpayer involves notifying him about new documents in the personal account. For example, it is possible in the form of a letter sent to the taxpayer’s e-mail in automatic mode or by SMS. On this basis, it seems necessary to talk about the normative consolidation as a basic principle of the idea to provide the best possible convenience for users through the application of digital technologies in the field of state (municipal) management. A significant number of orders of the FTS of Russia is devoted to the approval of forms and formats of documents used in the framework of tax law and sent (received) in electronic form. A mandatory part of such documents is the “Description of the exchange file,” including the requirements for XML files to transfer information in electronic form. The content of this part of the documents remains an unsolved mystery for most lawyers and other law enforcement officers. This aspect is important. Digital technologies are software tools used in the process of emergence, change and termination of legal relations, regulating the exercise of relevant rights and obligations. This will inevitably lead to the filling of normative and other legal documents with technical information and terminology. However, it is easy to conceal the infringement of the interests of the subjects of legal relations behind a maze of words understandable only to a specialist of technical specifics. In this regard, it is necessary to talk about the development of specialized terminology, metalanguage (Malchukova & Nesterov, 2008), which could convey correctly the features of technological processes for the purposes and tasks of jurisprudence. The main task of legal regulation is to ensure the interests of the parties to the legal relationship, therefore, the content of the legal act should be understandable. This provision is contained in paragraph 6 of Article 3 of the Tax Code of the Russian Federation: legislation acts on taxes and fees shall be formulated so that everyone may know precisely what taxes (fees, insurance premiums) when and in which order he has to pay. This requirement applies to regulations issued by the representative tax authorities. However, in the context of widespread use of digital technologies, requirements should be extended to the acts of tax authorities.

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In Russia, the control based on the risk-oriented approach to its planning that is provided by the Federal law of 26.12.2008 No. 294-FZ (as amended on December 27, 2018) “About protection of the rights of legal entities and individual entrepreneurs at implementation of the state control (supervision) and municipal control” is more and more widely introduced in the practice of the state and municipal control. Despite the fact that the risk-oriented approach is based on the principles of openness and transparency of criteria for defining risks and related risk categories, as well as the results of the distribution of controlled entities according them (“Basic Model for Determining Risk,” 2017), however, the concept of the planning system of field tax audits provides that not all risk criteria for the commission of a tax offence are publicly available. In that regard, we believe that the strategy for the development of digitalization should be based on the transparency of algorithms and the possibility of their verification. CONCLUSIONS In general, it seems necessary to formulate the following thesis: normative legal regulation, regulating the use of digital technologies in the field of taxation, must meet the requirements of clarity, transparency and understandability established by paragraph 6 of Article 3 of the Tax Code. Thus, consideration of the few cases of regulation of the use of digital technologies in the field of taxation will allow to formulate recommendations on the establishment of a number of basic provisions that determine the requirements for legal regulation in connection with the use of such technologies: • inadmissibility of direct or indirect imposition of digital infrastructure services; • use of digital technologies in the sphere of state (municipal) management in order the best possible convenience for users of such technologies; and • conformity of legal acts regulating the use of digital technologies of the requirements of clarity, transparency and understandability. ACKNOWLEDGMENTS The study was carried out with the financial support of Russian Foundation for Basic Research (RFBR) in the framework of the scientific project No. 1829-16102 “Transformation of legal personality of participants of tax, budget, and public banking relations in the development of digital economy.”

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REFERENCES Basic Model for Determining Risk Categories, Developed by the Ministry of Economic Development, Has Been Approved. (2017, April 7). Retrieved from http://ar.gov.ru/en-US/presscentr/news/view/517 Castro D., & New, J. (2016). The promise of artificial intelligence. Retrieved from https://www2.datainnovation.org/2016-promise-of-ai.pdf Federal Law No. 347-FZ of 04.11.2014 (as amended on 24.11.2014) “On Amendments to Parts One and Two of the Tax Code of the Russian Federation.” Retrieved from http://www.consultant.ru/document/cons_doc_LAW_170541/ Hawkins, D., & Blakesley, S. (2007). On intelligence. New York, NY: Times Books. Hubbard, F. P. (2016). Allocating the risk of physical injury from “sophisticated robots”: Efficiency, fairness, and innovation. In R. Calo, A. M. Froomkin, I. Kerr (Eds.), Robot Law (pp. 35–50). Cheltenham, England: Edward Elgar. Karp, A. (2016, April 2). Deep learning will be huge—and here’s who will dominate it. Retrieved from https://venturebeat.com/2016/04/02/deep-learning-will -be-huge-and-heres-who-will-dominate-it/ Kirova, L., & Makarevich, M. (2018). Legal aspects of the use of neural networks. Innovative Economy: Prospects of Development and Improvement, 1(27), 58. Lukanov, A. S. (2017). Modern information technologies as an instrument of the legal system of the state. Legal Bulletin of Samara State University, 3, 10. Malchukova, N., & Nesterov, A., (2008). Artificial intelligence: Philosophy, methodology, innovation. Philosophical Sciences, 3, 137. Message of the President to the Federal Assembly of the Russian Federation of March 1, 2018. (2018). Retrieved from http://www.consultant.ru/document/ cons_doc_LAW_291976/ Morhat, P. (2017). Artificial intelligence: A legal opinion. Moscow, Russia. Neznamov, A. V., & Naumov, V.B. (2018). Strategy of regulation of robotics and cyberphysical systems. Law, 2, 69–89. Order of the Government of the Russian Federation of July 28, 2017 No. 1632-p “About the adoption institute programme “Digital Economy of the Russian Federation.” Retrieved from https://base.garant.ru/71734878/ Order of the Ministry of Finance of the Russian Federation of November 10, 2015 No. 174n “About approval of the Procedure for exposure and receipt of invoices electronically on telecommunication channels using the strengthened qualified digital signature.” Retrieved from https://cis-legislation.com/document.fwx?rgn=83190 Richards, N., & Smart, W. (2016). How should the law think about robots? In R. Calo, A. M. Froomkin, & I. Kerr (Eds.), Robot Law (p. 5). Cheltenham, England: Edward Elgar.

PART V THE IMPACT OF ARTIFICIAL INTELLIGENCE ON THE ECONOMY AND FINANCIAL SERVICES

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CHAPTER 48

ARTIFICIAL INTELLIGENCE AS AN ECONOMIC CATEGORY The Essence, Specifics, and Perspectives of Practical Application Svetlana V. Lobova Altai State University Aleksandra V. Zakharova State University of Management Viktor I. Dobrosotskiy Moscow State Institute of International Relations (University) of the Ministry of Foreign Affairs Russian Federation Dmitry V. Bateikin Altai State University

Meta-Scientific Study of Artificial Intelligence, pages 429–433 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

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430    S. V. LOBOVA et al.

ABSTRACT The purpose of the chapter is to study artificial intelligence (AI) as an economic category for determining its essence, specifics, and perspectives of practical application. A complex of logical methods issued analysis, synthesis, induction, deduction, classification, systematization, and analogy. The authors perform comparative analysis of the existing conceptual approaches to determining the role of AI in economy, comparative analysis of decision-making by nonintellectual machines, human intelligence (HI) and AI, and determine the role of AI in the system of subjects of decision-making. The authors specify the notion of AI as a new phenomenon in the modern economy, which could be a production factor, a subject of socioeconomic relations, and a regulator of economic activity. It is determined that AI is a specific subject of decision-making, which combines the best features of nonintellectual machines and HI. It is recommended to use AI only if its full-scale usage is possible. Where the sample decisions are possible, it is recommended to stick to nonintellectual machines, and where human communications and perspectives of automatization are weak, it is recommended to stick to human management. It is concluded that AI is to supplement, not replace, the existing subjects of decision-making—nonintellectual machines and HI.

The start of the fourth industrial revolution in the second decade of the 21st century became an initial point of targeted development of AI. This new direction in R&D is developed according to a nonstandard scenario. Instead of practical implementation of thoroughly developed conceptual ideas, which is characteristic of most innovations, announcement of creation of AI in the near future led to a wave of practical research while the theoretical basis of this phenomenon is not yet formed. Rapid development of practice, as compared to theory, was a reason for chaotic study of AI, which led to the three following problems of modern economic science. The first problem consists in obscureness of the limits of scientific study of AI. The second problem is repeating scientific research in the sphere of AI. The third problem is connected to the high level of risk in development of AI, the absence of a clear idea, and the offered conduct of risk management of this process (Burukina, Karpova, & Koro, 2019; Israni & Verghese, 2019; Miller, 2019; Tack, 2019). In the context of the above problems, the fundamental research of AI becomes very important. The purpose of this chapter is to study AI as an economic category for determining its essence, specifics, and perspectives of practical application.

Artificial Intelligence as an Economic Category    431

METHODOLOGY The authors use a complex of logical methods: analysis, synthesis, induction, deduction, classification, systematization, and analogy. As a result of literature overview, it is determined that the fundamental issues of studying AI are considered within three various conceptual approaches to determining the role of AI in the economy. Comparative analysis of these approaches is performed in Table 49.1. As is noted in Table 49.1, certain scholars—Küfner, Uhlemann, and Ziegler (2018); Li, Hou, Yu, Lu, and Yang (2017); Nayak, Sinnappoo, Wang, and Padhye (2016); and Takechi and Ishiketa (2005)—think that AI is a new production factor. They define AI as a managerial component of machine production. The direction of application of AI should be automatization of production, which the advantages are replacement of humans in hazardous types of activities and organization of hi-tech production. Other authors—Bogoviz (2019); Biryukov and Antonova (2019); Kouziokas (2017); Popkova (2019); Popkova and Sergi (2019); Popkova, Ragulina, and Bogoviz (2019); and Sukhodolov, Popkova, and Litvinova TABLE 49.1  Comparative Analysis of the Existing Conceptual Approaches to Determining the Role of AI in Economy Role of AI in Economy Criterion of Comparison

Production Factor

Subject of Socioeconomic Relations

Regulator of Economic Activity

Notion of AI

managerial component of machine production

subject of machine communications

Method of intellectual processing of Big Data for regulation of economy

Sphere of Application of AI

production

distribution

state management

Direction of Application of AI

automatization of production

automatization of marketing

automatization of monitoring and control of sectorial markets

Advantages of AI

Replacing human in hazardous types of activities, organization of hitech production

Increase of effectiveness of selling products and individualization of marketing

Increase of transparency of economy, its highprecision monitoring, and highly effective management

432    S. V. LOBOVA et al.

(2018)—point out the perspective role of AI as a regulator of economic activity. The offered direction of application of AI within this approach is automatization of monitoring and control over sectorial markets, which the advantages include increase of transparency of economy, its high-precision monitoring, and highly effective management. We think that AI is capable of simultaneous execution of all three determined roles and is therefore a complex economic category, which essence, specifics and perspectives of practical application are determined in this chapter. The basis for this is our definition of AI as a universal mechanism of collection and intellectual processing of data of any volume, which is capable of autonomous decision-making of any level of complexity, as well as communications and management of machines.

RESULTS For specifying the essence of the scientific category of AI, we performed its comparative analysis with two existing analogs—nonintellectual machines and HI (see Table 49.2). As is seen from Table 49.2, similarly to nonintellectual machines, AI has the following advantages, as compared to HI: ability to make complex (with a lot of factors and criteria) decisions, guarantee of making of rational decisions, and ability for long continuous work. According to the set of criteria of decision-making, AI is slightly behind HI, as it does not take into account non-formalized criteria. The performed analysis allowed determining the role of AI in the system of subjects of decision-making (see Figure 49.1). Figure 49.1 shows that AI has an intermediary role in the system of subjects of decision-making. It exceeds nonintellectual machines but lacks several aspects of HI. Nonintellectual machines can quickly make complex decisions on the basis of long routine analysis of big data and usage of templates. AI combines the advantages of both analogs. It is capable of making optimal decisions, balancing between objectivity and subjectivity due to machine communications, considering formalized qualitative criteria, and analyzing long routines of big data. AI can make any decisions due to intellectual analysis of information and has the ability to offer original (creative) decisions.

Artificial Intelligence as an Economic Category    433 TABLE 49.2  Comparative Analysis of Decision-Making by NonIntellectual Machines, HI, and AI

Criterion of Comparison

NonIntellectual Machines

AI

HI

Possibility of quick complex (with a lot of factors and criteria) decisions

yes

no

Guarantee of rational decisions

yes

no

Ability for long continuous work

yes

no

Ability for consideration of formalized qualitative criteria during decision-making

no

Consideration of non-formalized criteria during decision-making

yes

no

yes

Ability to communicate with machines

no

Ability to communicate with humans

no

yes, but with limitations

yes

Ability to control machines

no

yes

yes, but with limitations

Key risks of decision-making

yes

Non-optimal decisions . . .  due to consideration of only formalized quantitative criteria

due to consideration of only formalized criteria

due to foundation on irrational (subjective) criteria

Untimely (delayed) decision-making . . .  due to technical failures

due to software failures

due to decrease of work efficiency

CONCLUSION Thus, in the course of the research, the authors specified the notion of AI as a new phenomenon in the modern economy that is capable of being a production factor, a subject of socioeconomic relations, and the regulator of economic activity at the same time. It is determined that AI is a specific subject of decision-making, which combines the best features of nonintellectual machines and HI. At the same time, despite the multiple advantages, AI possesses two obvious drawbacks that limit the possibilities of its practical application. Firstly,

434    S. V. LOBOVA et al. Irrational (subjective) decision making: – human communications – foundation on non-formalized qualitative criteria

Fact making of complex decisions: – long routine analysis of Big Data; – usage of templates.

– machine communications; – consideration of formalized qualitative criteria; – long routine analysis of Big Data.

Human intelligence

Artificial Intelligence

Nonintellectual machines

– intellectual analysis of information – offer of original (creative) decisions

Slow making of simple decisions: – Short intellectual analysis of small volume of information; – offer of original (creative) decisions.

Rational (objective) decision making: – complete absence of communications – consideration of only formalized quantitative criteria

Figure 49.1  The role of AI in the system of subjects of decision-making.

digital modernization of economic activities on the basis of AI requires large investments and is peculiar for high complexity due to the necessity for complete transformation of all processes and systems. Secondly, usage of AI is justified only under the condition of its full-scale usage and is inexpedient for solving separate economic problems. Where the sample decisions are possible, it is recommended to stick to nonintellectual machines, and where human communications and perspectives of automatization are weak, it is recommended to stick to human management. REFERENCES Biryukov, A., & Antonova, N. (2019). Expert systems of real time as a key tendency of artificial intelligence in tax administration. Advances in Intelligent Systems and Computing, 850, 111–118. Bogoviz, A. V. (2019). Industry 4.0 as a new vector of growth and development of knowledge economy. Studies in Systems, Decision, and Control, 169, 85–91.

Artificial Intelligence as an Economic Category    435 Burukina, O., Karpova, S., & Koro, N. (2019). Ethical problems of introducing artificial intelligence into the contemporary society. Advances in Intelligent Systems and Computing, 876, 640–646. Israni, S. T., & Verghese, A. (2019). Humanizing artificial intelligence. JAMA—Journal of the American Medical Association, 321(1), 29–30. Kouziokas, G. N. (2017). The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia, 24, 467–473. Küfner, T., Uhlemann, T.H.-J., & Ziegler, B. (2018). Lean data in manufacturing systems: Using artificial intelligence for decentralized data reduction and information extraction. Procedia CIRP, 72, 219–224. Li, B.-H., Hou, B.-C., Yu, W.-T., Lu, X.-B., & Yang, C.-W. (2017). Applications of artificial intelligence in intelligent manufacturing: A review. Frontiers of Information Technology and Electronic Engineering, 18(1), 86–96. Miller, T. (2019). Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence, 267, 1–38. Nayak, R., Sinnappoo, K., Wang, L., & Padhye, R. (2016). Artificial intelligence: Technology and application in apparel manufacturing. In Y. Li & R. Padhye (Eds.), 9th Textile Bioengineering and Informatics Symposium Proceedings 2016 (Vol. 2; pp. 658–655). Hung Hom, Hong Kong: Polytechnic University. Popkova, E. G. (2019). Preconditions of formation and development of industry 4.0 in the conditions of knowledge economy. Studies in Systems, Decision, and Control, 169, 65–72. Popkova, E. G., & Sergi, B. S. (2019). Will Industry 4.0 and other innovations impact Russia’s development? Exploring the future of Russia’s economy and markets. Bingley, England: Emerald. Popkova, E. G., Ragulina, Y. V., & Bogoviz, A. V. (2019). Fundamental differences of transition to industry 4.0 from previous industrial revolutions. Studies in Systems, Decision, and Control, 169, 21–29. Sukhodolov, A. P. Popkova, E. G., & Litvinova, T. N. (2018). Models of modern information economy: Conceptual contradictions and practical examples. Bingley, England: Emerald. Tack, C. (2019). Artificial intelligence and machine learning—applications in musculoskeletal physiotherapy. Musculoskeletal Science and Practice, 39, 164–169. Takechi, H., & Ishiketa, T. (2005, October 19–22). Artificial intelligence for query inference in manufacturing management databases. In LEM 2005—3rd International Conference on Leading Edge Manufacturing in 21st Century (pp. 409– 414). The Japan Society of Mechanical Engineers.

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CHAPTER 49

ECONOMIC FACTORS OF AI AND THE TOOLS OF THEIR CONTROL Yury L. Talismanov Russian Presidential Academy of National Economy and Public Administration Elena A. Kirova State University of Management Sergei V. Shkodinsky Research Institute of Finance of the Ministry of Finance of the Russian Federation Moscow Natalia A. Rykhtikova Krasnogorsk Branch of Russian Presidential Academy of National Economy and Public Administration Andrey G. Nazarov All-Russian Public Organization “Business Russia”

Meta-Scientific Study of Artificial Intelligence, pages 437–444 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

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ABSTRACT The purpose of this chapter is to determine the economic factors of AI and the tools of their control. The authors use the following methods: classification, systematization, logical analysis, development of algorithms, formalization, and the case method for studying the current economic practice and strengthening the evidential base of the research. It is substantiated that the process of creation, implementation, and dissemination of AI is subject to the influence of a range of factors, which include the factors of demand (accessibility of investments, consumer value), the factors of offer (provision with digital personnel, systemic character of digital modernization), and the factors of infrastructure (accessibility of technologies, equipment and software provision, and sufficiency of institutional provision). Differences in the influence of selected economic factors on development of AI in certain parts of the world in 2018 and on various types of activities in Russia in 2018 are shown.

AI is one of the breakthrough digital technologies that is to ensure the transition of the modern socioeconomic systems to Industry 4.0. This predetermines active state financing of R&D in the sphere of AI. In its turn, research institutes consider AI as a perspective object for studying and use its resources for it. Entrepreneurial structures expect potential advantages from usage of AI (large growth of efficiency, hi-tech production, and automatization of business processes) and are getting ready for its quick implementation. It is supposed that the conditions for dissemination of AI will be favorable. However, as the modern practical experience of digital modernization of the economy shows, the conditions of creation and dissemination of innovative technologies are very different. On the one hand, there are sectoral differences. Certain spheres of economy actively implement digital technologies and increase the level of automatization of business processes (e.g., service sphere and industry), while other spheres preserve the previous technological mode (e.g., agriculture). On the other hand, there are clear differences in view of the national and regional economies. Typologization of the countries of the world as to the level and rate of socioeconomic development is applicable to the speed of their digital modernization: developed countries show the highest speed, and developing countries show stability under run from them. This spatial and sectoral differentiation of the modern socioeconomic systems as to the level of favorability of conditions for their digital modernization is the basis for the working hypothesis of this chapter; like other breakthrough digital technologies, AI is subject to the influence of economic factors and could be successfully implemented into the economic

Economic Factors of AI and the Tools of Their Control     439

practice only under the condition of their systemic, positive influence, which creates favorable conditions for this. The purpose of the work is to determine the economics factors of AI and the tools of their control. METHODOLOGY As a result of content analysis of the existing studies and publications on the topic of AI, we determined that in most of the works of modern authors, AI is treated as a progressive digital technology which is to oust the previous technologies in the near future. It is supposed that dissemination of AI is not subject to influence of any specific economic factors and is an inevitable and regular stage in development of socioeconomic systems. This point of view is presented in the works of Allam and Dhunny (2019); Cao, Lu, Wen, Lei, and Hu (2018); Jaafari, Zenner, Panahi, and Shahabi (2019); Kumar Deb, Jain, and Deb (2018); and Partel, CharanKakarla, and Ampatzidis (2019). An alternative point of view is presented in the works of Bogoviz (2019); Gonzalez (2017); Khan (2018); Klintong, Vadhanasindhu, and Thawesaengskulthai (2012); Popkova (2019); Popkova and Sergi (2019); Popkova, Ragulina, and Bogoviz (2019); and Sukhodolov, Popkova, and Litvinova (2018). These scholars are confident in the necessity for mandatory consideration of economic factors in view of dissemination of AI. In this work, we stick to this point of view and use the methods of classification, systematization, logical analysis, development of algorithms, formalization, and case method for studying the current economic practice and strengthening the evidential base of the research. RESULTS The economic factors of AI and the perspective tools of their control at the public and corporate level are determined (see Table 49.1). • As is seen from Table 49.1, economic factors of AI are classified according to the criterion of belonging to the companies that implement it and are divided into three categories: First Category: Factors of demand—external as to the companies that implement AI, determining demand for it in the market. In this category, we distinguish this factor as accessibility of investments for digital modernization of companies on the basis of AI.

440    Y. L. TALISMANOV et al. TABLE 49.1  Economic Factors of AI and Different Tools of Their Control Group of Factors

Factors

Directions of Influence

Tools of Management

Level of Management

Investment marketing

corporate

Stimulation of investments

public

Consumer marketing

corporate

Social marketing

public

Modernization of education, state order

public

Personnel marketing

corporate

Stimulation of digital modernization

public

Factors of Demand (external)

accessibility of investments

direct

consumer value

direct

Factors of Offer (internal)

provision of digital personnel

direct

systemic character of digital modernization

direct

accessibility of technologies, equipment, and software provision

direct

sufficiency of institutional provision

direct

Factors of Infrastructure (external)

Reorganization of business corporate Provision of grants for R&D, stimulation of import substitution

public

Development of foreign economic activities

corporate

Development of institutional provision

public

Marketing of interaction with the state

corporate

The lower the level of risks of investments and the more profitable the conditions of investing, the more favorable the influence of this factor on dissemination of AI at the corresponding market. • Second Category: Factor of offer—internal as to the companies, determining their ability and readiness for implementation of AI. In this category, we distinguish this factor as provision of digital personnel. The higher the offer of digital personnel in the labor market and the higher the level of their qualification and the more predictable the set of their competences, the more favorable the influence of this factor on dissemination of AI in the corresponding market. • Third Category: Factors of infrastructure—external as to the companies that implement AI, determining the essential possibility of implementing AI at this market. In this category, we distinguish this factor as accessibility of technologies, equipment, and software provision. The logic of managing the determined economic factors of AI with the help of the offered tools is shown by the developed algorithm (see Figure 49.1).

Economic Factors of AI and the Tools of Their Control     441

Is there demand for the products manufactured with the usage of AI?

No, deficit of demand

Corporate and state management that is aimed at stimulation of demand

Yes

Is the infrastructural provision of AI sufficient? No, deficit of infrastructure Yes

Yes

Corporate and state management of infrastructure development

Is the offer of AI sufficient? No, deficit of offer Corporate and state management that is aimed at stimulation of digital modernization of business

Figure 49.1  The algorithm of managing the economic factors of AI.

Figure 49.1 shows that it is necessary to provide sustainable and sufficient demand for the products that are manufactured and sold with the help of AI, as in case of a deficit of demand, there are no market stimuli for implementation of the potential of digital modernization of business on the basis of AI. The influence of the selected economic factors on development of AI in certain countries of the world in 2018 is analyzed in Table 49.2, and on various types of activities in Russia in 2018 in Table 49.3. The data of Table 49.2 and Table 49.3 show that the level of demand and offer and infrastructural provision of digital modernization of business on the basis of AI are very different in countries of the world and in different

442    Y. L. TALISMANOV et al. TABLE 49.2  Evaluation of the Influence of the Selected Economic Factors on Development of AI in Certain Countries of the World in 2018

Country

Research and Development Expenditures (% of GDP)

Researchers in R&D (per million people)

Germany

2.94

4,893.15

The New High-Tech Strategy Innovations for Germany

United States

2.74

4,313.38

Strategy for American Innovation National Robotics Initiative 2.0: Ubiquitous Collaborative Robots (NRI–2.0)

United Kingdom

1.69

4,429.58

Eight Great Technologies

France

2.25

4,307.11

La Nouvelle France Industrielle

Japan

3.15

5,209.97

5th Science and Technology Basic Plan

China

2.11

1,205.68

Made in China 2025

Russia

1.10

2,979.10

Federal Program “Digital Economy of the Russian Federation”

Adopted Concept of Digital Modernization of Economy

Source: Compiled by the authors based on Federal State Statistics Service (2019).

Postgraduates (digital personnel)

Type of Economic Activities

Expenditures for Technological Innovations, RUB Billion

TABLE 49.3  Evaluation of the Influence of the Selected Economic Factors on Development of AI on Various Types of Activities in Russia in 2018

Adopted Concept of Digital Modernization

777.5

58

Concept “Digital Energy”

Assembly of buildings from composite constructions, construction of coverings for buildings, and other construction works

0.0

26

—

Communications, activities connected to computational equipment and IT, scientific R&D, and provision of other types of services

492.1

133

—

Crop research, cattle breeding, crop research with cattle breeding (combined agriculture), provision of services in the sphere of crop research, landscape gardening, and cattle breeding except for veterinarian services

15.0

241

Extracting and processing productions, production and distribution of electric energy, natural gas, and water

Concept “Scientific and Technological Development of ‘Digital agriculture’”

Source: Compiled by the authors based on Federal State Statistics Service (2019).

Economic Factors of AI and the Tools of Their Control     443

types of activities. Therefore, the conditions for creation, implementation, and dissemination of AI in them are different, and it is necessary to apply different tools of managing the economic factors. CONCLUSIONS Thus, as a result of the research it is determined that the process of creation, implementation, and dissemination of AI is subject to the influence of the whole range of the factors, which include the factors of demand, factors of offer, and the factors of infrastructure. Management of these factors are to be conducted in a strict logical order, for which the corresponding algorithm is offered, with application of the tools of marketing and state regulation at the corporate and state levels at the same time. REFERENCES Allam, Z., & Dhunny, Z. A. (2019). On big data, artificial intelligence and smart cities. Cities, 89, 80–91. Bogoviz, A. V. (2019). Industry 4.0 as a new vector of growth and development of knowledge economy. Studies in Systems, Decision and Control, 169, 85–91. Cao, G., Lu, Z., Wen, X., Lei, T., & Hu, Z. (2018). AIF: An artificial intelligence framework for smart wireless network management. IEEE Communications Letters, 22(2), 8119495, 400–403. Federal State Statistics Service of the RF (Rosstat). (2019). Russia in numbers: Short statistical collection. Retrieved from http://www.gks.ru/bgd/regl/b18_11/Main .htm Gonzalez, W. J. (2017). Artificial intelligence in a new context: “Internal” and “external” factors. Minds and Machines, 27(3), 393–396. Jaafari, A., Zenner, E. K., Panahi, M., & Shahabi, H. (2019). Hybrid artificial intelligence models based on a neuro-fuzzy system and metaheuristic optimization algorithms for spatial prediction of wildfire probability. Agricultural and Forest Meteorology, 266–267, 198–207. Khan, A. (2018). Cognitive vehicle design guided by human factors and supported by Bayesian artificial intelligence. Advances in Intelligent Systems and Computing, 597, 362–372. Klintong, N., Vadhanasindhu, P., & Thawesaengskulthai, N. (2012). Artificial intelligence and successful factors for selecting product innovation development. In Proceedings—3rd International Conference on Intelligent Systems Modelling and Simulation, ISMS 2012, 6169736, 397–402. Kumar Deb, S., Jain, R., & Deb, V. (2018). Artificial intelligence creating automated insights for customer relationship management. In Proceedings of the 8th International Conference Confluence 2018 on Cloud Computing, Data Science and Engineering, Confluence 2018, 8442900, 758–764.

444    Y. L. TALISMANOV et al. Partel, V., CharanKakarla, S., & Ampatzidis, Y. (2019). Development and evaluation of a low-cost and smart technology for precision weed management utilizing artificial intelligence. In Computers and Electronics in Agriculture, 157, 339–350. Popkova, E. G. (2019). Preconditions of formation and development of industry 4.0 in the conditions of knowledge economy. Studies in Systems, Decision and Control, 169, 65–72. Popkova, E. G., Ragulina, Y. V., & Bogoviz, A. V. (2019). Fundamental differences of transition to industry 4.0 from previous industrial revolutions. Studies in Systems, Decision, and Control, 169, 21–29. Popkova, E. G., & Sergi, B. S. (2019). Will Industry 4.0 and other innovations impact Russia’s development? Exploring the future of Russia’s economy and markets. Bingley, England: Emerald. Sukhodolov, A. P., Popkova, E. G., & Litvinova, T. N. (2018). Models of modern information economy: Conceptual contradictions and practical examples. Bingley, England: Emerald. World Bank. (2019). Indicators: Science & technology. Retrieved from https://data .worldbank.org/topic/science-and-technology?view=chart

CHAPTER 50

POST-ECONOMY OF AI New Challenges and Perspectives of Sustainable Development of Socio-Economic Systems Evgenii M. Buhvald Institute of Economics of Russian Academy of Sciences Elena I. Larionova Financial University under the Government of the Russian Federation Pavel T. Avkopashvili Altai State University Syuzanna T. Adamyants Moscow State Institute of International Relations Alexander N. Alekseev Plekhanov Russian University of Economics

Meta-Scientific Study of Artificial Intelligence, pages 445–452 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

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ABSTRACT The purpose of this chapter is to develop a conceptual model of sustainable development of socioeconomic systems in the post-economy of AI. Regression analysis is used for determining the dependence of the change of the sustainable development index on the change of the digital competitiveness index. The research objects are countries that are leaders in the global rating of the level of sustainability of development according to the result of 2018, with Russia in the 63rd position. The authors determined the contradictory influence of digital modernization on the basis of AI on the implementation of global goals in the sphere of sustainable development. A conceptual model of sustainable development of socioeconomic systems in the post-economy of AI is developed. It shows framework outlines of balancing the emerging new challenges and perspectives.

Just after the start of the fourth industrial revolution (2012–2018), this revolution has had a strong influence on modern socioeconomic systems. It’s transformation on the basis of the leading digital technologies stimulates the transition to a new technological mode—Industry 4.0, which will lead to formation of a new evolutionary type of economy. Of course, each new leading digital technology is breakthrough, but in our opinion, AI is the most revolutionary technology, as it is the basis of other leading digital technologies. Thus, we think that deep changes in the modern socioeconomic systems in the near future will be determined primarily by creation and dissemination of AI. Institutionalization of these practices will lead to the creation of the post-economy of AI. The essential difference from the modern post-industrial economy is the full automatization of the production and distribution processes on the basis of the cyber-physical systems under the control of AI. In the context of the abovementioned specific features, the problem of sustainable development of socioeconomic systems in the post-economy of AI becomes very important. The working hypothesis of the research is that transition to this new evolutionary type of economy will open new perspectives for sustainable development and will create new challenges for implementing the corresponding global goals. METHODOLOGY The performed overview of the existing research and publications on the selected topic showed that the concept of “post-economy of AI” is new for modern economic science. At the same time, most of the works of modern scholars are devoted to its separate manifestations through the prism of implementation of the goals in the sphere of sustainable development; namely,

Post-Economy of AI    447

• knowledge economy as an economic system (Ahmed, 2017a, 2017b; Jabłoński, 2018; Ogundeinde & Ejohwomu, 2016; Pînzaru, Anghel, & Mihalcea, 2017; Popkova, 2019; Popkova, Ragulina, & Bogoviz, 2019; Popkova & Sergi, 2019; Ramos-Gil, Márquez-Domínguez, & Romero-Ortega, 2018; Sukhodolov, Popkova, & Litvinova 2018; Tiron-Tudor, Nistor, C. S., & Ştefănescu, 2018); • digital economy as an economic system in which digital technologies are widely used, and information is transferred into the digital form (Gazzola, Colombo, Pezzetti, & Nicolescu, 2017); and • Industry 4.0 as a new technological model (Bogoviz, 2019). The authors used regression analysis to determine the dependence of the change of the sustainable development index on the change of the digital competitiveness index. Statistical data are given in Table 50.1. TABLE 50.1  The Values of the Sustainable Development Index and the Digital Competitiveness Index in the Countries with the Most Sustainable Development and in Russia (2018) Position in the Rating of Sustainability of Development

Country

Index of Sustainable Development (points 1–100)

Index of Digital Competitiveness (points 1–100)

1

Sweden

85.0

97.453

2

Denmark

84.6

96.764

3

Finland

83.0

95.248

4

Germany

82.3

85.405

5

France

81.2

80.753

6

Norway

81.2

95.724

7

Switzerland

80.1

95.851

8

Slovenia

80.0

71.427

9

Austria

80.0

86.770

10

Iceland

79.7

82.654

11

Netherlands

79.5

93.886

12

Belgium

79.0

82.165

13

Czech Republic

78.7

71.488

14

United Kingdom

78.7

93.239

15

Japan

78.5

82.170

63

Russian Federation

68.9

65.207

Source: Compiled by the authors based on Bertelsmann Stiftung and Sustainable Development Solutions Network (2019), IMD (2019).

448    E. M. BUHVALD et al. TABLE 50.2  Results of the Regression Analysis of Dependence of the Change of the Sustainable Development Index on the Change of the Digital Competitiveness Index Regression Statistics  Multiple R

0.7026

  R 2

0.4936

 Normed R 2

0.4574

  Standard Error

2.6456

 Observations

16

Dispersion Analysis Regression

df

SS

MS

F

Significance F

1

95.5201

95.5201

13.6471

0.0024

6.9993

Leftover

14

97.9899

Total

15

193.5100

Coefficients

Standard error

t-statistics

R-Value

Lower 95%

Upper 95%

58.7625

5.7935

10.1428

0.0000

46.3366

71.1884

0.2472

0.0669

3.6942

0.0024

0.1037

0.3907

Y-crossing

The preliminary logical analysis of the data for Table 50.1 showed that the countries with the most sustainable development have the highest digital competitiveness in 2018. More precise results, obtained with the help of regression analysis, are given in Table 50.2. The data from Table 50.2 show that growth of the digital competitiveness index by one point leads to increase of the sustainable development index by 0.2472 points. Significance F (0.0024) does not exceed 0.05, which shows statistical significance of the determined regression dependence at the level α = 0.05. The coefficient of determination (R 2 = 0.4936) shows that the change of the sustainable development by 49.36% is explained by the change of the digital competitiveness index. RESULTS Bases on the global goals in the sphere of sustainable development, which were formulated and adapted by the UN, we determined new challenges and perspectives of sustainable development socioeconomic systems in the post-economy of AI (see Table 50.3). As is seen from Table 50.2, in the post-economy of new AI perspectives for implementing almost all goals in the sphere of sustainable development are created, but there are also new challenges for implementation of a lot

Post-Economy of AI    449 TABLE 50.3  New Challenges and Perspectives of Sustainable Development of Socio-Economic Systemsin the Post-Economy of AI Goals of Sustainable Development

New Challenges —

New Perspectives

1

Increase of living standards

2

Food security

3

Healthy living

—

Increase of labor load and possibility of implementing healthy living

4

Accessibility of education

—

Development of EdTech, increase of accessibility of education

5

Gender equality

—

Gender equality due to division into humans (without gender differentiation) and machines

6

Ecological effectiveness

—

Increased intellectual ecological control

7

Accessibility of energy

Growth of energy consumption

8

Economic growth

Additional social risks of crises

New vectors of growth and innovational development

9

Innovational development

Risks of innovational activities

in the sphere of HighTech

10

Reduction of disproportions in development of countries

Increase of disproportions of developed and developing countries

Increase of living standards of the Third World countries

11

Provision of security

Problem of cyber security

Intellectual control of security

12

Resource effectiveness

Growth of consumption of resources

Increase of resource effectiveness due to more precise production, distribution, and consumption

13

Fighting climate change

—

Intellectual support for fighting the climate changes

14

Effectiveness of usage of water resources

—

15

Effectiveness of usage of forest resources

Intellectual control over the usage of resources, increase of effectiveness

16

Globalization and effectiveness of institutes

Problem of emergence of the institute of interaction of humans and machines

Expansion of possibilities of globalization, automatization for optimization of institutes

17

Global integration

—

General socioeconomic problems as stimuli for integration

Artificial synthesis of food

Source: Compiled by the authors based on UN, 2019.

Increase of accessibility of benefits Increase of accessibility of food

—

450    E. M. BUHVALD et al. Direction 1: fighting the challenges of sustainable development in the posteconomy of AI

Goal: sustainable development of socio-economic systems in the post-economy of AI 1. development of the sphere Ed Tech; 2. development of the sphere High Tech; 3. usage of new possibilities of environment protection and mass production and distribution of food; 4. intellectual optimization of institutes.

T O O L S

1. Search for the means of reduction of resource and energy consumption; 2. management of social risks; 3. provision of cyber security; 4. responsible production and consumption and leveling of disproportions.

Direction 2: implementing the perspectives of sustainable development in the posteconomy of AI

Result: provision of balance and high effectiveness of the posteconomy of AI

Figure 50.1  The conceptual model of sustainable development of socioeconomic systems in the post-economy of AI.

of goals. New challenges and perspectives are systematized in the conceptual model of sustainable development of socioeconomic systems in the post-economy of AI (see Figure 50.1). As is seen from Figure 50.1, the offered conceptual model aims at sustainable development of socioeconomic systems in the post-economy of AI. The tools of its achievement are two directions: the first direction is fighting the challenges of sustainable development in the post-economy of AI, and the second direction is the implementation of perspectives of sustainable development in the post-economy of AI. Both of these directions are closely interconnected and are implemented at the same time. CONCLUSIONS As a result of the research, the offered hypothesis on the influence of digital modernization on the basis of AI on implementation of the global goals in the sphere of sustainable development is proved. On the one hand, creation and dissemination of AI opens new perspectives for sustainable development, increasing accessibility of benefits (including food and education), ensuring gender equality in view of division into humans (without consideration of sex) and machines, allowing for increased intellectual ecological

Post-Economy of AI    451

control, creating new vectors of growth and innovational development in the sphere of high tech, increasing living standards of the countries of the third world, and so on. On the other hand, the post-economy of AI has new challenges for sustainable development due to popularization of artificially created food, growth of resource and energy consumption, emergence of additional social risks of crises of interaction of people and machines and innovational activities, increase of disproportions of developed and developing countries, and aggravation of the problem of cyber security. REFERENCES Ahmed, E. M. (2017a). Erratum to: ICT and human capital spillover effects in achieving sustainable East Asian knowledge-based economies. Journal of the Knowledge Economy, 8(3), 1113. Ahmed, E. M. (2017b). ICT and human capital spillover effects in achieving sustainable East Asian knowledge-based economies. Journal of the Knowledge Economy, 8(3), 1086–1112. Bertelsmann Stiftung and Sustainable Development Solutions Network. (2019). Sustainable development index and dashboards report 2018. Retrieved from http:// sdgindex.org/assets/files/2018/01%20SDGS%20GLOBAL%20EDITION %20WEB%20V9%20180718.pdf Bogoviz, A. V. (2019). Industry 4.0 as a new vector of growth and development of knowledge economy. Studies in Systems, Decision, and Control, 169, 85–91. Gazzola, P., Colombo, G., Pezzetti, R., Nicolescu, L. (2017). Consumer empowerment in the digital economy: Availing sustainable purchasing decisions. Sustainability (Switzerland), 9(5), 693. IMD. (2019). World Digital Competitiveness Ranking 2018. Retrieved from https:// www.imd.org/wcc/world-competitiveness-center-rankings/world-digital -competitiveness-rankings-2018/ Jabłoński, M. (2018). Value migration to the sustainable business models of digital economy companies on the capital market. Sustainability (Switzerland), 10(9), 3113. Ogundeinde, A., & Ejohwomu, O. (2016). Knowledge economy: A panacea for sustainable development in Nigeria. Procedia Engineering, 145, 790–795. Pînzaru, F., Anghel, L., & Mihalcea, A. (2017). Sustainable management in the new economy: Are Romanian companies ready for the digital challenge? In Proceedings of the 5th International Conference on Management Leadership and Governance, ICMLG 2017, 346–352. Popkova, E. G. (2019). Preconditions of formation and development of industry 4.0 in the conditions of knowledge economy. Studies in Systems, Decision, and Control, 169, 65–72. Popkova, E. G., Ragulina, Y. V., & Bogoviz, A. V. (2019). Fundamental differences of transition to industry 4.0 from previous industrial revolutions. Studies in Systems, Decision, and Control, 169, 21–29.

452    E. M. BUHVALD et al. Popkova, E. G., & Sergi, B. S. (2019). Will Industry 4.0 and other innovations impact Russia’s development? Exploring the future of Russia’s economy and markets, 34–42. Bingley, England: Emerald. Ramos-Gil, Y., Márquez-Domínguez, C., & Romero-Ortega, A. (2018). The press in the context of the Andean community of nations (CAN): Without sustainable monetization in the digital economy. Advances in Intelligent Systems and Computing, 721, 1084–1093. Sukhodolov, A. P., Popkova, E. G., & Litvinova, T. N. (2018). Models of modern information economy: Conceptual contradictions and practical examples. Bingley, England: Emerald. Tiron-Tudor, A., Nistor, C. S., & Ştefănescu, C. A. (2018). The role of universities in consolidating intellectual capital and generating new knowledge for a sustainable bio-economy. Amfiteatru Economic, 20(49), 599–615. UN. (2019). Goals in the sphere of sustainable development. Retrieved from https:// www.un.org/sustainabledevelopment/ru/sustainable-development-goals/

CHAPTER 51

THE ROLE OF THE SYSTEM OF PROVISION OF CYBER SECURITY AMONG THE ECONOMIC FACTORS OF AI Viktor I. Dobrosotskiy Moscow State Institute of International Relations University Elena L. Gulkova State University of Management Mikhail Y. Zakharov State University of Management Pavel T. Avkopashvili Altai State University

ABSTRACT The purpose of the chapter is to determine the specifics and roles of the system of provision of cyber security among the economic factors of AI and to

Meta-Scientific Study of Artificial Intelligence, pages 453–459 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

453

454    V. I. DOBROSOTSKIY et al. determine the principles of managing these factors for supporting its positive influence on creation, implementation, and dissemination of AI. Regression analysis is used for determining the influence of the level of cyber security on usage of digital technologies in modern socioeconomic systems. Statistically, significant influence of the level of cyber security on the usage of digital technologies by the state in modern socioeconomic systems is substantiated, which allows assigning the system of provision of cyber security to the number of economic factors of AI.

The perspective of creating AI in the midterm (before 2024) and disseminating AI in the long term (2030) actualized the problem of provision of cyber security. This problem has three aspects. The first aspect is secure storing of digital data that are used by AI. As AI uses only digital data, all information that is necessary for its work should be unified into one system of digital data. Unification of all information in one place and its transfer into the digital form may lead to its loss or distortion in case of software failures, which violate the conditions of storing digital information. The second aspect of the problem of provision of cyber security is supporting continuous and correct AI work. AI is a complex technical device with software that requires update, maintenance, and repairs. Untimely elimination of failures in the work of AI may lead to termination of its work, reduction of efficiency, or distortion of its work (violation of cognitive algorithms). The third aspect of the problem of provision of cyber security is protection of digital data and AI from cyber criminals. This aspect includes protection of personal information, preservation of national and commercial secrets, and prevention of unsanctioned digital operations that are controlled by AI (e.g., bank transfers). The multi-aspect character of the problem of provision of cyber security predetermines the high complexity of its solution. Based on this, we offer a hypothesis that the system of provision of cyber security occupies a special (the most important) role among the economic factors of AI. The purpose of this chapter is to determine the specifics and roles of the system of provision of cyber security among the economic factors of AI and to determine the principles of managing this factor for supporting its positive influence on creation, implementation, and dissemination of AI. METHODOLOGY The system of provision of cyber security belongs to the economic factors of AI in most of the works of modern authors: Bogoviz (2019); Kshetri (2013); LeClair and Pheils (2014); Lerums and Dietz (2018); Popkova (2019); Popkova and Sergi (2019); Popkova, Ragulina, and Bogoviz (2019); Srinivas, Das,

System of Provision of Cyber Security Among the Economic Factors of AI    455

and Kumar (2019); Sukhodolov, Popkova, and Litvinova (2018); Trifonov, Manolov, Yoshinov, Tsochev, and Pavlova (2017); Yan, Bo, and Ni (2014); and Zorin, Grinavtseva, Bogomolova, Morozova, and Litvinova (2016). We use regression analysis for determining the influence of the level of cyber security (cyber security index, calculated by the International Telecommunication Union, 2019) on the usage of digital technologies in modern socioeconomic systems in view of the subjects of usage: • consumers for intellectual consumption: purchase and usage of products with the help of AI; • business for intellectual entrepreneurship: automatization of business processes on the basis of AI; and • state for implementation of intellectual government: state regulation of economy and provision of state services on the basis of AI. The information and analytical basis of the research is statistical data of “The Global Information Technology Report,” prepared within the World Economic Forum (2016). In particular, the following indicators are used: • sixth pillar—individual usage as the indicator of usage of digital technologies by consumers; • seventh pillar—business usage as the indicator of usage of digital technologies by business. • eight pillar—government usage as the indicators of usage of digital technologies by the state. The objects of the research are the countries that show the highest readiness for implementing AI, according to Entrepreneur (Singh, 2018). The initial statistical data for the research are shown in Table 51.1, and results of the analysis are shown in Tables 51.2 and 51.3. The data in Table 51.2 shows the absence of statistically significant dependence of the usage of digital technologies by consumers on the level of cyber security, as significance F (0.1592) exceeds 0.05 (equation of regression is statistically insignificant at the significance level α = 0.05), and R 2 is very low (0.1879), which shows weak correlation of the indicators (18.79%). The data of Table 51.3 show the absence of statistically significant dependence of usage of digital technologies by business on the level of cyber security, as significance F (0.4517) exceeds 0.05 (regression equation is statistically insignificant at the significance level α = 0.05), and R 2 is very low (0.0578), which shows weak correlation of the indicators (5.78%).

456    V. I. DOBROSOTSKIY et al. TABLE 51.1  The Level of Cyber Security and Usage of Digital Technologies in a Selection of Countries in View of the Subjects (consumers, business, state) in 2018 6th Pillar: Individual Usage, points 1–7 (y1)

7th Pillar: Business Usage, points 1–7 (y2)

8th Pillar: Government Usage, points 1–7 (y3)

Cybersecurity Index, points 0–1 (x)

China

3.9

3.9

4.6

0.624

United States

6.2

5.9

5.4

0.919

Germany

6.2

5.8

4.8

0.679

South Korea

6.5

5.4

5.6

0.782

Canada

5.7

4.9

5.1

0.818

France

6.0

5.0

5.3

0.819

Sweden

6.7

6.0

5.0

0.733

Japan

6.4

5.9

5.4

0.786

United Kingdom

6.6

5.2

5.4

0.783

Finland

6.6

5.8

5.0

0.741

Singapore

6.4

5.4

6.3

0.925

Russia

5.3

3.6

4.4

0.788

Country

Source: Compiled by the authors based on International Telecommunication Union (2019), World Economic Forum (2019).

TABLE 51.2  Regression Analysis of the Influence of the Level of Cyber Security on the Usage of Digital Technologies by Consumers in the Selection of Countries in 2018 Regression Statistics Multiple R

0.4335

R-Square

0.1879

Normed R-Square

0.1067

Standard Error

0.7447

Observations

12

Dispersion Analysis df

SS

MS

F

Significance F

1

1.2833

1.2833

2.3140

0.1592

Leftover

10

5.5458

0.5546

Total

11

6.8292

Coefficients

Standard error

t-statistics

R-Value

Lower 95%

Upper 95%

Y-crossing

2.9357

2.0531

1.4299

0.1832

–1.6388

7.5102

x

3.9663

2.6074

1.5212

0.1592

–1.8432

9.7758

Regression

Source: Calculated by the authors.

System of Provision of Cyber Security Among the Economic Factors of AI    457 TABLE 51.3  Regression Analysis of the Influence of the Cyber Security Level on Usage of Digital Technologies by Business in Selection of Countries in 2018 Regression Statistics Multiple R

0.2404

R-Square

0.0578

Normed R-Square Standard Error Observations

–0.0364 0.7997 12

Dispersion Analysis df Regression

SS

MS

F

Significance F

0.6133

0.4517

1

0.3922

0.3922

Leftover

10

6.3945

0.6394

Total

11

6.7867

Coefficients

Standard error

t-statistics

R-Value

Lower 95%

Upper 95%

Y-Crossing

3.5163

2.2046

1.5950

0.1418

–1.3957

8.4284

x

2.1926

2.7997

0.7831

0.4517

–4.0456

8.4308

Source: Calculated by the authors.

RESULTS The performed structural and logical analysis of the economic factors of AI, which include investment attractiveness of the projects on creation and implementation of AI, activity of R&D in the sphere of AI, and so on, showed that the system of provision of cyber security is distinguished against this background. Unlike other economic factors, which have short-term influence on AI (e.g., investments or R&D are necessary only for the initial implementation or one-time update of AI), cyber security has long-term influence on AI—during the whole period of its usage. While other economic factors have fragmentary influence on the interested, cyber security has systemic influence on all interested parties in AI. The results of the performed analysis allowed determining the role of the system of provision of cyber security among the economic factors of AI (see Figure 51.1). As is seen from Figure 51.1, the system of provision of cyber security has a special role among the economic factors of AI, being the most important factor—as it determined the influence of other factors and the level of demand, offer, and regulation of AI. For supporting the positive influence of

458    V. I. DOBROSOTSKIY et al. System of provision of cyber security

influence

determination of demand Intellectual consumption

determination of offer Intellectual entrepreneurship

determination of regulation Intellectual government

Other factors, including investment attractiveness of the projects on creation and implementation of AI, activity of R&D in the sphere of AI, etc.

Figure 51.1  Role of the system of provision of cyber security among the economic factors of AI.

the system of provision of cyber security on creation, implementation, and dissemination of AI, we developed the following principles of managing this factor, which are recommended for practical application: • the principle of comprehensiveness, • principle of high technology, and • poly-subjective principle. CONCLUSIONS Thus, the system of provision of cyber security is the strongest economic factor of AI, as it has the systemic influence on other factors and all interested parties—intellectual consumers, intellectual companies, and intellectual government. That’s why during management of economic factors it is recommended to pay attention to provision of cyber security by the principles of comprehensiveness, high technology, and poly-subjective character. REFERENCES Bogoviz, A. V. (2019). Industry 4.0 as a new vector of growth and development of knowledge economy. Studies in Systems, Decision and Control, 169, 85–91. International Telecommunication Union. (2019). Global cybersecurity index. Geneva, Switzerland: Author. Retrieved from https://www.itu.int/dms_pub/itud/opb/str/d-str-gci.01-2017-pdf-e.pdf Kshetri, N. (2013). Cybercrime and cyber-security issues associated with China: Some economic and institutional considerations. Electronic Commerce Research, 13(1), 41–69.

System of Provision of Cyber Security Among the Economic Factors of AI    459 LeClair, J., & Pheils, D. (2014). Are we prepared: Issues relating to cyber security economics. ASEE Annual Conference and Exposition, Conference Proceedings, 2(1), 18–26. Lerums, J. E., & Dietz, J. E. (2018). The economics of critical infrastructure controls systems’ cyber security. 2018 IEEE International Symposium on Technologies for Homeland Security, HST 2018, 8574159. Popkova, E. G. (2019). Preconditions of formation and development of industry 4.0 in the conditions of knowledge economy. Studies in Systems, Decision, and Control, 169, 65–72. Popkova, E. G., Ragulina, Y. V., & Bogoviz, A. V. (2019). Fundamental differences of transition to industry 4.0 from previous industrial revolutions. Studies in Systems, Decision, and Control, 169, 21–29. Popkova, E. G., & Sergi, B. S. (2019). Will Industry 4.0 and other innovations impact Russia’s development? Exploring the future of Russia’s economy and markets. Bingley, England: Emerald. Singh, N. (2018, September 5). Which countries are ready for AI adoption? Retrieved from https://www.entrepreneur.com/article/319555 Srinivas, J., Das, A., & Kumar, N. (2019). Government regulations in cyber security: Framework, standards, and recommendations. Future Generation Computer Systems, 92, 178–188. Sukhodolov, A. P., Popkova, E. G., & Litvinova, T. N. (2018). Models of modern information economy: Conceptual contradictions and practical examples. Bingley, England: Emerald. Trifonov, R., Manolov, S., Yoshinov, R., Tsochev, G., & Pavlova, G. (2017). An adequate response to new cyber security challenges through artificial intelligence methods. Applications in business and economics. WSEAS Transactions on Business and Economics, 14, 263–271. World Economic Forum. (2016). The global information technology report 2016. Retrieved from https://www.weforum.org/reports/the-global-information-technology -report-2016 Yan, J., Bo, R., & Ni, M. (2014). An economic-based cyber-security framework for identifying critical assets. IEEE Power and Energy Society General Meeting, October, 6939921. Zorin, O. A., Grinavtseva, E. V., Bogomolova, E. V., Morozova, I., & Litvinova, T. N. (2016). Contradiction of clustering: Cluster as a necessary condition and a threat to economic security. International Journal of Economic Policy in Emerging Economies, 9(1), 89–99.

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CHAPTER 52

DIGITALIZATION AS A FACTOR OF DEVELOPMENT OF RUSSIA’S BANKING SYSTEM Alim B. Fiapshev Financial University under the Government of the Russian Federation Oksana N. Afanasyeva Financial University under the Government of the Russian Federation

ABSTRACT Digitalization is a cornerstone of one of the priority national projects implemented in the Russian Federation, the purpose of which is to introduce digital technologies not only in business processes but also in many other spheres of social life. The financial market and its banking segment take a proactive role in this direction and have significantly advanced in the use of these innovations in their daily practice. The aim of the study was to assess the possibility of making digitalization a significant factor in the development of the

Meta-Scientific Study of Artificial Intelligence, pages 461–468 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

461

462    A. B. FIAPSHEV and O. N. AFANASYEVA banking system and the entire Russian economy. To achieve this goal, we used the methods of interviewing representatives of the banking community to determine their vision of the most important factors in the development of the Russian banking system. Processing the results of the survey and subsequent modeling based on weights of the selected factors revealed the limitations of the opportunities to carry out digitalization in banking sector aimed to achieve the goals of the national economy in terms of the importance of other factors, in particular the structural and institutional factor, as well as external factors relating to sanctions and pressure on the Russian economy.

Industrial digital transformation, which has affected almost all sectors of the economy and spheres of public life today, determines the capacity of countries to stimulate economic dynamics and solve a broad spectrum of social problems. The results of numerous studies confirming the dependence of economic growth on investment during this transformational period, as well as the experience of countries outstripping others in implementing appropriate changes, serve as a basis for government decision-making with the aim of active promotion of digital technologies in day-to-day economic practice. The decisions adopted in the Russian Federation in this area clearly demonstrate the desire of its leadership not to be on the periphery of these changes. It should be recognized that not only individual sectors of the domestic economy, but also spheres of public life, have made significant progress in the use of digital technologies, strengthening, on the one hand, their competitive position, and, on the other, increasing the volume and quality of services provided. This can be fully applied to the financial sector and, taking into account its distinct specialization, to the banking system. According to all current estimates, this sector is formed on the basis of extensive empirical studies on most countries of the world which are leaders in terms of the digital transformation index. The leading position of the banking system in this process is typical for the Russian reality. Digitalization is considered an important factor in optimizing business processes in the main directions of development of the Russian financial market until 2021 implemented by the Bank of Russia. Great importance is attached to innovations, which allow through the use of artificial intelligence to resolve issues associated with the remote identification of customers of financial-credit organizations, extending choice of services for them on the basis of aggregated Internet platforms for financial products sale, quick payments, and so on. An important role in the promotion of these innovations is given to the institutions of the banking system. METHODOLOGY Studies of the problems with the banking system and the factors of its development, despite their large number, theoretical, and mainly practical

Digitalization as a Factor of Development of Russia’s Banking System    463

value, often underestimate the importance of the interconnectedness of these factors, focused mainly on the internal conditions and their impact on financial results. Among the first works, which contained a comparative analysis of the functioning of the banks, the factors affecting this process, should be identified (Berger & Humphrey, 1991). Such studies were intensified at first in the United States (Berger, Hancock, & Humphrey 1993; Berger, Leusner, & Mingo 1997), and later in European countries, Japan (Altunbaş, Liu, Molyneux, & Seth, 2000), and China (Berger, Hasan, & Zhou, 2009). The analysis of the factors of development in European banks and the conceptual grounds to possibly increase the efficiency of their activities on this basis are contained in the works (Dietsch & Lozano-Vivas, 2000; Chaffai, Dietsch, Lozano-Vivas, 2001; Lozano-Vivas & Pastor, 2010; Lozano-Vivas, Pastor, & Hasan, 2001, Lozano-Vivas, Pastor, & Pastor, 2002). Russian authors were not aloof from the process of popularizing this research area. A number of works are devoted to the analysis of the factors accompanying the development of Russian banks, their infrastructure, their influence on the results of their activities (Afanasyeva, 2016; Belousova, 2011; Belousova & Cozyr, 2016; Fiapshev, 2018; Mamonov, 2011; Mamonov & Vernikov, 2015; Pavlyuk, 2006; Ponomarenko & Sinyakov, 2018; Styrin, 2005; Ushakova & Kruglova, 2018). Inflation, exchange rate, GDP per capita, demand density, level of financial intermediation, indices of industrial production and output by basic types of economic activity, the number of banks per 1,000 people, the activity of the regulator’s supervisory policy, were considered as factors in the development of Russian banks. differences in the level of development of Russian regions, etc. Most of these factors turned out to be significant and had a positive effect on the banking business. While not disputing the relevance of the results of the investigation of factors affecting the efficiency of the banking system, we note that the attention to this problem stemmed from the importance of both internal and external conditions for improving the effectiveness of credit institutions in the interests of the development of the entire national economy. The greatest priority among these conditions is the speed of the deployment of advanced technologies in the banking business. A significant number of works of both foreign and domestic researchers are also devoted to the evidence that illustrates the correlation between the financial results of the banking sector and the new technology penetration rates. For example, research in the use of blockchain technology in the financial sector is contained in several works (Baklaeva, 2017). Today, this connection is very visible. At the same time, this raises a new question as to the extent to which the trend of accelerating the digitalization of business can contribute to the solution of macroeconomic problems that have increased in severity due to the external circumstances in relation to the national economy and thus adding uncertainty to the economic prospects. All of the above determines the relevance of this study.

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One of the main features of the developmental research challenges of the modern Russian banking system is the identification of its vulnerability, despite the positive results that have been achieved recently by the Central Bank in the regulatory process of supervisory practice. In this regard, the opinions of researchers–practitioners are of great importance, as it is they who can to some extent serve as a reflection of the objective processes occurring in the banking system. RESULTS The analysis of the changes in the banking system made it possible to identify the following features: optimization of management with the active use of new technologies, against the background of centralization and concentration of the industry, nationalization, lack of security and diversification, and disproportionate development of its constituent elements. Factors which have the potential to influence the development of the banking system were grouped according to the criterion of belonging to a particular level of the economic system: macro-level factors (economic, political, legal, natural, technological, sociocultural); meso-level factors (the situation in the banking sector, regulatory and legislative framework, the state of the business environment). Aggregated factors were subsequently detailed. As a result, the respondents assessed the impact of 24 factors. The influence of each factor was evaluated according to the scoring system. The results revealed a growing skepticism in assessing the prospects for the development of the banking system. This was reflected in the values of factors beyond the control of the monetary authorities, such as the state of the markets, mainly the financial market, which is increasingly influenced by the growing outflow of capital from the country and the withdrawal of foreign investors from it. The same can be attributed to the current level of risks in the industry, which is the result of the general risks of doing business in Russia, as well as to the presence of significant institutional barriers to the development of the banking system and, accordingly, the entire national economy. The next stage of the analysis was the construction of a factor model. With the help of the calculated shares of influence, a weighted factor model was designed, having the form: B = f (w1 , w 2 ,, w 24 ), where B is the current state of the banking system; w1, w2 is the weight (share of influence) of a particular factor. The current status was expressed as the total assets of the system. The identification of a specific functional assessment of influence of each factor (or set of factors) on the banking system as a whole is a timeconsuming task, since the “function of influence” f is essentially a “black box.” However, it is possible to conduct a study of the variables included in

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this function, that is, the share of the influence of specific factors. To this end, the factors were consolidated into groups, which facilitated the calculation of the average weighted share of influence per factor, as well as the total share of influence of groups of factors at different levels. The modeling results indicated that the largest total share of influence is referred to as a group of meso-level factors, namely the factors of the subgroup, “The situation in the banking sector.” To test the obtained factor model for sensitivity (stability) to changes in the composition of input variables (factors), a new classification of factors was constructed with ranking the groups by the degree of influence. The degree of influence is directly correlated with the number of factors having this degree. The correlation coefficient amounted to 90.48%. The sensitivity (stability) of the model to changes in the number of input variables (factors) was analyzed using the dependence of the share of influence on the number of factors. In other words, it is traced whether the exclusion of one of the factors from the model leads to qualitative changes in the average share of the influence of other factors (per one factor). It showed that the change in the average share of influence does not have a strong impact on the banking system as a whole. The average change was 0.16%. But at the same time, most of the excluded factors change the average share of influence on the value above the average. There are 14 factors of this type. The exclusion of any factor influenced the change in the group share of the action (i.e., the change in the total share of the influence of the subgroup). To confirm this assumption, a dispersion analysis of the group fraction of influence was carried out, which gave the following results: The average of the group variances was σ 2 = 0.000128 , the intergroup variance was δ2 = 0.00024 . The obtained table of values of correlation relations showed a close relationship between the variation of the share of influence of one factor and the group phenomenon. That is, in view of the active intergroup interactions, the change in the influence of one factor belonging to any group plays a role in changing the influence of other group factors on the banking system as a whole. The constructed factor model confirmed that the banking system is a complex integral entity, the development factors of which are closely linked. But at the same time, the significance of the influence of the elimination of factors beyond the control of the monetary authorities was revealed. This includes the macro-environment factors that are associated with external conditions (the likely tightening of the sanctions regime with all the consequences which this necessarily entails) the outflow of capital, the volatility of the national currency, restrictions on access to foreign sources of funding, and so on. The exclusion of this factor essentially means that the Central Bank gains more flexibility in its influence on credit institutions, as well as an increase in the importance of

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the factors it controls. The role of the technological component, assigned to different groups of factors, was also highly appreciated. CONCLUSIONS The degree of connectivity of the factors of development of the Russian banking system, as well as their obvious conditionality by the external environment accompanying the current stage of functioning of the Russian economy, complicate the task of implementation by the domestic regulator of its main function, which consists in increasing and productive realization of its credit and investment potential in the interests of the development of the entire national economy. The preservation of the negative external political and economic background accompanying the development of the Russian economy will inevitably reduce the effectiveness of the stabilization and incentive decisions of the monetary authorities. This was partly confirmed by the inflationary impulses received by the Russian economy in the first half of 2018, which resulted in the adequate reflection of the interest rate policy of the domestic regulator, which makes the prospects for the subsequent transition to the easing of the monetary rate uncertain. An important result of this stage of development of the national economy and its banking sector was the demonstration of limited internal opportunities to achieve the declared goals in terms of socioeconomic dynamics. This limitation can also be seen in the possibility of implementing the main directions of development of the financial market, the most important of which is the active interaction of the banking system with other sectors of the economy on the basis of the progressive expansion of the use of advanced technologies. These and other decisions, taken within the framework of regulatory and supervisory practices (while recognizing their importance and timeliness), clearly continue to “work” for the interests of the largest market participants. In fact, they make the banking system become more inward-looking, where the need to ensure the financial stability of credit institutions prevails over other macroeconomic objectives. And all this is taking place against the background of general institutional disorder that represents a disincentive for business and institutional activity. This does not allow us to qualify the technological factor as self-sufficient to solve the set of economic and social problems of the Russian reality. In this regard, the coherence in the directions of state economic policy and its linkage with institutional changes, long overdue structural perturbations have gained even greater urgency. The set of solutions in these important functional areas should be clearly complemented by active steps to progressively eliminate external factors and overcome domestic economy isolationism. The implementation of the comprehensive policy will make it possible to eliminate

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the fragmentary efforts of regulatory bodies that are taking place today and to fully use the potential of technological innovations in the interests not only of the banking system, but also of the entire Russian economy. The main result of the reduction of costs and access terms to banking services, optimization of the size and structure of bank resources, the use of artificial intelligence and in general digital technologies contributes to the steady growth of financial results of credit institutions, strengthening their competitive positions in the international financial market. It is also associated with an increase in the availability of financial and, in particular, banking services for a significant number of Russian citizens. But the question is whether small- and medium-sized banks in terms of the current realities of the regulatory process and supervisory practices implemented by Russian banks will be able to meet the challenges posed by the era of digitalization and the expansion of the use of artificial intelligence. It should be recognized that while these processes are important for the development of the entire national economy, the immediate prospects are potentially associated with deepening structural imbalances in the banking system. As already shown by the analysis of empirical data, its productive potential, stabilization, and stimulating policy opportunities of the Bank of Russia will be limited in this perspective by the negative impact of a number of internal and external factors. The main ones are produced by the continuing impact of sanctions on the Russian economy, the state of the institutional environment, the structural features of the Russian economy, which will reduce the positive impact of this global trend on macroeconomic dynamics and the possibility of solving social problems. REFERENCES Afanasyeva, O. N. (2016). Method of banking system stability determination. Banking Business, 1, 11–16. Altunbaş, Y., Liu, M. H., Molyneux, P., & Seth, R. (2000). Efficiency and risk in Japanese banking. Journal of Banking & Finance, 24(10), 1605–1628. Baklaeva, N. M. (2017). Blockchain technologies in the practice of inter-budget relations. Economics and Business: Theory and Practice, 8, 13–18. Belousova, V. Yu., & Kozyr, I. O. (2016). How macroeconomic variables affect the profitability of Russian banks. Journal of the New Economic Association, 2(30), 77–103. Berger, A. N., Hancock, D., & Humphrey, D. B. (1993). Bank efficiency derived from the profit function. Journal of Banking & Finance, 17(2), 317–347. Berger, A. N., Hasan, I., & Zhou, M. (2009). Bank ownership and efficiency in China: What will happen in the world’s largest nation? Journal of Banking & Finance, 33(1), 113–130.

468    A. B. FIAPSHEV and O. N. AFANASYEVA Berger, A., & Humphrey, D. (1991). The dominance of inefficiencies over scale and product mix economies in banking. Journal of Monetary Economics, 28(1), 117–148. Berger, A. N., Leusner, J. H., & Mingo, J. J. (1997). The efficiency of bank branches. Journal of Monetary Economics, 40(1), 141–162. Chaffai, M. E., Dietsch, M., & Lozano-Vivas A. (2001). Technological and environmental differences in the European banking industries. Journal of Financial Services Research, 19(2–3), 147–162. Dietsch, M., & Lozano-Vivas, A. (2000). How the environment determines banking efficiency: Comparison between French and Spanish industries. Journal of Banking & Finance, 24(6), 985–1004. Fiapshev, A. B. (2018). Structural and territorial features of the banking system as a result and a factor of asymmetry in the socio-economic development of Russian regions. Modern science: Actual problems of theory and practice, 8, 78–84. Lozano-Vivas, A., & Pastor, J. T. (2010). Do performance and environmental conditions act as barriers for cross-border banking in Europe? Omega, 38(5), 275–282. Lozano-Vivas, A., Pastor, J. T., & Hasan, I. (2001). European bank performance beyond country borders: What really matters? European Finance Review, 5(1– 2), 141–165. Lozano-Vivas, A., Pastor, J. T., & Pastor, J. M. (2002). An efficiency comparison of European banking systems operating under different environmental conditions. Journal of Productivity Analysis, 18(1), 59–77. Mamonov, M. E. (2011). Impact of the crisis on the profitability of the Russian banking sector. Banking, 12, 15–26. Mamonov, M. E, & Vernikov, A. V. (2015). Bank ownership and cost efficiency in Russia, revisited (Working papers by Bank of Finland Institute for Economies in Transition). Retrieved from https://publications.hse.ru/en/preprints/151485013 Pavlyuk, D. V. (2006). Efficiency model of Russian banks. Applied Econometrics, 3, 3–8. Ponomarenko, A., & Sinyakov, A. (2018). Impact of strengthening banking supervision on the structure of the banking system: Conclusions based on agentbased modeling. Money and credit, 1, 26–50. Styrin, K. (2005). What explains differences in efficiency across Russian banks? Moscow, Russia: EERC. Retrieved from https://econpapers.repec.org/paper/ eerwpalle/01-258e-1.htm Ushakova, Yu., & Kruglova, A. (2018). Competition in the Russian banking sector before and after the intensification of the supervisory policy: Conclusions based on the variation and spread of interest rates. Money and Credit, 2, 22–50.

CHAPTER 53

A PARADIGM SHIFT IN BUSINESS MANAGEMENT IN THE CONTEXT OF INDUSTRY 4.0 Kirill A. Gorelikov MGIMO University Aleksey V. Komarov Moscow Aviation Institute (National Research University) Ekaterina R. Bezsmertnaya Financial University

ABSTRACT This chapter discusses the problems associated with the emergence of Industry 4.0. The main stages of the evolution of the industrial revolution, the use of cyber-physical systems in the production process are analyzed. The need for transition to a new business model in the conditions of Industry 4.0 is identified and justified. On the basis of the study, the authors propose to consider the process of virtual management of cyber-physical systems in the

Meta-Scientific Study of Artificial Intelligence, pages 469–476 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

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470    K. A. GORELIKOV, A. V. KOMAROV, and E. R. BEZSMERTNAYA transition to Industry 4.0, give its definition, and formulate the main characteristics of the business model using cloud storage, components of the system of end-to-end transparency, and interaction between people and machines. The main purpose of the chapter is to investigate the impact of Industry 4.0 on the effectiveness of the company’s financial management in accordance with the fundamental changes in the industries. This chapter is a response to the new evolutionary and revolutionary changes taking place in industrial production. The chapter analyzes the world experience in the use of Industry 4.0 technologies and the possibility of its application by Russian companies within the global value chains.

The term Industry 4.0 is associated with the birth of the fourth industrial revolution, which will be accompanied by changes in tasks for machines, technologies, processes, and labor. The concept of Industry 4.0 is used in the real sector of the leading OECD countries. This concept offers considerable opportunities for Russia as a traditional industrial country. The fourth industrial revolution is a response to changing business conditions (Gorelikov, 2009). It is necessary to respond effectively to the everchanging demand of its customers so that the companies can be successful in the market. For this reason, it is imperative that the technology and technological equipment for the production of their goods be improved. We can say that different technologies are starting to move closer to each other. These are telecommunications, automation, and computer technologies. With the convergence of technologies, a virtual world will be created. After that, the virtual world will be reflected in the physical world. However, if changes occur only on the supply side, they will also change the demand, causing the so-called individualized mass production, that is, a positive relationship between the latter and other industries. At the same time, enterprises will need to respond to changing market conditions, technological changes, and innovations so that they can meet the needs of their customers while continuing to look for savings through the value engineering. If the company does not accept these market conditions, it may face financial difficulties and financial problems. METHODOLOGY In order to make the processes of industrialization smoother, so-called digital twins will be created in mass production. Digital twins are images of the products that the manufacturing company will produce in the future. The image of future products will be interconnected with the real product, so it will influence the emergence of cyber-physical systems. It will be necessary to collect and store data as a result of the growth in scale of production. As can be seen from the above thesis, the amount of data will be significantly

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increased. This big data needs to be analyzed very carefully (Abbott, 2014). From this big data, the company obtains the data needed to ensure an efficient production process. Gradually, a situation will arise in which many workers specializing in manual labor will be replaced by robotic production (Brynjolfsson & McAfee, 2014), which will lead to the structural unemployment. These evolutionary and revolutionary changes will begin first in the primary sector of the economy, and then they will be followed by transport, energy, and then other sectors of the economy, which will certainly affect the efficiency and competitiveness of enterprises and the national economy as a whole (Bauernhansl, 2013). For this reason, research and analysis of Industry 4.0 problems are the priorities of the national projects of the government and the president of the Russian Federation. In this regard, it is also worth noting the Order of July 28, 2017 No. 1632—the program defines the goals, objectives, directions, and terms of implementation of the main measures of state policy to create the necessary conditions for the development of the digital economy in Russia, in which data in digital form is a key factor in production in all areas of socio-economic activity. RESULTS Financial management as a field of research has begun to develop since the beginning of the 20th century. The evolution of financial management is divided into three main stages (the traditional stage, the transition stage, and the modern stage). The traditional stage took place from the beginning of the 20th century to the end of the 1930s—during this period financial management was focused primarily on the financing instruments, institutions, and procedures used in the capital markets, as well as on the legal aspects of financial events. These elements formed the basis of financial management as an economic science. The transition stage took place from about 1940 to 1950 and focused on the day-to-day problems faced by financial managers—planning, controlling, and managing working capital. The modern stage (started in 1955) is based on the application of quantitative methods of analysis, valuation models, dividend policy, financial modeling, behavioral finance, and so on. The development of financial management was influenced mainly by the second industrial revolution (mass electricity production based on the division of labor), and the third industrial revolution (development of IT and automation of production). Companies had to adapt to these radical changes if they wanted to remain competitive. The same situation is developing during the fourth industrial revolution, where only the enterprises that will be able to keep pace with the changes and adapt their

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technologies in a way that they can effectively respond to customers’ needs will be successful. The second industrial revolution (according to most historians it began around 1870, in the second half of the “long 19th century”) was characterized by three factors: higher levels of automation through the development of mass production; more efficient linkages in production through the division of labor; and further progress in the use of energy sources such as electricity and oil. Standardization has been a key factor in these achievements (Gilmore & Pine, 1996), including r quality standards (e.g., within trade blocs) and transport systems (e.g., transport container). Legal and trade protection were also needed to ensure that innovators could benefit from their creativity without being emulated by competitors. The global technology boom characterized the third industrial revolution. In 1969, the first message in the ARPANET system was sent—the ancestor of today’s Internet. In addition, the possibilities for automation have been greatly enhanced by the application of Moore’s Law—an empirical observation that the number of transistors on an integrated circuit doubles approximately every 2 years. The widespread automation in combination with the complex changes in the agricultural sector had led to the emergence of the so-called green revolution, as a result of which the significant progress was achieved in the production of agricultural products. The fourth industrial revolution is driven by extreme automation and connectivity. Extreme automation will, as a first step, expand the range of jobs that can be automated, including not only repetitive functions within low-skilled jobs, but also cover routine, middle-skilled jobs. The relative impact of this extreme automation on income inequality between low-skilled and high-skilled workers seems likely to increase. We expect artificial intelligence (AI) to be a pervasive feature of the fourth industrial revolution. Extreme automation through the use of AI will increasingly automate some of the skills that previously only humans possessed. In comparison with the capabilities of computers, AI demonstrates the significant progress in handling large amounts of data, as well as in the recognition of language and images. Extreme automation could allow using more robots and AI in the production of goods, analysis of results, making difficult decisions, and research of the impact of environmental factors. Extreme connectivity provides more universal, global, and instantaneous connectivity. It generates new business models and provides opportunities for economic supply in ways that were previously impossible. For example, the creation of Uber, a taxi application for smartphones, was only possible thanks to the wide dissemination of portable Internet-enabled devices. Supply has created its own demand. Services such as Facebook, WhatsApp,

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Pinterest, Snapchat, Twitter, and Instagram have had a key role in the social interaction among citizens around the world. The principle of the fourth industrial revolution is based on radical changes in the concept of industrial production, which will take place, for example, in smart factories. Materials, work-in-progress, semi-finished products, finished products, and production machines will exchange information with each other, and all processes will be decentralized, and this will affect the speed and efficiency of the entire production process. Smart production processes and all technology are the key components of smart factories. The next key component of the Industry 4.0 phenomenon is the Internet of things (Ashton, 2009). All components in smart factories will collect data (big data), which in turn will be analyzed using computer technology; the company will get better information about the needs, behaviors, and requirements of its customers, and at the same time, it will have information about the properties of the products. Raw materials, components, and products will get their own identity in smart factories (they can be interconnected, simulated, and interact with each other). Cyber-physical systems will be virtually launched and become available to all authorized personnel or equipment (Monostori, 2014). Cyber-physical systems comprise three basic levels: • services based on available data, • data models of physical objects in the network infrastructure, and • physical object (see Figure 53.1). In the production process, the production company will have the initial operating algorithms by which the product can be produced. These algorithms will be owned by the company or purchased as a service from an external provider; however, it will be possible to produce products based on data stored in the cloud. All the documentation of products, 3D models, processed data, and so on, will be stored there. In the end, there will be the product that will meet the requirements and wishes of customers. In the future, it is expected that customers will be able to produce the final product on their own: the first step is to buy algorithms from an external supplier, then use the stored data, which will contain all the parameters and methods of manufacturing the product; then the customer prints the final product through a 3D printer as the last step.

Services

Cloud storage

Figure 53.1  Cyber-physical systems in industry 4.0.

Physical object

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End-to-end transparency is above the production process, facilitating the decision-making process. Industry 4.0 will also lead to new ways of creating value and new business models in the economy. A comparison of the traditional business model and the new business model in line with Industry 4.0 is shown in Figure 53.2. The new business model is based on virtual copies of providers’ processes. These processes will be linked to virtual copies of the processes in the production company. The most important channel will be the virtual sales channel, which will be associated with a virtual copy of the production company process. The products will be produced in smart factories. In connection with the creation of new business models, it will be necessary to adapt to these models in the company in accordance with changes in the production system. The traditional view of production will be modified; production will become more efficient through the mass use of robotic devices and will quickly respond to the changing demand of customers. In the future, however, enterprises will need to change their approach to financial reporting through the use of common predictive analysis, predictive modeling, and normative modeling. General predictive analytics generates information about the causes of the situation. Predictive modeling will effectively forecast the variants of future development. Prescriptive modeling is based on the best possible information. All of these methods will have to be used in conjunction with big data, from which they will choose only the relevant information necessary to make management and financial decisions of the company.

Suppliers • virtual copy Manufacturers

Distributors • virtual sales channels Sales

Clientele

Figure 53.2  Comparison of traditional business model and new business model.

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CONCLUSIONS New business models radically change the way of looking at the production and sale of products and, consequently, services. First, it will be changes in industrial enterprises, and then changes in the service sector and, consequently, in society as a whole. As production changes, the structure of production factors will also change. Companies will have to purchase new technologies (SMART technologies), which will require significant initial investment. Without this investment, they will not be able to communicate with their suppliers or buyers in the future. Human work will be largely replaced by robotic work. Companies will need particularly highly qualified staff with the knowledge in the field of IT. This task is set by the president of the Russian Federation in the decree “on national goals and strategic objectives of the Russian Federation for the period up to 2024.” A significant number of jobs will be replaced by robots at the production, low and medium levels of management. However, companies will have to adapt to these changes, including in the aspect of reporting—most of the reports will be prepared by smart machines, and the human will monitor and analyze the relevant information. In this regard, we have to answer the following questions: • Who will be responsible for reporting? • Who will be the primary owner of the data? • What will the primary data stream look like? The process of digitalization will have a decisive impact on the forms and methods of financial management, and this, in turn, will require new ways of reporting on economic transactions. Therefore, new methods of control will be developed, which will use new methods of obtaining and processing information from big data. All of these changes will be of a multidisciplinary nature, such as financial management with the help of information technology, financial and managerial accounting with software and hardware. Thus, the digitization of management processes will have a significant impact on all business entities. And if large companies will be able to find financial resources to ensure their competitiveness in the market in the context of the development of artificial intelligence, small companies will face a shortage of investment resources necessary for the digitization of their production. In the context of the fourth industrial revolution, business structures will need digital accounting, which would contribute to the effective management of those processes that support the digitalization of enterprises.

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REFERENCES Abbott, D. (2014). Applied predictive analytics: Principles and techniques for the professional data analyst. New York, NY: Wiley. Ashton, K. (2009). That “Internet of things” thing. RFiD J, 22, 97–114. Bauernhansl, T. (2013). Industry 4.0: Challenges and limitations in the production. Keynote. ATKearney Fact. Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. New York, NY: W.W. Norton & Company. Gilmore, J. H., & Pine, B. J. (1996). The four faces of mass customization. Harvard Business Review, 75, 91–101. Gorelikov, K. A. (2009). Fundamentals of cyclical development of modern economy. Moscow, Russia: INION RAN.

CHAPTER 54

REMOTE BANKING IN MODERN RUSSIA IN THE CONDITIONS OF ARTIFICIAL INTELLIGENCE DEVELOPMENT Nelli V. Tskhadadze Financial University Aza D. Ioseliani Financial University

ABSTRACT The purpose of this research is the study of systems of remote banking services, the stages of their development, the various features of the activities and associated risks, as well as providing suggestions for improving the system of remote banking service in Russia. Theoretical features of remote banking service and its structural elements are considered; the comparative characteristic of providing similar services in Russia and abroad is given; the technological and functional features of communication channels of remote ser-

Meta-Scientific Study of Artificial Intelligence, pages 477–487 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

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478    N. V. TSKHADADZE and A. D. IOSELIANI vicing in banks are studied; the range of remote banking services in Russia is studied; the analysis of the effectiveness and risks of using remote banking technologies in Russian banks is given; and the ways of improving the provision of remote banking technologies in the Russian market are proposed in the chapter. The development and implementation of any form of remote customer service systems is quite an expensive process for the bank and is associated with numerous risks, on the part of both customers and the banks themselves. In order to reduce costs and increase the speed of services, the authors identified the fundamental trends in the development of remote banking.

One of the characteristic features of the modern development of artificial intelligence (AI) and the future digitalization of the economy is the rapid and widespread development of remote technologies that are increasingly penetrating into all spheres of social and economic life. Banks and finance are developing in line with the overall change. Today, in the conditions of fierce competition and constant innovative development of many areas of our lives, the banking industry has not remained unchanged. Leading banks strive to continuously improve the quality of their services while reducing the cost of their services. The purpose of this research is the study of systems of remote banking services, the stages of their development, the various features of the activities and associated risks, as well as providing suggestions for improving the system of remote banking services in Russia. METHODOLOGY Achieving this goal is carried out by • consideration of the theoretical features of remote banking and its structural elements; • comparative characteristics of the provision of such services in Russia and abroad; • research of technological and functional features of remote service channels in banks: by telephone, via the Internet, and special selfservice devices; • consideration of the range of remote banking services in modern Russia; • analysis of efficiency and risks of using remote technologies in Russian banks; and • recommendations to improve the provision of remote banking technologies in the Russian market.

Remote Banking in Modern Russia in the Conditions of AI Development    479

RESULTS Remote banking—a set of specific services through which the bank’s customers can remotely perform different banking operations. To do this, they could use their computer or phone, to visit the branch of the bank that is no longer required. Remote technologies provide the client with maximum convenience in using banking services and minimum time and financial costs at all stages of work with the bank. Obtaining information about banking services and products, filling in the necessary forms and making the appropriate requests, as well as receiving and monitoring the banking product, all this is automated at a high speed, quickly and efficiently provided, and saves a sufficient amount of individual’s time resources and legal entities. The main vector of development of all systems of remote banking services is the creation of a set of certain services through which the bank’s customers can remotely perform a variety of banking operations. It was formed due to the rapid development of information technology and globalization of markets, which gave rise to consumers’ high standards of comfort with various services, even in banking. The basic principle of all remote banking systems is the exchange of various information between the customer and the bank at a distance. At the same time, the bank ensures a proper level of security and confidentiality of such communication because, today, the bank’s client can not only receive information about their accounts remotely, but also manage them and perform various operations. In the client’s arsenal, there are such opportunities as remote access to accounts, payments, and transfers, as well as the opening of deposits and a large number of information materials, such as exchange rates or the location of the nearest ATMs. The first criterion for the difference between the systems of remote banking services is called service delivery channels. There are the following types of channels for providing remote service in banks: by telephone, via the Internet, and special self-service devices (Yudenkov, Tysyachnikova, Sandalov, & Ermakov, 2015). Banking Via Telephone Before all of this, there was a telephone connection, and before the development of modern technologies of the Internet, it was a very common channel. Customers could get any information on the accounts, as well as advice on products, it was enough to call the bank. Such a mechanism has remained today, although it was much reduced (Tskhadadze, 2017b).

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Payment and Reference Terminals and ATMs Specialists identify self-service mechanisms as another channel of remote banking, such as payment and reference terminals, ATMs, and others (Regulations of the Central Bank of the Russian Federation of April 24, 2008 No. 318-P). This type of remote banking services required high investments from the bank at the initial stages of development (Lyamin, 2015). Internet-Banking One of the most promising and rapidly developing channels of banking services today is the Internet. The peculiarity of this channel is the fact that the interaction of the bank with the client even on the Internet is strictly regulated by the standards of the Bank of Russia. Among other things, there are clear provisions for the design of bank sites that provide their services through them (Letter of the Bank of Russia of October 23, 2009 No.128-T): • The full company name of the credit institution included in the Unified State Register of Legal Entities (USRLE) should be presented on the website page. • The registration number, which the Bank of Russia has assigned to this credit organization; abbreviated name in Russian, and so on, must be placed. The Bank of Russia also monitors the security of remote banking services, so all customer transactions are subject to various encryption methods (Letters of the Bank of Russia of October 26, 2010 No.141-T). Client-Bank The possibility of remote banking services via a personal computer is called client-bank. The bank often provides its customers with support in the installation and use of such systems, which increases the level of service from the bank. This system is divided into two types: • Classic bank client, also called thick client, is a special program that is installed on the personal computer of the bank’s client. With the help of it, all customer’s data is stored on the computer: payment orders, account statements, and so on. • Thin client or Internet client—interaction with the bank directly on the Internet through a browser. In this case, all information about the user’s actions is stored on the bank’s servers.

Remote Banking in Modern Russia in the Conditions of AI Development    481

For the smooth and effective operation of the remote banking system, it is necessary to attract highly qualified specialists. This list includes financial specialists, information and security experts, lawyers, and many other professions (Letters of the Bank of Russia of April 27, 2007 No. 60-T). For the first time, remote banking appeared in the United States of America, which is not surprising, as the banking system of this country is one of the oldest and today is the largest and most reliable in the world. Another reason for the emergence and development of Internet banking in America is the restriction on opening branches of their banks that existed in the mid-1990s. Thus, in 1995 the first bank providing its services by means of the Internet, Security First Network Bank, was opened. In the modern world, the United States is recognized as a leader in remote banking. At the moment, almost 90% of American banks provide services to their customers remotely. They offer a fairly diverse range of services, which include currency exchange, loan processing, opening of deposits, the possibility of its insurance, and control over transferring money out of the accounts, as well as participation in trading on stock exchanges. The main feature of the functioning of remote banking systems in the United States is that the client gets most of these services for free, and only for some of them, the bank charges a small commission. Following America online banking has become widespread in Europe. Compared to the United States and Europe, the development of remote banking in Russia is lagging far behind. There are objective reasons for this; for example, we must not overlook the fact that these systems have begun to develop in our country relatively recently and have already shown good growth rates. Consequently, it may conclude that the Russian market has good potential. Analytical agency Markswebb Rank and Report recently presented the results of research that was then correlated with the total population among users of remote banking systems. The study was based on a survey of more than 3,000 Russians, which was then correlated with the total population of our country (MarksWebb Rank & Report Analytical Agency, 2017). The main findings of the survey are summarized below: • 41.6 million of our citizens make at least one active operation on the Internet per month. • 39.4 million are active users of mobile and SMS banking. • 35.4 million use Internet banking, of which 70% are Sberbank Online clients. • 29 million people pay online by credit card. • 17.5 million are owners of e-wallets (Bankir.ru—Information Agency, 2017).

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Not all indicators are given here, but the trend is clear. People in Russia want and are ready for further development of remote banking systems. They learn modern technologies, develop their economic literacy, and increase the degree of confidence in banks, which is very important in the implementation of any financial transactions at a distance (Internet Finance, 2017). According to the agency J’son & Partners Consulting (http://web.json .ru), the structure of payments by various remote banking services differs significantly. According to Figure 54.1, it can be concluded that the payment structures are completely different from each other. Thus, customers of banks choose different forms of remote service to perform various active operations. For example, Figure 54.1 shows that a mobile connection clients pay mostly via SMS banking (over 80%), followed by mobile banking. Internet banking takes a very small share in the performance of such operations. This is due to the convenience and ease of such transactions by means of SMS and mobile banking. According to the agency RusTelekom in the personal services segment, the volume of the federal market for Internet banking increased from 1.7 to 2.0 trillion roubles in 2016 compared to 2014, while the number of transactions decreased to 79.9 million. In the segment of legal entities, the market volume increased from 351.4 to 392.5 trillion rubles, the number of transactions increased to 1044.7 million in the same period. 10 9 8 7 6 5 4 3 2 1 0

SMS banking

Mobile banking

Mobile communication Remittance

Internet banking

Utilities and other obligatory payments Other payments

Figure 54.1  The structure of payments through the services of e-banking in Russia (compiled by the authors). Note: The data of the Analytical Agency J’son & Partners Consulting were used.

Remote Banking in Modern Russia in the Conditions of AI Development    483 1,000 Legal entities, min. Legal entities active, min. Natural persons, min. 157.1

136.1

100

80.9

Natural persons active, min. 63.5

45.4 30.7

10

3.1 3.0

3.8 3.7

4.4 4.3

1

2014

2015

2016

Figure 54.2  Volume of Internet banking subscriber base in Russia (compiled by the authors). Note: The data of the Analytical Agency J’son & Partners Consulting were used (http://web.json.ru; Bank RBS http://www.bankdbo).

The situation is quite different if we compare the volume of subscriber bases of legal entities and individuals—Internet banking clients (see Figure 54.2). According to the Central Bank of Russia and Rostelecom’s calculations, Figure 54.2 shows the number of customer accounts using Internet banking: legal entities have only 4.4 million, and individuals have 157.1 million users. The figures indicate the annual growth, which points to positive trends in the use of this service. The number of active users for each of the presented options is much less. Based on Figure 54.3, it can be concluded that the shares among large banks almost repeat the total volume occupied by a certain credit institution in the industry. Sberbank holds very high market shares, and all five major players take 75% of the volume in the B2C segment. It’s time to remember another reason why banks are so actively developing their remote service systems: almost all banks make good money on the commissions they charge for providing these services. The fact is that the development and implementation of any form of remote customer service systems is quite an expensive process for the bank (Letter No. 197-T, 2007). It requires time, people, and financial resources, which greatly increases the cost of implementation and maintenance of remote technologies for banks (Tskhadadze, 2017a).

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3.4 3.0 2.8

Sberbank online VTB 24 0.2 0.3

0.5

0.5

0.6 0.7

6.3

0.1 0.4

6.4

6.7

Alfa-bank Russian standard

0.5

Bank Home Credit 1.3

Other banks

1.3 1.4

Figure 54.3  Structure of the Russian market of Internet banking according to the key participants, %. Note: The data of the official website of the Central Bank of the Russian Federation were used (http://www.cbr.ru)

Monthly costs of users of remote banking services in the capital region were analyzed. Analysts took into account everything for which the bank can take money from them: subscription fees for connecting and servicing the Internet and mobile bank, fees for transfers and payments, access charges, and so on. All data were taken from payment forms in Internet and mobile banks, on bank websites, as well as in support services. To compare the losses, three typical profiles of an active user of remote banking services were identified: state employee with a monthly income of 45,000 rubles and remote banking operations—32,600 rubles (which included the average value of utility bills, consumer credit, various transfers, communications fees, Internet charges and other expenses); specialist income—90,000 rubles, the operation of remote banking services—61,500 rubles; the head income—150,000 rubles, the operation of remote banking services—86,900 rubles. Having studied the tariffs of various banks, the specialists made the necessary calculations and obtained the results indicated in Table 54.1. The analysts came to the conclusion that the cost of remote servicing for clients is influenced more by one-time fees for transfers to third-party banks by card number (ranging from 0.5% to 2%) and by account number (ranging from 0.1% to 1%), it is not charged at Tinkoff Bank, Citibank, and Credit Europe Bank.

Remote Banking in Modern Russia in the Conditions of AI Development    485 TABLE 54.1  The Cost of Providing Remote Banking Services for Clients of Russian Banks per Month, Rubles Client Category Total Amount of Transactions per Month, Rubles

Head

Specialist 86,900

61,500

State Employee 32,600

Bank  Tinkoff

0

0

0

 Citibank

300

225

225

  MTS Bank

450

315

220

  Bank of Moscow

624

360

240

  VTB 24

645

434

272

  Russian Post

673

460

315

 Alfa-bank

707

509

380

  Opening Bank

790

550

375

  UniCredit Bank

800

575

325

 Gazprombank

836

489

295

 Sberbank

865

605

345

Source: Compiled by the authors

According to their data, internal transfers are free in all banks, but some of them have restrictions. For example, Sberbank takes a commission for transfers to a card issued in another city. The most democratic bank is Tinkoff Bank, which does not take a commission from its customers for the analyzed types of services. But Sberbank was the most expensive bank among the entire list. Sberbank is among the banks with the largest amount of fees for transfers to other banks by account number (1%; http://www.sberbank.ru/). CONCLUSIONS Russia today lags behind the developed countries of the world, both in the range of banking products offered, and in the development of remote banking channels, but now the leading domestic banks have the opportunity to actively develop not only the generally recognized technologies of remote banking, but also to offer their innovative solutions. The fundamental trends in the development of remote banking services, aimed at reducing costs and increasing the speed of services, are: • application of multichannel service strategies for individuals; • transfer of remote customer service systems to outsourcing;

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• establishment of joint remote service systems by various banks; • improving the quality of services and their safety; and • providing integrated services due to the increase in accessible types of services. To sum up, we note that mobile banking in Russia is rapidly developing. According to the mobile Banking Rank survey in 2017, 18 million people aged 18–64 years were customers of this channel of remote banking services. The range of provided functions, the possibility of paying fines, and other mandatory payments, the creation of personal templates when performing the same operations are expanded. Call centers are replaced by online chats inserted into the interface of the mobile application to provide operational customer support. The applications themselves have become easier to use for clients’ convenience and time savings. REFERENCES Bank e-Banking. (n.d.). Electronic data. Retrieved from http://www.bankdbo Bankir.ru—Information Agency. (2017). Retrieved from http://bankir.ru iFin-2012—XII International Forum on Remote Financial Services and Technologies. (2015). Retrieved from http://forumifin.ru/ Internet Finance. (2017). Retrieved from http://ifin.ru/ Letter No. 197-T of the Bank of Russia of December 7, 2007. “On risks in remote banking services.” Retrieved from http://www.consultant.ru/document/ cons_doc_LAW_73368/ Letters of the Bank of Russia of April 27, 2007 No.60-T. “On the peculiarities of customer service by credit institutions using the technology of remote access to the customer’s Bank account (including Internet banking).” Retrieved from https://base.garant.ru/12153297/ Letter of the Bank of Russia of October 23, 2009 No.128-T. “On recommendations for information content and organization of web sites of credit institutions on the Internet.” Retrieved from http://www.consultant.ru/document/ cons_doc_LAW_93100/ Letters of the Bank of Russia of October 26, 2010 No.141-T. “On Recommendations on the approaches of credit institutions to the selection of providers and interaction with them in the implementation of remote banking services.” Retrieved from http://www.consultant.ru/document/cons_doc_LAW_106206/ Lyamin, L. V. (2015). Application of electronic banking technologies. The risk-based approach, a series of the Library of the Centre for Research of Payment Systems and Calculations. Moscow, Russia: KnoRus. MarksWebb Rank & Report Analytical Agency. (2017). Retrieved from http://marks webb.ru/ Publication on High Technologies. (2018). Retrieved from http://www.cnews.ru

Remote Banking in Modern Russia in the Conditions of AI Development    487 Regulations of the Central Bank of the Russian Federation of April 24, 2008 No. 318-P. “On the order of conducting cash transactions in credit institutions in the Russian Federation” (p. 2.8 “Organization of work with cash using ATMs, electronic cashiers, automatic safes and other software and hardware systems”). Retrieved from http://www.consultant.ru/document/ cons_doc_LAW_49832/eeb5679e3c5ccae487c71b3bcf35b0463a558df9/ Remote Banking Service. (2015). The series of the Library of the Centre for Research of Payment Systems and Calculations. Moscow, Russia: KnoRus-CIPSiR. The Position of the Central Bank of the Russian Federation Dated 03.10.2002 No. 2-P. “About clearing settlements in the Russian Federation.” Retrieved from http://www.cbr.ru Tskhadadze, N. V. (2017a). Concept and essence of bank risks. In Collection of scientific works “Economics and management: From theory to practice.” (pp. 21–25). Rostov-on-Don, Russia: Innovative Development Center Of Education And Science. Tskhadadze, N. V. (2017b). The theoretical aspects of marketing in the management system of the bank. In Collection of scientific works “Prospects of development of economics and management” (pp. 14–21). Chelyabinsk, Russia: Innovative Development Center Of Education And Science. Yudenkov, Yu. N., Tysyachnikova, N. A., Sandalov, I. V., & Ermakov, S. L. (2015). Internet technology in the banking business. Prospects and risks. Moscow, Russia: KnoRus.

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CHAPTER 55

THE TRANSFORMATION IN COLLECTIVE INVESTMENT UNDER THE INFLUENCE OF ARTIFICIAL INTELLIGENCE Yana N. Radzievskaya Financial University Yuriy Yu. Shvets Financial University Olga V. Karamova Financial University Evgeny V. Sumarokov Financial University Alexandra E. Sergeyeva Financial University

Meta-Scientific Study of Artificial Intelligence, pages 489–498 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

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ABSTRACT The actual data, potential, and prospects of the use of digital technologies in the management of the environment and the system of collective investment of the Russian Federation are investigated in this chapter. The legislative framework, the results, and the potential implementation of the system of modern digital financial technologies tools are analyzed. The chapter presents a retrospective comparative analysis of the results of the Russian systems of banking and collective investment over the past 10 years. The purpose of the study is to identify and substantiate the most promising areas of digital technologies in the management of collective investment in Russia. The study was conducted with help of statistical data of the Central Bank of the Russian Federation, Rosstat, and the Investment Funds Association. Numerical analysis methods and comparison methods of development trends were applied. The most promising directions for the development of collective investment management with the use of modern financial technologies at the macro level are identified.

Collective investment, as a component of the investment process, involves a wide range of opportunities for each investor. Current pace of development of computer technologies and mechanisms of artificial intelligence (AI) practically provides a wide range of instruments for the selection and attraction of potential customers that can be used by the management market institutions. Examples include the following services: personalization of the history of consumer interaction with a product or service (Customer Journey); scoring-programs that integrate data from social networks and perform biometric identification of clients; speech analytics systems, and a number of others. In the study of the processes and problems of the introduction of AI in modern Russian society, the authors focus on existing government programs in the field of digital economy (Sokolov et al., 2017; Petrov, 2018), their analysis and comparison with similar programs of the leading countries: the United Kingdom, the United States, Germany (Sokolov et al., 2017), and Finland (Zhilkin, 2018). In this regard, there is a need to determine the most promising areas that use financial technologies and AI tools in the environment of collective investment in Russia. To achieve this goal, the main task is to identify opportunities to use the existing experience of the banking sector of the Russian Federation in this direction. METHODOLOGY The aspect approach as the main approach of research is used that highlights issues of economic development of the environment and system of collective

The Transformation in Collective Investment Under the Influence of AI    491

investment. Empirical and pragmatic elements of interaction between the digital technologies tools and the market structure were applied. Numerical methods of analysis (approximation), as well as methods of comparison and analogy of market trend as the main research methods were used. RESULTS The market and the collective environment investment of the Russian Federation are under the supervision of the CBR, which also puts forward initiatives to establish an appropriate legal framework and strengthen the market structure. It should be noted that the current legislation of the Russian Federation regulating the introduction of the digital component of the economy in its activities is represented by federal laws, target programs, concepts, and development strategies (see Table 55.1), which has started to form with the approval in 1995 of The Draft Federal Law of the Russian Federation of February 20, 1995, No. 24-FZ, “On information, informatization and protection of information.” Let us note that special attention is paid to the information security of Russia in the legislative and regulatory acts presented in Table 55.1 and in other documents of this type. The introduction of financial technology and AI tools into the structure of the financial market should be result-oriented, including the increase in the assets of market funds in the total GDP of the state. Let us present this comparison for two systems of the Russian Federation: banking and its collective investment system (see Figure 55.1). The data on the funds for 2017 are calculated based on the information provided for the three quarters of 2017. The calculations are made on the average data on assets for the corresponding years. There is a clear contrast between the trend lines: the ratio of assets of credit institutions to GDP (growth) and assets of funds to GDP (decline). Trends are retrospectively determined in the long-terme; at the same time, since 2015, there has been a slight increase in the assets of the funds in GDP. This situation requires special attention from the regulator, and implies an urgent need for the active introduction of tools and technologies of AI in the collective investment of the Russian Federation. In addition, the share of open funds assets in relation to the GDP of the Russian Federation is significantly inferior to these ratios in other countries. Thus, the global average for 2017 was 64.93% (in the United States, 126.56%; in France, 83.87%; in Germany, 63.15 %; in China, 12.94%; and in India,12.51% (The International Investment Funds Association [IIFA], 2018). The website of the regulator has a special section FINTECH, where the CBR publishes all materials related to the introduction of digital

The Federal target program “Electronic Russia”

The order of the Government of the Russian Federation of January 1, 2013 No. 2036-p “About the approval of strategy of development of branch of information technologies in the Russian Federation for 2014–2020 and for the future till 2025”

Strategy of development of the information technology industry in the Russian Federation for 2014– 2020 and for the future until 2025

URL: http://www.consultant.ru/ document/cons_doc_LAW_90180/

The order of the Government of the Russian Federation of January 28, 2002, No. 65.

URL: http://www.consultant.ru/ document/cons_doc_LAW_154161/

Approving Document, Number and Date of Registration

Title of Legislative Act

01/28/2002

11/01/2013

Date of Entry Into Force

06/09/2010

10/18/2018

Date of Most Recent Amendment to Act

(continued)

Transition to the provision of public services and execution of state functions in electronic form by authorized federal authorities; development of infrastructure for access to state and municipal services and information on the activities of authorities and local governments; ensuring the solution of problems of public administration using elements of e-government; development of e-government infrastructure

“Development of the information technology sector to a full-fledged branch of the Russian economy, creating highly productive jobs and ensuring the production of high-tech and competitive products; providing various sectors of the economy with high-quality information technologies in order to increase productivity; ensuring a high level of information security of the state, industry, and citizens.”

Main Goals/Objectives

TABLE 55.1  Main Programs and Development Strategies in the Field of AI in Russia

492    Y. N. RADZIEVSKAYA et al.

The program “Digital Economy of the Russian Federation”

The strategy of information society development in Russian Federation to 2017– 2030

State program of the Russian Federation “Information society (2011–2020)”

Title of Legislative Act

URL: http://www.consultant.ru/ document/cons_doc_LAW_221756/

The order of the government of the Russian Federation of July 28, 2017, No. 1632-p “On approval of the program “Digital Economy of the Russian Federation”

URL: http://www.consultant.ru/ document/cons_doc_LAW_216363/

The decree of the president of the Russian Federation of May 9, 2017, No. 203 “On the strategy of development of the information society in the Russian Federation for 2017–2030”

URL: http://www.consultant.ru/ document/cons_doc_LAW_162184/

The order of the Government of the Russian Federation of April 15, 2014, No. 313 “About the approval of the state program of the Russian Federation “Information society (2011–2020)”

Approving Document, Number and Date of Registration

07/28/2017

05/09/2017

04/15/2014

Date of Entry Into Force

07/28/2017

05/09/2017

09/25/2018

Date of Most Recent Amendment to Act

The creation of an ecosystem of the digital economy of the Russian Federation . . . , creation of necessary and sufficient conditions of institutional and infrastructural character . . . , increase of competitiveness in the global market . . . 

Creating conditions for the formation of a knowledge society in the Russian Federation

Enhancing the quality of life and work of citizens, improving the conditions of organizations, the development of the economic potential of the country through the use of information and telecommunication technologies

Main Goals/Objectives

TABLE 55.1  Main Programs and Development Strategies in the Field of AI in Russia (continued) The Transformation in Collective Investment Under the Influence of AI    493

494    Y. N. RADZIEVSKAYA et al. 0.35

4.00

0.30

3.50 0.25 3.00 2.50

0.20

2.00

0.15

1.50 0.10 1.00 0.05

0.50 0.00

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Ratio of assets of credit institutions to GDP, %

1.87

2.58

2.34

2.17

2.42

2.81

3.10

3.75

4.14

3.94

Ratio of assets of open funds of the Russian Federation (including “funds of the funds”) to GDP, %

0.29

0.21

0.24

0.19

0.15

0.15

0.13

0.12

0.15

0.14

Ratio of assets of open funds of the Russian Federation (including “funds of funds”) to GDP, %

Ratio of assets of credit institutions to GDP, %

4.50

0.00

Figure 55.1  Ratio of assets of credit institutions and open punds of the Russian Federation (including “funds of funds”) to GDP (compiled by the authors). Note: The following data sources were used: Investment Company Facebook, 2018, The International Investment Funds Association (IIFA), 2018, National Accounts, 2019. Federal state statistics service of the Russian Federation.

technologies in its work, giving the opportunity for their wide discussion. For example, the draft federal law “on digital financial assets” is currently being discussed in this section (The Draft Federal Law No. 419059-7, 2018). At the same time, we note that the development program of financial technologies for the period 2018–2020, presented by the Central Bank of the Russian Federation includes a number of measures designed to affect the introduction of innovation technologies in the financial market (Karamova, 2018). Let us consider the activities recommended by the Bank of Russia for the development of innovative technologies in the financial market (see Table 55.2).

The Transformation in Collective Investment Under the Influence of AI    495 TABLE 55.2  The Bank of Russia’s Main Promising Areas of Activity in the Field of Financial Technologies Examples of Applications of the Tools Financial Technology

Banking Sector

Collective Investments

RegTech

“Customer identification (KYC); identification of suspicious activity and fraud prevention; automation of procedures for the preparation and submission of accounts; compliance control” (The main directions of development of financial technologies for the period 2018–2020, The CBR, 2018, URL: http://cbr.ru/ StaticHtml/File/36231/ON_FinTex_2017.pdf

SupTech

“Analysis of borrowers’ affiliation; demand forecasting for cash; predictive analysis of the stability of credit and other organizations based on payment data; online transactional analysis of data of credit institutions in terms of operations of financial market participants in order to identify fraud” (The main directions of development of financial technologies for the period 2018–2020, The CBR, 2018, URL: http://cbr.ru/ StaticHtml/File/36231/ON_FinTex_2017.pdf

Big Data и Smart Data

Big Data uses five components: value, diversity, volume, speed, reliability. Reduction of “volume” occurs with the help of Smart Data. Gathering information to analyze customers, markets, demand, cases of fraud, development trends of the market, and further filtering data (Central Bank of Russia, 2018).

Mobile Technology

Formation of tools for data collection and analysis of effective control of both individual customers and entire industries, as well as to predict potential problems.

Artificial Intelligence, Robotics, and Machine Learning

Possibility to check and filter while creating Smart Data, when deciding which data should be saved and which should be blocked. Applications of the data for customer analysis, fraud detection, market analysis, and compliance.

Biometrics

Customer identification and recognition, fraud prevention. Different types of biometrics can be used: face, voice, fingerprint, iris, hands, veins. Use of biometric consortium data, International Biometric Industry Association (IBIA), and other organizations.

Distributed Registry Technology

Distributed Ledger technologies (DLT) (private, hybrid or exclusive) with elements of centralization (full or partial) and control; can be used to prevent and detect fraud, protect transactions, improve the efficiency of financial systems, etc.

Open Interface

Application Programming Interface (API) allows traders to directly connect their screening programs to a broker account to exchange prices in real time and place orders.

Note: The following data sources were used: the main directions of development of financial technologies for the period 2018–2020 (2017), Report for public consultations issues and directions of development of regulatory and supervisory technologies (RegTech and SupTech) in the financial market in Russia.

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CONCLUSIONS 1. The study showed that the modern legislative framework of the Russian Federation, designed for the introduction of collective investment in the work of financial institutions and markets, has been elaborated in detail and includes elements of the methodology, which facilitates its application for users. At the same time, the market of collective investment in modern Russia is in dire need of the introduction of tools and technologies of artificial intelligence, which is clearly seen when comparing the results of the banking sector of the Russian Federation and global figures in this area. 2. Comparative analysis of promising areas in the field of financial technologies (in banking and collective investment) leads to the conclusion that it is necessary to promote their active implementation in the infrastructure of collective investment of the Russian Federation. 3. At the same time, there is an urgent need to highlight the problematic issues of the collective investment market in a separate programming document, detailing the stages and “milestones” of the introduction of both mechanisms and tools of digital technologies and artificial intelligence. 4. Based on the attained positive results of the banking system of the Russian Federation in the area of development of collective investment management using modern digital technologies, it is preferable to undertake the following actions at the macro level: a. to carry out work on further improvement of Russian legislation in the field of AI and financial technologies. This implies taking into account the existing challenges and the current situation, specifying the tasks of the collective investment; increasing attention from the mega regulator to the financial market. b. at the micro level: the use of an individual approach to each investor (depending on the level of his income and education, social status and region of residence, as well as other factors); active full-scale introduction of digital methods in all parts of the system (maximum exclusion of the “factor of personal influence”). REFERENCES Central Bank of Russia. (2018). Report for public consultations. Issues and directions of development of regulatory and supervisory technologies (RegTech and SupTech) in the financial market in Russia. Retrieved from https://www .cbr.ru/Content/Document/File/48604/Consultation_Paper_181016.pdf

The Transformation in Collective Investment Under the Influence of AI    497 Investment Company Fact Book. (2018). Retrieved from https://www.lexissecurities mosaic.com/gateway/sec/testimony/pdf_2018_factbook.pdf Karamova, O. V. (2018). Institutional factors of historical transformation of the Russian economy. Problems of configuration of the global economy of the XXI century: Socio-economic idea of progress and possible interpretations. In S. A. Tolkachev (Ed.), Collection of scientific articles (pp. 32–39). Krasnodar, Russia: Scientific Research Publishing House Institute of Economics of the Southern Federal District. National Accounts. (2019). Federal state statistics service of the Russian Federation. Retrieved from https://gks.ru/bgd/regl/b19_15/Main.htm Petrov, A. A. (2018). Digital economy: Russia’s challenge in global markets. Trade policy, 1(13), 44–75. Sokolov, I. A., Drozhzhinov, V. I., Raykov, A. N., Kupriyanovsky, V. P., Namiot, D. E., & Sukhomlin, V. A. (2017). Artificial intelligence as a strategic tool of economic development of the country and improvement of its public administration. Part 1. The UK and the USA experience. International journal of open information technologies, 5(9), 57–75. Retrieved from https://cyberleninka.ru/ article/n/iskusstvennyy-intellekt-kak-strategicheskiy-instrument-ekonomicheskogo-razvitiya-strany-i- sovershenstvovaniya -ee-gosudarstvennogo The Decree of the President of the Russian Federation of May 9, 2017, No. 203. “On the strategy of development of the information society in the Russian Federation for 2017–2030.” Retrieved from http://www.consultant.ru/document/ cons_doc_LAW_216363/ The Decree of the RF Government of November 1, 2013 No. 2036-R (edition of October 18, 2018). “On approval of the strategy of development of the information technology industry in the Russian Federation for 2014–2020 and up to 2025.” Retrieved from http://www.consultant.ru/document/ cons_doc_LAW_154161/ The Draft Federal Law No. 419059-7. “On digital of financial assets” (edition adopted by the State Duma of the Federal Assembly of the Russian Federation in the reading on May 22, 2018). Retrieved from http://www.consultant.ru/ cons/cgi/online.cgi?req=doc&base=PRJ&n=172447#07793757642436121 The Draft Federal Law of the Russian Federation of February 20, 1995, No. 24-FZ, “On information, informatization and protection of information.” Retrived from http://www.consultant.ru/document/cons_doc_LAW_5887 The International Investment Funds Association. (2018). Retrieved from https:// www.iifa.ca/ The Order of the Government of the Russian Federation of January 28, 2002, No. 65. Retrieved from http://base.garant.ru/184120/ The Order of the Government of the Russian Federation of April 15, 2014, No. 313. “About the approval of the state program of the Russian Federation “Information society (2011–2020).” Retrieved from http://www.consultant.ru/ document/cons_doc_LAW_162184/ The Order of the Government of the Russian Federation of July 28, 2017, No. 1632p. “On approval of the programme “Digital economy of the Russian Federation.” Retrieved from http://base.garant.ru/71734878

498    Y. N. RADZIEVSKAYA et al. Zhilkin, V. A. (2018). Artificial intelligence and digital technologies in legal activity in digital reality (on the example of Finland). Journal of foreign law and comparative law, 5(72), 16–21. Retrieved from https://cyberleninka.ru/article/n/ iskusstvennyy-intellekt-i-tsifrovye-tehnologii-v-yuridicheskoy-deyatelnosti-vtsifrovoy-realnosti-na-primere-finlyandii

CHAPTER 56

FEATURES OF ARTIFICIAL INTELLIGENCE TECHNOLOGIES AND THEIR USE AND IMPACT ON TRANSFORMATION IN THE BANKING SECTOR Galina V. Stankevich Pyatigorsk State University Gayane Yu. Atayan North Caucasus Federal University Olga N. Amvrosova North Caucasus Federal University Catherine V. Kasevich North Caucasus Federal University Tatiana V. Kara-Kazaryan Pyatigorsk State University

Meta-Scientific Study of Artificial Intelligence, pages 499–506 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

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ABSTRACT This chapter considers and actualizes the problems of application of artificial intelligence (AI) in the banking sector, analyzes the legal and regulatory acts of the Russian Federation, which determine the importance and strategy of development of the use of AI in the economy and in the banking segment. The aim of the study was to determine the current state and prospects of AI in the Russian banking system. In the course of scientific research, the authors worked on the following tasks: analysis of the current state of information technology in the Russian banking sector, the study of legislation in the field of credit and financial institutions AI, evaluation of the prospects of digital and branchless banking, as well as the problem of information security and protection of banking secrecy in the new environment. The authors came to the conclusion that the banking sector is advanced in terms of information technology and robotics. However, it should be taken into account that the use of AI technologies in banking creates new threats and challenges that require the introduction of new approaches to information security, protection of banking secrecy, and timely adaptation to changing conditions.

In the modern world, the guidelines and directions of all spheres of society are qualitatively changing under the influence of the development of information technologies; new approaches to understanding the world order are being created. The role of information is increasing, new technologies are being introduced more actively, the production of information products and services is growing, and in turn, computer literacy is increasing. These information realities define a new type of society and management at the macro and micro levels. The construction and development of the information society affects the economic conditions, the standard of living of citizens, and the viability of the state as a whole. New technologies no longer develop autonomously from each other, being separate elements of the system, but create a single information space. Nanotechnologies contribute to a new stage of development, which reduces human participation in the provision and maintenance of all mechanisms of society. The concept of AI appeared more than 60 years ago and was defined as the creation of computer systems capable of performing tasks that usually require human intelligence. However, information technologies had not been introduced in the sphere of financial services for a long time because the level of equipment did not allow solving large-scale tasks. Over the past 10 years, the computer production of AI has been actively implemented and used in the economy of different countries, determining the degree of development of the state. The development of AI gave impetus to the emergence of a new form of economy—digital.

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METHODOLOGY The digital economy as an object of legal support is not yet sufficiently represented in the legislation and therefore there is a lack of development of this problem in legal science. At the same time, the high demand for this topic makes it possible to predict the emergence of digital law in the near future, which would determine the peculiarities of the creation and application of law, taking into account the trend of expanding the use of digital technologies in legal practice. In May 2017, the decree of the President of the Russian Federation approved the strategy of information society development in Russian Federation from 2017–2030 (The Decree of the Russian Federation of May 9, 2017 No. 203). It sets the task of developing the knowledge society in Russia, increasing access to quality goods and services. In July 2017, the program, “Digital Economy of the Russian Federation,” developed by the Ministry of Communications and Mass Media of the Russian Federation, was adopted, which identified certain indicators of the digital economy by 2025. The document defines the key directions of development of priority areas of the economy: neurotechnology of artificial intelligence and medical and space technologies (Order of the Government of the Russian Federation of July 28, 2017 No.1632-p). The study of AI technologies and the possibilities of their use in the banking sector was carried out using general scientific research methods, among which analysis, synthesis, deduction, and modeling were involved, as well as formal legal, statistical, and comparative research methods, which constitute a group of private scientific methods, were widely used. RESULTS AI, automation of digital processes in our country and abroad, is used at the advanced and progressive level in the banking sector of the economy. The banking segment has become a kind of benchmark for improving the information technology level in other areas. However, the history of the introduction of AI in the banking sector was not so rapid. In the early 1980s Citibank Investment Bank tried to build several system experts, using one of the branches of artificial intelligence, which was supposed to have the ability to make decisions at the expert-person level. They were not the only ones. Many other Wall Street companies launched similar projects at the time. In 1987, Security Pacific National Bank created a fraud prevention working group aimed at countering the unauthorized use of debit cards at ATMs and stores by means of AI (Butenko, 2018). All these basic projects did not

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so much lead to practical results as they became a kind of benchmark for the development of a new direction in the banking industry. In the 1990s, AI technologies were not developed and introduced into banking activities, as they had a long-term prospect of obtaining benefits and profits that did not meet the needs of the time. In 2018, the Central Bank of Russia developed “Main Directions of Development of Financial Technologies in the Banking Sector for 2018–2020.” Within the framework of the concept, it is necessary to ensure the breakthrough of financial technologies, actively introducing AI to the financial market during these 2 years. From the point of view of the financial organizations and their clients’ interests, the most promising technologies are big data and their analysis, artificial intelligence, robotics, biometrics, cloud technologies, open interfaces (API), as well as technologies of distributed registers. The Bank of Russia intends to use them both within its own structure and for the organization of interaction between banks, their customers, as well as government agencies-operators of state information systems (Central Bank of Russia, n.d.). The transformation of this area of the economy concerns the widespread introduction in the sector of financial and banking services (banks, investment funds, insurance organizations) automation, robots, bots (electronic devices for communicating with customers), computer centers for information processing without office customer service (via the Internet and mobile communications), artificial intelligence, remote maintenance, and provision of a variety of financial and banking services. All these changes have an impact on the nature of customer relationships and lead to the changes in the nature of the banking operations themselves. Moreover, the rapid development of digital technologies threatens the very existence of banks as financial intermediaries. The natural reaction of banks to these processes is to reduce the costs of doing banking business. As a result, a wave of bank employees layoffs swept across all countries and continents. Large-scale layoffs of employees took place in American, European, Russian, and other banks. For example, PJSC Sberbank, in the conditions of a growing number of users of remote channels (more than 30 million actively use mobile and online banking of 135 million customers of the bank), at the end of December of 2016 announced a reduction in the number of personnel in 2017 by about 8%, which is 26,000 employees, followed by further reductions in 2018 (Bloomberg, 2016). Trends to minimize the cost of banking involve the opening of bank branches without employees, in which retail banking services are provided automatically through terminals on a self-service basis. This model of interaction with customers in the global banking business is called “branchless banking” (Kievich, 2016).

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Remote relations between banks and customers are also called digital banking, electronic banking (e-banking), online banking, or remote banking. Digital banking services can be provided using the mobile Internet via mobile communications. For example, such services as provision of bank account statements to the client; provision of information on bank products (loans, deposits, unit investment fund, etc.); transfers between your own accounts; intrabank transfers to the accounts or card of another client of the bank; transfers to a card to another bank; transfers to accounts in another bank; payment for mobile communications, home phone, utilities, taxes, and so on. Some digital banking services can be provided through Internet resources (Drueva, 2014). Further development of the virtual banking business, according to experts, can lead to the fact that the existing banks that have an office with several employees, where you can call and get an answer to questions, could disappear, and then there would be only logins, passwords, answering machines and robots that provide banking services, and it would be a totally digital banking (Katasonov, 2017). A lot of Russian banks use chatbot technology. With their help, users get quick access to financial services through messengers. The most popular application, on the basis of which experiments with banking chatbots are being developed, is Telegram, which is already equipped with built-in banking options. It is the most common messenger also because other popular platforms have not opened full-function versions (although it is known about the launch of the banking chatbot called “Point” for legal entities in Facebook Messenger). With the help of this messenger, the most common “talking bots” are created, which are actively used and developed by Sberbank. Whatsapp and Viber are also of great interest as these two messengers are the leaders in terms of user coverage, but currently they are not used in the banking area. In OTKRITIE Bank, chatbots are considered to be rather a niche service. Chatbot is a method of communication with those who are not able or do not want to contact the call center; also, the bank is planning to integrate into the mobile application live chat support with the help of bot technology. Tinkoff Bank uses bot technology in order to increase customer loyalty and reduce costs of service channels, but the bank Home Credit applies chatbots primarily for the departmental needs (Kolomeets, 2016). Most likely, according to the forecasts of commercial world agencies, the development of chatbots will be very fast. Russian companies are engaged in the development of AI in the banking sector to provide opportunity to communicate in Russian in the chat system. In 2017, Moscow hosted a conference on the use of chatbots and robotics, in which more than 250 representatives of banks, experts, and application design specialists participated.

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Sixteen participants shared their experience in creating and promoting chatbots. At the end of the conference, a master class was conducted by Arkady Sandler, CEO of Nanosemantics, one of the leading companies in the field of AI development. Its topic was dedicated to the methods of creating and “educating” about the right bot that currently is undoubtedly of great relevance for the financial industry (Katasonov, 2017). However, the use and application of chatbots does not provide complete security of the system, as the bank is unable to control it fully. Nevertheless, the functions of the bot are often connected not only with information, but also with payments. Therefore, many banks solve the problem of creating and providing a safe environment for the chatbot platform, attracting the best brains in the field of programming to this process and allocating adequate funding for this. Another new digital trend in the banking sector is crowdfunding aimed at attracting financial resources from a large number of people. The purposes of crowdfunding are the implementation of a service or product, events, financial assistance to needy, financial support to natural persons, and legal entities. The author of the term is Jeff Howe (“What Crowdfunding Sites,” n.d.). Introduction of information technologies and AI in the activities of credit and financial institutions makes the problem of increasing financial literacy among people of different ages in Russia more vital. In this regard, the Ministry of Finance of the Russian Federation is actively implementing the program “Promotion of Financial Literacy and Development of Financial Education in the Russian Federation” (“National Strategy for Improving,” n.d.). The aim of the project is to improve the financial literacy of Russian citizens, promote the formation of Russian citizens’ reasonable financial behavior, informed decisions and responsible attitude to personal finance, and improving efficiency in the consumer financial protection area. The Ministry of Finance of the Russian Federation attracts to the solution of this problem scientific specialists who conduct open scientific seminars with different categories of citizens by providing telephone information support. According to the results of 2017, the number of use of national bank cards reached about 40%. According to the head of the Acquiring Department of VTB Bank A. Kirichek, in 2018, this figure reached 45%. Regarding the amount of noncash payments, it is noted that in 2017 it increased from 12 to 16 trillion rubles. In 2018, bank cards turnover increased to 20–21 trillion rubles (Amvrosova & Atayan, 2018). The introduction of AI in the banking area presented in such forms as digital banking and branchless banking, gave rise to new approaches and regulators to the most important institution of banking that is banking secrecy. There is a need for additional protection of financial interests of citizens, credit institutions, and the state as a whole. New methods of

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protection and security of restricted information have been defined. New conditions and threats dictate different mechanisms of influence. CONCLUSIONS The institute of banking secrecy is regulated by the civil code of the Russian Federation, the Federal Law “On information, information technology, and information protection.” The right to protection of banking secrecy is based on the constitutional guarantees of privacy, personal secrecy, and the prevention of the dissemination of information about the private life of a person without his consent, as the right of everyone of the confidentiality of the information about his bank accounts and deposits and other information, the types and amount of which are established by law, and the corresponding obligation of banks and other credit institutions to keep bank secrecy, as well as the obligation of the state to ensure this right in legislation and law enforcement. Consequently, there is a need for judicial regulation of the procedure for obtaining by state bodies the information that constitute bank secrecy to ensure client protection by fixing in the legislation a closed list of bodies that have access to such information, as well as the amount of information that they are entitled to receive in certain cases. In this regard, information and tax legislation is changing very intensively. It should be noted that according to paragraph 2 of Article 86 of the Tax Code (as amended), not in all cases the tax authority has the right to request information about the bank’s customers. For example, the tax authority may not request information about the bank’s client, unless it is related to the audit of the client as a taxpayer (Atayan & Amvrosova, 2018). The state power in our country adopts acts, which define new directions of the world order, based on the development and application of innovative technologies. Remote, nonfinancial and Internet banking, cloud technologies, derivative online options, remote identification define a new course of development of the banking system. The use of AI technologies in the banking sector meets the needs of the information society, increases the information mobility of the population, ensures the financial well-being of economic entities, creating more progressive trends for economic growth, and in turn strengthens the state itself as a whole. REFERENCES Amvrosova, O. N., & Atayan, G. Yu. (2018). Some problems of the use of credit (debit) card. Policy and Law, 6, 53–60.

506    G. V. STANKEVICH et al. Atayan, G. Yu., & Amvrosova, O. N. (2018). Banking secrecy: Problems of legislative and practical law enforcement. Public service and personnel, 2, 125–130. Bloomberg, L. P. (2016). Sberbank will fire 26 thousand employees, Finanz.ru. Retrieved from https://www.finanz.ru/novosti/aktsii/sberbank-uvolit-26 -tysyach-sotrudnikov-1001602108 Butenko, E. D. (2018). Artificial intelligence in banks today: Experience and prospects. Finance and Credit, 24, i3. Central Bank of Russia. (n.d.). Main directions of development of financial technologies in the banking sector for 2018–2020. Retrieved from http://www.cbr.ru/static html/file/41186/on_fintex_2017.pdf Drueva A. A. (2014). The Legal status of participants of innovative activity (doctoral dissertation). Moscow State Law Academy, Moscow, Russia. Federal Law of July 27, 2006 N 149-FZ “On information, Information technologies and Information Protection.” Retrieved from http://www.szrf.ru/szrf/doc .phtml?nb=100&issid=1002006031000&docid=104 Katasonov, V. Yu. (2017). Digital Finance. Cryptocurrency and e-economy. Freedom or a concentration camp? Moscow, Russia: Book World. Kievich, A. V. (2016). Crowdinvesting as an alternative model of financing of the investment project. Economy and Banks, 1, 58–64. Kolomeets, M. (2016). Invasion of the chat-bot. Banking review, 9. Ministry of Finance of the Russian Federation. (n.d.). National Strategy for Improving Financial Literacy 2017-2023 Moscow-2017. Retrieved from https://m.minfin .gov.ru/ru/document/?id_38=118377-proekt_natsionalnaya_strategiya_ povysheniya_finansovoi_gramotnosti_2017-2023_gg National Strategy for Improving Financial Literacy 2017–2023. (n.d.). https://m.minfin .gov.ru/ru/document/?id_38=118377-proekt_natsionalnaya_strategiya_ povysheniya_finansovoi_gramotnosti_2017-2023_gg Order of the Government of the Russian Federation of July 28, 2017 No. 1632p. “On approval of the Program “Digital Economy of the Russian Federation.” Retrieved from http://static.government.ru/media/files/9gFM4FHj4 PsB79I5v7yLVuPgu4bvR7M0.pdf The Decree of the Russian Federation of May 9, 2017 No. 203. “On the Strategy of information society development in the Russian Federation to 2017-2030,” Legal-reference system “Garant.” Retrieved from https://www.garant.ru/ products/ipo/prime/doc/71570570/ What Crowdfunding Sites Can Be Trusted in Russia. (n.d.). Retrieved from http:// svoedelo-kak.ru/finansy/kraudfanding.html

CHAPTER 57

THE ROLE AND IMPORTANCE OF ELECTRONIC TRADING PLATFORMS IN TERMS OF DIGITALIZATION OF THE ECONOMY Nina V. Demina Pyatigorsk State University Marina V. Chistova Pyatigorsk State University Olga S. Eremina Pyatigorsk State University Olga I. Natkho Pyatigorsk State University Aleksandra V. Ryzhuk Pyatigorsk State University

Meta-Scientific Study of Artificial Intelligence, pages 507–515 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

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ABSTRACT This chapter considers and summarizes the main postulates about the content, species diversity, and the role and importance of electronic trading platforms in the further digitalization of the Russian economy. The authors convincingly prove that the development of the digital economy in Russia will contribute to the improvement of the business and investment climate and increase the transparency of business conditions, mainly due to the development of digital platforms and electronic trading platforms. The purpose of scientific research is the role and importance of electronic trading platforms in the development of the digital economy. In the course of the study, the authors studied the essence of electronic trading platforms, analyzed the regulatory framework for the functioning of electronic trading platforms, classified existing electronic trading platforms in accordance with their functional purpose, and identified areas for further development of electronic trading platforms as an important element of the digital economy. Despite the sufficient list of legislative documents regulating the functioning of electronic trading platforms, there is a clear lack of effectiveness of the existing legislation in the field of electronic procurement.

The emergence of global communication networks, and, first of all, the Internet, the improvement of communication and the introduction of advanced IT-technologies led to the digitalization of the world economy. The digital economy is understood as a set of social relations that are formed when using electronic technologies, electronic infrastructure, and services, technologies for analyzing large amounts of data and forecasting in order to optimize production, distribution, exchange, consumption, and increase the level of socioeconomic development of states (Leonova, 2017). Thanks to the digitalization of the economy, traditional ways of doing business have been replaced by new, more modern ones using e-commerce and digital technologies. Today, an electronic trading platform can be called almost any online resource, with which sales transactions between customers and suppliers are concluded. Modern IT-systems and databases allow you to automate the process of working on electronic trading platforms (hereinafter-ETP), visualize the processes, identify various risks, including a cartel, predict the problems for separate procurements, and so on, which will further lead to the formation of an integrated intellectual procurement system, which will be based on the use of new technologies, such as big data analysis, artificial intelligence, transformation of information into a digital form (Demina & Chistova, 2017). And, ultimately, e-commerce and e-commerce platforms will further contribute to the development of the digital economy.

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METHODOLOGY According to McKinsey, the potential economic effect of digitalization of the Russian economy will increase the country’s GDP by 4.1–8.9 trillion by 2025 (Digital McKinsey, 2017). In our opinion, such a bold macroeconomic forecast should be associated not only with the overall digitalization of activities, but also with the development and implementation of innovative business models and technologies. In addition, the development of the digital economy will contribute to the improvement of the business, investment, and innovation climate in the country by increasing the availability and efficiency of public services, the development of the ecosystem of numerous business services, and increasing the transparency of the business environment. All of the above is largely dependent on the development of digital platforms and electronic trading platforms, structurally constituting one of the parts of the e-commerce system. One of the actively developing forms of e-commerce in the last 10–15 years is electronic trading platforms, which in general are online information systems that allow suppliers and consumers of various goods and services to unite in one information and trading space and provide the participants of ETP with a number of services that significantly increase the efficiency of business suppliers and guarantee the safety of the transaction for consumers (Nazimov, Lee, Suslenkov, & Dolgina, 2018). RESULTS Possibilities of ETP can bring its participants to a fundamentally new level of interaction, which enables to carry out all procedures for the search of potential partners, the preparation and conclusion of transactions with the help of the software functions of the electronic trading platform, and the necessary level of confidentiality of the bidding is provided through encryption of transmitted information and the use of the mechanism of electronic digital signature. Now work of electronic trading platforms in the Russian Federation is regulated by the Federal Law of April 5, 2013, No. 44-FZ, “About contract system in the sphere of purchases of goods, works, services for ensuring the state and municipal needs” (Legal-reference system “Consultant Plus”), the Federal Law of July 18, 2011, No.223-FZ, “About purchases of goods, works, services by separate types of legal entities” (Legal-reference system “Garant”); the Federal Law of July 26, 2006, No. 135-FZ, “About protection of competition” (Legal-reference system “Consultant Plus”); the Federal Law of April 6, 2011, No. 63-FZ, “About the electronic signature” (Legalreference system “Consultant Plus”); the Federal Law of July 24, 2007, No.

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209-FZ, “On the development of small and medium-sized businesses in the Russian Federation” (Legal-reference system “Garant”); the Federal Law of December 29, 2012, No. 275-FZ “On the state defense order” (Legalreference system “Consultant Plus”); and some other federal laws, certain regulations, and orders of the government of the Russian Federation, as well as regulations on procurement (zakupki.gov.ru), which contains requirements for procurement, in particular, the procedure for the preparation and conduct of procedures, the conditions of procurement, the order of conclusion, and execution of contracts. Unfortunately, we have to state that at the moment the e-commerce market in Russia is characterized by insufficient efficiency of the current legislation in the field of e-procurement, which leads to a decrease in the productivity of both the electronic trading platforms themselves and the companies operating on them. At the same time, the state’s dissatisfaction with the effectiveness of public procurement leads to further improvement of legislation in this area. So, thanks to this, in the last 2–3 years, there have been two main trends in improving the sphere of legal and regulatory framework of the functioning of electronic trading platforms: firstly, there is a legislative consolidation of the rigid framework and boundaries in regulating the activities of the ETP through the functional development of a single information system, and secondly, the process of convergence of two basic laws on procurement: the Federal Law of April 5, 2013, No. 44-FZ, “About contract system in the sphere of purchases of goods, works, services for ensuring the state and municipal needs” (Legal-reference system “Consultant Plus”), and the Federal Law of July 18, 2011, No. 223-FZ, “About purchases of goods, works, services by separate types of legal entities” (Legal-reference system “Garant”). The identified trends allow us to agree with the point of view of the general director of Sberbank-AST N.Yu. Andreyev that the further development of ETP in Russia will largely depend on how lawmakers will be able to analyze best practices and incorporate them into the regulation of similar relations in procurement as at the Federal Law No. 44-FZ, “About contract system in the sphere of purchases of goods, works, services for ensuring the state and municipal needs” and the Federal Law No. 223-FZ, “About purchases of goods, works, services by separate types of legal entities” (Interview with Nikolay Yuryevich Andreyev, General Director of CJSC “Sberbank-AST,” 2018). On the basis of our analysis of the regulatory framework governing the activities of ETP, it is possible to build a classification of existing electronic trading platforms in accordance with their functional purpose: 1. Federal (budgetary) trading platforms or B2G (business-to-government) platforms which are used for the organization of public procurement. Currently, public procurement is carried out on six

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federal electronic trading platforms (FETP), such as Sberbank-AST, national electronic platform, RTS-Tender, SUE “Agency for state order, investment activity and interregional relations of the Republic of Tatarstan”, JSC Unified Electronic Trading Platform, electronic trading system of JSC Russian Auction House. 2. Commercial electronic platforms or B2B (business-to-business) platforms where commercial organizations act as customers. At the same time, B2B sites, in turn, are divided into three types: –– electronic platforms that are created and supported by buyers (buyer-driven); –– electronic platforms, which on the contrary are created and supported by large suppliers (supplier-driven or seller-driven); and –– electronic platforms that are created and maintained by a third party intermediary between sellers and buyers (third-party driven), this category of platforms is the most numerous one. On commercial sites, the customer can be any legal entity or individual, including an individual entrepreneur. Each commercial electronic trading platform (ETP), as well as the federal (budget) has its own address on the Internet (its website). There are independent (public) and private (corporate) platforms that belong to specific organizations (trading platforms). Such ETPs are focused on large customers with high procurement volumes, a wide range of goods and services, they provide an opportunity not only to participate in electronic auctions, but also allow you to prepare procurement plans, collect and analyze the needs for inventory (TMC), hold tenders and auctions, carry out qualification selection of suppliers, as well as generate reports and analyze procurement activities. 3. E-commerce platforms between individuals C2C (consumer-to-consumer), where some individuals can sell goods to other individuals. 4. E-commerce retail systems, where the seller is a company and the buyers are mostly individuals B2C (business-to-consumer). 5. Electronic trading platform on sale of a debtor’s (bankrupt’s) property, designed to automate the procedure of bidding in the sale of property of debtors during the procedures applied in business about bankruptcy, in accordance with the requirements of the Federal Law of October 26, 2002, No. 127-FZ, “On insolvency (bankruptcy)” (Legal-reference system “Consultant Plus”) and the Order No. 495 of the Ministry of Economic Development of 23.07.2015 (”Legal-reference system “Garant”). On approval of the Procedure for conducting tenders in electronic form for the sale of property or enterprise of debtors in the course of procedures applied in bankruptcy proceedings, Requirements to

512    N. V. DEMINA et al. operators of electronic platforms, to electronic platforms, including technological, software, linguistic, legal and organizational means necessary for conducting tenders in electronic form for the sale of property or enterprise of debtors in the course of procedures applied in bankruptcy proceedings, amendments to the order of the Ministry of Economic Development of Russia of April, 5, 2013 No. 178 and invalidation of some orders of the Ministry of economic development. (Legal-reference system “Garant”)

As for the total number of electronic trading platforms, it is quite difficult to determine their exact number, although there is information in the public domain that there are several thousand of such platforms. For example, these are such Russian ETP as B2B-Center (b2b-center.ru); B2B-Rusnano (b2b-rusnano.ru); B2B-Energo (b2b-energo.ru); B2B-NPK (b2b-npk. ru); B2B-Avia (b2b-avia.ru); scientific and technical consortium Altimeta (ultimeta.ru); Norbit (norbit.ru); trading portal Fabrikant (fabrikant.ru); group of sites OTC.RU (otc.ru); iTender (itender-online.ru); NAUMEN group of sites (naumen.ru); Sberbank-AST (sberbank-ast.ru); LLS “Baltic electronic platform” (bepspb.ru); interregional electronic trading system (m-ets.ru); JSC “RUSSIA Online” (rus-on.ru); regional trading platform (regtorg.com); Ural ETP (etpu.ru); all-Russian electronic trading platform (the trading platform-vetp. of the Russian Federation); open-trading platform (utpl.ru); merchant purchasing system “AMS Service” (ams.lotexpert. ru); Crimean electronic platform (torgi82.ru); universal electronic platform ESTP.RU (estp.ru), as well as international, such as China Bidding Ltd (chinabidding.org); dgMarket The Development Gateway Foundation Inc. (ru.dgmarket.com); tenders of Ukraine (Ua-Tenders.com); procurement Portal of JSC “Sovereign wealth fund “Samruk-Kazyna” (tender.sk.kz); and others (Association of Electronic Trading Platforms, http://aetp.ru/etp/ list [List of Electronic Platforms (ETP)]). Despite the great diversity, currently the ETP is primarily a set of information and technical solutions that ensure the interaction of the buyer (customer) with the seller (supplier) through electronic communication channels at all stages of the transaction. All this allows us to state that electronic trading platforms, on the one hand, act as a kind of intermediaries between the buyer and the seller, customers and suppliers, and on the other hand, contribute to improving the efficiency of business processes in organizations and the acquisition of economic benefits by all participants of the ETP by attracting new customers, business partners, suppliers, reducing associated costs, and increasing profits, as ETP makes it possible to participate in online trading without the personal presence of bidders and choose the most appropriate procedure, which will provide the best price offers. Thus, at present, the modern ETP, on the one hand, acts as an information and technical solution, an Internet resource, a marketing tool, a

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means of interaction of participants in trade procedures, and on the other hand, as one of the most natural elements of the development of the digital economy and innovative infrastructure, whose potential for the development has not been used to the full. Based on the fact that digitalization for Russia is one of the state priorities, the plans of further activities of the government of the Russian Federation are aimed at enhanced development of digital technology and their implementation in all spheres of life in accordance with the program approved by the Order of the Government of the Russian Federation of July 28, 2017 No. 1632-p (Program “Digital Economy of the Russian Federation, 2017). It is the implementation of the activities of this document that will cover everything from the development of electronic software, electronic commerce (electronic goods for electronic money on electronic trading platforms), electronic production, e-health, e-education, and e-banking system to e-citizenship, e-elections, e-government, and finally, e-state. CONCLUSIONS The development of the digital economy in Russia will contribute to the improvement of the business and investment climate, and increase the transparency of the business environment, mainly due to the development of digital platforms and electronic trading platforms. It is electronic trading platforms that are now one of the most promising and actively developing forms of e-commerce. They are online information systems that allow suppliers and consumers of various goods and services to unite in one information and trading space and provide ETP participants with a number of services that increase the efficiency of suppliers’ business and guarantee the security of the transaction for consumers. Currently, the work of electronic trading platforms in the Russian Federation is regulated by a number of federal laws, as well as by separate resolutions and orders of the government of the Russian Federation, as well as by the regulations on procurement (zakupki.gov.ru). However, there is a clear lack of effectiveness of the existing legislation in the field of electronic procurement, which leads to a decrease in the productivity of both the electronic trading platforms and the companies operating on them. At the same time, the state’s dissatisfaction with the effectiveness of public procurement leads to further improvement of legislation in this area. According to the analysis of the regulatory framework governing the activities of electronic trading platforms, it is possible to identify such classification groups of ETP in accordance with their functional purpose, as federal (budget) trading platforms or B2G (business-to-government) platforms; commercial electronic platforms or B2B (business-to-business) platforms;

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e-commerce platforms between individuals C2C (consumer-to-consumer); retail e-commerce systems, where the seller is a company and buyers are mainly individuals B2C (business-to-consumer); electronic trading platforms for the sale of property of debtors (bankrupts). It is quite difficult to determine the exact total number of electronic trading platforms, although there is information in the public domain that there are several thousand such platforms. Thus, at present, the modern ETP, on the one hand, acts as an information and technical solution, an Internet resource, a marketing tool, a means of interaction of participants in trade procedures, and on the other hand, as one of the most natural elements of the development of the digital economy and innovative infrastructure, whose potential for the development has not been used to the full. Today, Russia can take full advantage of the positive momentum for the development of the digital economy. The development of electronic trading platforms as an important element of the digital economy, in our opinion, will be further concentrated around the automation of the increasing process of operations and the exclusion of the negative impact of the human factor on the effectiveness of trade. Thus, the automation of marketing analysis and collection of information about the participants of the ETP will allow for the intelligent selection of suppliers for specific trades, which will enable both customers and performers to cover all tenders of specific interest to them, increase the level of competition, conduct customization of participants, and ultimately, to form a comprehensive intelligent procurement system based on the use of new technologies, such as big data analysis, artificial intelligence, transformation of information into a digital form. REFERENCES Demina, N. V., & Chistova, M. V. (2017). To the question about the specifics of ecenters in terms of prevention of cybercrime. Modern Science, 1, 26–29. Digital McKinsey. (2017, July). Digital Russia: A new reality Retrieved from https:// www.mckinsey.com/ru/~/media/McKinsey/Locations/Europe%20and%20 Middle%20East/Russia/Our%20Insights/Digital%20Russia/Digital-Russiareport.ashx Federal Law of July 26, 2006, No. 135-FZ, “About protection of competition” (Legalreference system “Consultant Plus”).Retrieved from http://www.consultant. ru/document/cons_doc_LAW_61763/ Federal Law of July 24, 2007, No. 209-FZ, “On the development of small and medium-sized businesses in the Russian Federation,” Legal-reference system “Garant.” Retrieved from https://base.garant.ru/12154854/

Role and Importance of Electronic Trading Platforms    515 Federal Law of April 6, 2011, No. 63-FZ, “About the electronic signature” (Legalreference system “Consultant Plus”). Retrieved from http://www.consultant. ru/document/cons_doc_LAW_112701/ Federal Law of July 18, 2011, No. 223-FZ, “About purchases of goods, works, services by separate types of legal entities” (Legal-reference system “Garant”). Retrieved from https://base.garant.ru/12188083/ Federal Law of October 26, 2002, No. 127-FZ, “On insolvency (bankruptcy)” (Legalreference system “Consultant Plus”). Retrieved from http://www.consultant .ru/document/cons_doc_LAW_39331/ Federal Law of December 29, 2012, No. 275-FZ, “On the state defense order” (Legal-reference system “Consultant Plus”). Retrieved from http://www.consultant.ru/document/cons_doc_LAW_140175/ Federal Law of April 5, 2013, No. 44-FZ, “About contract system in the sphere of purchases of goods, works, services for ensuring the state and municipal needs” (Legal-reference system “Consultant Plus”). Retrieved from http:// www.consultant.ru/document/cons_doc_LAW_144624/ Interview With Nikolai Andreev, General Director of “Sberbank-AST.” (2018). Retrieved from http://www.sberbank-ast.ru/Content.aspx?cid=2833 Leonova, K. S. (2017). The need and possible consequences of digitalization of the Russian economy. Economics and Business: Theory and Practice, 12, 103–105. List of Electronic Platforms (ETP). Retrieved from http://cpprf.ru/list-electronictrading-platforms.html Nazimov, A. S., Lee, S. R., Suslenkov, Yu. V., & Dolgina, V. T. (2018). Evaluation of economic efficiency of electronic trading platforms. Fundamental Research, 1, 96–100. Order No. 495 of the Ministry of Economic Development of 23.07.2015. Retrieved from Legal-reference system “Garant.” https://base.garant.ru/71340068/ Program “Digital Economy” of the Russian Federation. (2017). Retrieved from http://static.government.ru/media/files/9gFM4FHj4PsB79I5v7yLVuPgu4bv R7M0.pdf

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CHAPTER 58

TREND ANALYSIS IN THE USE OF ARTIFICIAL INTELLIGENCE IN FINANCIAL MANAGEMENT Andrey V. Efimov Pyatigorsk State University Anna V. Savtsova North Caucasus Federal University Olga N. Pakova North Caucasus Federal University Yuliya N. Dyakova St. Petersburg State University of Aerospace Instrumentation Alfiia A. Sokolova North Caucasus Federal University

Meta-Scientific Study of Artificial Intelligence, pages 517–525 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

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ABSTRACT The purpose of this study is to conceptualize the problems in applying artificial intelligence (AI) in various fields and spheres of activity, including financial management. On the basis of generalization and analysis of data of theoretical researches and economic reviews, the authors describe the current situation as a transition from reasoning intellect, or applied artificial intellect, to self-programmable intellect which is characteristic for the global trends, but in contrast to Russian trends. In Russia, only a fifth of financial institutions have gone beyond the simplest technologies of AI. The authors of the study summarize and critically analyze the trends in the use of AI in the banking sector, identify problems, and formulate directions for the development of these trends. The authors integrate the views of various scientists regarding the term AI and discuss the lack of a clear philosophical basis that defines the concept intelligence and its main components. The methodological basis of the study were the methods of scientific knowledge: scientific abstraction, retrospective analysis, methods of economic statistics, system, logical-semantic and comparative analysis. Based on the theoretical generalization of points of view on the studied problem, empirical data, and international and domestic experience, the authors conclude that it is advisable to clarify the definitions of the concept of intellect and its components on the basis of its philosophical understanding, which will contribute to the analysis and timely adjustment of developing trends, including the field of financial management.

The human mind is characterized by the presence of cognitive distortions—a feature of decision-making, increasing in situations of uncertainty. A person is also characterized by subjectivism and errors of an unintentional nature. The desire to eliminate these restrictions has led to the need to study AI. Thus, AI can formulate a logical conclusion, which for a person is complex and not sufficiently obvious. It is able to optimize, classify, and process a large amount of information (Vorontsova, Dedyukhina, Kosinova, Momotova, & Yakovenko, 2019). The author of the term AI is considered to be John McCarthy. The formulation of this concept presented by him at a conference at Dartmouth University does not directly address the essential characteristics of human intelligence. McCarthy (2007) understood AI as the science and technology of creating intelligent machines, especially intelligent computer programs. He believed that scientists in the field of AI can benefit from the methods and tools that are not specific to the person if it contributes to the resolution of problems. It should be noted that the exact interpretation of the English phrase AI implies the ability to reason intelligently, which somewhat corrects the requirements for the intellectual machine. In this sense, we share the opinion of the chairman of the St. Petersburg branch of the Russian Association of AI Gavrilova, who emphasizes the presence of fantastic anthropomorphic

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coloration in the incorrect Russian translation of this term (Gavrilova & Khoroshevsky, 2000). The main problem, both in the opinion of McCarthy (1960s of the 20th century), and according to the most modern scientists studying the issues of AI, is the lack of an exact understanding of the notion of intelligence and what computational procedures can be considered to be intellectual ones. We have an idea only of the individual mechanisms of intelligence, but we are not aware of intelligence as a whole phenomenon. That is why in the definition presented by McCarthy, the notion intelligence refers only to the “computational component of the ability to achieve goals in the world” (McCarthy, 2007). It can be noted with great confidence that in this case this is a computational algorithm. METHODOLOGY The methodological basis of the research consists of the concepts and provisions of modern computer science and economics, set out in the works of domestic and foreign scientists in the field of AI, information technology, economics and financial management. Systematic and integrated approaches were applied in the research as general methodological principles of studying the possibilities of using AI in financial management. It also provided an opportunity to identify cause-and-effect relationships and integrate various directions of the study. It is important to note that the prominent domestic and foreign scientists such as Hebb, Hemming, Turing, Shannon, Glushkov, Pospelov, Ershov, Lyapunov, and others were engaged in research of AI. However, to date, there is no unified view about intelligence and intelligent machine, or about the benefits and challenges of implementation of self-programmable machines in the life and activities of man. The information and analytical base of this topic was based on the study of official analytical reviews and statistical research data, materials of authors’ research and expert opinions of famous Russian and foreign scientists, information from relevant Internet sources, direct authors’ observations, and conclusions. RESULTS Intellect (intellectus) translated from Latin means “feeling, perception, understanding, concept, reason.” The mind is defined by a number of scientists as the mental ability of the body to adapt to new situations, as well as to manage the environment, using training and experience, understanding,

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and using abstract concepts. The reference and encyclopedic content of Internet sources characterize intelligence as a general capacity to cognize and solve difficulties, which unites all the cognitive abilities of a person: sensation, perception, memory, representation, thinking, imagination, as well as attention, will, and reflection. In the early 1980s of the 20th century, the term AI began to be associated with the demonstration of computer systems and algorithms capabilities traditionally inherent in the human mind: understanding the language, learning, the ability to reason, solve problems, and so on (Barr & Feigenbaum, 1981). In particular, it related to the approaches used in solving problems (Materials of the analytical agency TAdviser, n.d.). The basic properties of AI include the following: understanding the language, learning and the ability to think and, importantly, to act. Gradually, a complex of intensively developing related technologies and processes was formed in the field of AI: • • • • •

natural language text processing, machine learning, expert system, virtual agents (e.g., chatbots and virtual consultants), and recommendation systems.

It is self-programming (improvement of software in situations, the reaction to which is not described by the algorithm) that military experts note as the main distinguishing feature of AI, including in relation to automation. In addition to self-learning and adaptability (self-programming), the president of the Russian academy of rocket and artillery sciences, doctor of technical sciences, Professor Vasily Burenok also points to the ability of these systems to make decisions in circumstances of significant uncertainty, using information from a variety of different problem areas. Accordingly, it is possible to allocate two directions in applying existing tools of AI: using tools and techniques specific to the person and using different approaches if it helps solving problems. That is, AI does not necessarily have to think and acts like a person, it must solve problems of the level characteristic of human intelligence (Kusakina, Vorontsova, Momotova, Krasnikov, & Shelkoplyasova, 2019). There is another viewpoint of Neznamov, the head of the research center for regulation of robotics and AI, senior lawyer of Dentons, which has the right to exist—AI as an umbrella concept with many meanings. Conventionally, the meanings can be grouped around the strong (superintelligence) and weak (applied) AI. At the same time, all experts concur in one point— the currently available intelligent systems are used in very specific areas and are applicable in nature, for example, FinTech, Legal Tech, and so on.

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There are two basic approaches to the development of AI systems (Copeland, 1993): • top–down or semiotic, involves the development of systems that simulate high-level mental processes (thinking, reasoning, speech, emotions, creativity, etc.), for example, expertise and knowledge, inference systems; and • bottom–up or biological, involves the modeling of intelligent behavior based on biological elements (neural networks and evolutionary calculations), the creation of computing systems such as neuro-and biocomputer. The essence of the bottom-up approach contradicts the content of the science of AI in McCarthy’s understanding—they are united only by a common ultimate goal. Representatives of the Russian Association of AI distinguish the following definitions that from their point of view most accurately characterize the science of AI: • involves solving problems of hardware in the loop simulation (HILS) of those human activities that are traditionally considered intelligent (Averkin, Haase-Rapport, & Pospelov, 1992); • the main objective is the reconstruction of reasonable arguments and actions with the use of computer systems and other artificial devices (Osipov, 2008); and • is engaged in the creation of intelligent systems—technical or software systems that are able to solve problems that are traditionally considered creative, specific to a particular subject area, knowledge of which is concentrated in the memory of these systems. The structure of the system is represented by three basic blocks—knowledge base, problem solver and intelligent interface (Averkin et al., 1992). In Russia, according to the research of the TAdviser analytical center, AI has affected such areas as intelligent infrastructure monitoring, big data collection and processing, knowledge management, technical and medical diagnostic systems, the formation of individual learning trajectories, consumer behavior analysis, smart platforms, and so on (Materials of the analytical agency TAdviser). However, the volume of the market of AI and machine learning is growing rapidly every year and the growth rate is constantly increasing. If in 2017 the market volume was estimated at 700 million rubles, then by 2020, according to experts, the projected market volume will be 28 billion rubles, that is, it will grow by 40 times.

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In 2017, the number of Russian projects with the use of AI was estimated at several tens; in 2018 there were hundreds of them. On the basis of the experience of Abbyy, though traditionally, the most active in this regard are banks and financial institutions; in 2018 energy companies, retail, and telecommunications were engaged in the “race of AI.” In large companies, for example, Gazprom Neft, Sberbank, MTS, Severstal, teams of specialists who are involved only in projects using intelligent technologies were introduced. This year, according to experts, AI will become an integral part of the business of every major company (Momotova, Belokon, Kilinkarova, Mayboroda, & Stroi, 2019). Thus, according to Timakov, head of the machine learning company Norbit (group of companies “LANIT”), it is already possible to identify a number of areas in the business processes of which AI can be integrated best: • personalization of client offers and creation of new generation of recommendation services; • automatic processing of user content and actions: for example, analysis of reviews, requests, identification of bots, etc.; • maintenance of infrastructure concept smart and safe city; • technical and medical expert systems; and • automation and augmented reality in industrial production, and so on. According to representatives of companies-developers of AI systems, the main and primary consumers will be business areas with the following indicators: • • • • •

large number of transactions in service delivery processes; structured and formalized business processes; complexity and variety of choice of solutions; high level of standardization of processes; and the need for information and consulting support here and now.

Thus, the most relevant for Russian areas of application of AI and machine learning technologies are the banking sector, retail, insurance companies, telecom, oil industry, military-industrial complex, and smart city. In the financial sector, AI technologies are mainly used to create new user experiences and analyze big data to identify dependencies. Even now, neural networks are involved in decision-making on the approval of loans and the calculation of the amount of insurance (Lopatina, Tselih, Chugunova, & Ostrovskaya, 2014). The success of the use of AI technologies is now associated with the presence of a large amount of data that is needed for the effective training

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of analytical models. This is most reflected in the results of deep learning models—neural networks. To date, AI is already sufficiently developed and reliable in all that concerns risks, privacy, human factor problems, and marketing strategies. In the banking environment, this circumstance allowed six key trends to form related to AI: the use of chatbots and robo-advisors, product offer personalization and increasing customer loyalty, Internet of things (IoT), prevention of external threats, and improving operational efficiency (Materials of the analytical agency TAdviser). The use of chatbots in order to provide financial advice or obtain the necessary information and service is faced with a strong desire of the client to communicate with the person. For example, Harvard business school Ryan W. Buell published an extensive study on automation, conducted since 2008, which showed that customers who use only ATMs are less likely to be satisfied with banking services than other users. That is, automation is only useful for business when it helps to bring it closer to the client. Robo-advising shows the steady annual growth and has become an alternative to financial advisors on banking issues, purchases, and cash transactions. Thus, the volume of the portfolio under the control of robots in the U.S. financial markets reaches one trillion dollars. By 2020, it will be more than two trillion dollars. Service in the digital world is becoming more personalized. To clearly determine a person’s preferences, it is enough to have information about the three most popular GPS-coordinates: home, work, and favorite place. According to Khasin, senior managing director of Sberbank, the IoT will provide new opportunities for integrating AI systems and financial services. It is a question of financial services for things representing the human user. For example, by 2025 a refrigerator will be able to contact shop managers and make an order where the food basket is cheaper or better meets the customer’s ideas about the quality of products. The excess of Internet connected devices and providing information about user preferences helps to preserve electronic footprints in the cloud. This will further automate and transfer financial communication to the level of thing-thing. The next trend in financial management with the help of AI is related to the automation and algorithmization of activities in the back office. There is a gradual transition from the work of transactions to work with deviations. The staff will monitor deviations in time, timing of transactions, adjust deviations, and the processes will be almost fully automated. Gradually developing the concept of a bank without an office, banks stop or reduce the growth of the branch network. As of 2018, Russia is successfully developing two banks that have chosen the online model as the main one, and do not have branches at all: Rocketbank and Tinkoff. The digitalbank model assumes dynamic development of IT-infrastructure and remote

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services. There are no costs for the maintenance of offices or staff salaries; only technology is available. As an advantage, the bank’s clients point to the use of convenient channels for communication: social networks, messengers, and so on. New information technologies make it possible to synchronize data in such a way that the transition from one channel to another is not lost to customer history, service speed, and high level of protection. CONCLUSIONS Thus, to date, there is no clear understanding of the term AI, which complicates the study of this area of knowledge and identifies emerging trends. In our opinion, goal-setting, self-programming, and intuition are some of the basic components that characterize any intelligence, including artificial. The main global trends in the use of AI in finance are associated with the transition from reasoning intelligence to intelligence making decisions based on experience and implicit data. The Russian practice is mainly characterized by the narrowly applied nature of the use of AI in financial management. However, already, Sberbank is actively implementing with the use of AI: • at the design stage—forecasting of potential and real demand for banking products, risk assessment; • at the stage of management of production processes—automation of processing of documents on the loan and its approval, automation, and optimization of interaction with existing and potential customers; • at the stage of promotion—product offer personalization, regulation of interest rates depending on the credit rating of the client; and • at the sales stage—automated and self-service interfaces in the channels of communication with customers. One of the ambiguous areas for AI in financial management is still the analysis of foreign exchange markets. This is confirmed by the experience of the largest American banks. Most of them are studying applications for machine learning, but have hesitated to use them yet (JP Morgan, Wells Fargo, etc.). The introduction of AI technologies leads to the emergence of new problems, one of which is the reduction of jobs. According to expert data, 30% of bank jobs will disappear within five years.

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REFERENCES Averkin, A. N., Haase-Rapoport, M. G., & Pospelov, D. A. (Eds.). (1992). Explanatory dictionary of artificial intelligence. Moscow, Russia: Radio and Communication. Barr, A., & Feigenbaum, E. (Eds.). (1981). Handbook of artificial intelligence. Los Altos, CA: William Kaufman Copeland, B. J. (1993). Artificial intelligence: A philosophical introduction. Oxford, England: Blackwell. Gavrilova, T. A. (2000). Introduction to the intellectual systems. In T. A. Gavrilova, & V. F. Khoroshevsky (Eds.), Knowledge base of intelligent systems: Textbook for universities (pp. 9–38). St. Petersburg, Russia: SPb. Ivin, A. A., & Nikiforov A. L. (Eds.). (1997). Dictionary of logic. Moscow, Russia: VLADOS. Kusakina, O. N., Vorontsova, G. V., Momotova, O. N., Krasnikov, A. V., & Shelkoplyasova, G. S. (2019). Using managerial technologies in the conditions of digital economy. In E. Popkova & V. Ostrovskaya (Eds), Perspectives on the use of new information and communication technology (ICT) in the modern economy (pp. 261– 269). Cham, Switzerland: Springer. Lopatina, E. U., Tselih, C. N., Chugunova, E. V., & Ostrovskaya, V. N. (2014). Managing risks of venture entrepreneurship. Asian Social Science, 10(23), 191–198. Materials of the Analytical Agency TAdviser. (n.d.). (Electronic resource). Retrieved from http://www.tadviser.ru/index.php/ McCarthy, J. (2007). What is AI? (Personal website: http://www-formal.stanford. edu/jmc Momotova, O. N., Belokon, L. V., Kilinkarova, S. G., Mayboroda, T. A., & Stroi, G. V. (2019). Conceptual approaches to formation of financial strategy of a higher education institution. In E. G. Popkova (Eds.), The future of the Global Financial System: Downfall of Harmony (pp. 803–812). Cham, Switzerland: Springer. Osipov, G. S. (2008). Artificial intelligence: The state of research and a look into the future. St. Petersburg, Russia: SPb. Russell, S. & Norvig, P. (Eds.) (2006). Artificial intelligence: A modern approach. Moscow, Russia: Williams. Vorontsova, G. V., Dedyukhina, I. F., Kosinova, E. A., Momotova, O. N., & Yakovenko, N. N. (2019). Perspectives of development of managerial science in the conditions of information society. In E. Popkova & V. Ostrovskaya (Eds), Perspectives on the use of new information and communication technology (ICT) in the modern economy (pp. 980–989). Cham, Switzerland: Springer.

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CHAPTER 59

PROSPECTS OF THE USE OF ARTIFICIAL INTELLIGENCE AND AUTOMATIZATION SYSTEMS IN ACCOUNTING AND AUDITING IN THE REALITIES OF THE DIGITAL ECONOMY Natalia N. Balashova Volgograd State Agricultural University Spartak A. Vardanyan Volgograd State Agricultural University Maria V. Volodina Volgograd State Agricultural University Nataliya A. Ishkina Volgograd State Agricultural University Ilya A. Koshkarev Volgograd State Agricultural University

Meta-Scientific Study of Artificial Intelligence, pages 527–534 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

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ABSTRACT This chapter presents the results of a research on the modern market of information systems for automatization of accounting and auditing. On the basis of this research of the modern trends in the development of the digital economy, the prospects for using artificial intelligence (AI) in accounting and auditing were analyzed. The goal of this study is to analyze the current market of software products for automating accounting and auditing, condition of application of AI in these areas, as well as studying the requests and requirements of practitioners for the automatization systems of these processes. The scale of the research covers the analysis of Russian and foreign systems for the automation of accounting and auditing, and also reveals the condition of application of AI in the largest audit companies in the world. The main methodology is based on the analysis of Russian and foreign scientific literature in these spheres, the opinions of specialists in this field, and periodicals and electronic resources.

Practically all over the world, business is more actively introducing digital resources, computer programs, AI, and automation systems into its activities. This is due to the fact that such systems make it possible to increase the efficiency of production and management, since they can be used to spend less time, work, and money to carry out business. The relevance of the research is due to the fact that today the government of the Russian Federation has proclaimed the path of development towards the expansion of the digital economy. One of the constituent elements of the National Program, Digital Economy of the Russian Federation, adopted by the order of the government of the Russian Federation dated July 28, 2017, is the development of the information infrastructure and digital technologies. And this process is accompanied by intensive development and more active introduction of new information technologies into the economic activities of the organizations. The purpose of the study is to analyze the current condition and to identify the general directions and prospects of the development of information technology and use of AI in accounting and auditing. To achieve this goal, it is necessary to consistently solve the following tasks: • to explore the relevance of the active using of AI in the processes of accounting and auditing in the realities of the digital economy; • to research the conditions of the modern market of information systems and computer programs for automating accounting and auditing; and • to identify the main advantages and disadvantages that can be achieved with the widespread introduction of AI and automatization systems of accounting and auditing.

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METHODOLOGY In the course of the study, a various methodology was used to receive information about the subject under study. General scientific methods such as analysis and synthesis, induction and deduction, analogy, statistical sampling, comparison, description, measurement, and others were used. The main methodology is based on the analysis of the Russian and foreign scientific literature on these issues, the opinions of specialists in this field, and periodicals and electronic resources. The analysis of modern foreign software for automation of accounting, as well as their comparison with Russian counterparts, made it possible to identify the advantages and disadvantages that are important for practitioners. Thanks to the inductive and deductive methods of research of scientific works of Russian and foreign experts, it was possible to analyze and forecast the development of digital systems and the possibilities of introducing AI into accounting and auditing. Investigating the scale of computer programs use, statistical methods were used to identify the current degree of use of modern information systems in the studied areas. RESULTS AI is not a new phenomenon. It is concerned with the task of using computers for understanding of human intelligence. The concept of artificial neural networks appeared in the middle of the last century, and since then, this section of science has been constantly evolving. For example, highresolution cameras, modern sensors, and a global positioning system have allowed the creation of unmanned vehicles. Fears that such a traditionally computerized industry, as financial accounting, will be under heavy pressure from AI, do not seem to be in vain (Balashova & Vardanyan, 2015). In society, it is believed that soon AI will greatly reduce the need for specialists, if it does not oust the profession of an accountant from the market. The study “Where Machines Could Replace Humans—and Where They Can’t (Yet),” conducted by McKinsey in 2016, predicts that 86% of tasks of accountants and auditors can potentially be automated (Chui, Manyika, & Miremadi, 2016). In an Oxford University study, accountants and auditors are listed among professionals who may suffer from computerization. Deloitte claims that the United Kingdom could lose about half a million of jobs in the financial sector due to automation (Frey & Osborne, 2013).

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However, there is no information anywhere, what percentage of errors can AI make when it is analyzing a text compared to a man—and this directly affects the responsibility of the auditor (Vardanyan, 2017). Accounting, reporting, and auditing are those areas of professional activity in relation to which their participants are sure that changes are coming. In the United Kingdom, for example, the review Future of Accountancy from the software maker FreeAgent revealed the absolute certainty of 96% of respondents that by 2022 all or a substantial part of their work will be automated (GAAP.ru, 2018). It is worth noting that automation is only one aspect of the arrival of AI, but even with such an incomplete view of the consequences, almost all professional accountants are aware that their role is about to change. According to the director of the British auditing company Raedan, Jonathan Bareham, AI will be “the next step in automation and expansion of efficiency that cloud software has already provided.” According to him, it will increase time savings, reduce errors, and increase compliance. Today, similar technologies are also actively developing in Russia. For example, the Knopka company exists in the Skolkovo Foundation. This company has developed a technology for automating some parts of accounting, which allows processing all bank payments of a client using AI to determine the type of transaction and to draw up a bank statement. Knopka Company has set for itself an ambitious goal—to develop such a technology that will automate up to 90% of accounting. “This means that the system will perform 90% of all accounting operations on its own, and only 10% will require human participation,” representatives of Knopka explained (Korotkova, 2016, p. 2). By 2019, the company Knopka had developed 42 robots to automate various accounting processes. For example, the primary documentation is immediately scanned by the first robot, and it turns out one file in pdf format, which consists of all the scanned documents. This allows a batch scanning, which significantly saves time. After that, the second robot automatically divides the file received from the first robot, saving in that time a lot of files in jpeg format. Next, the third robot analyzes each file received from the second robot in jpeg format, compares it with its database of standard documentation forms, and determines which type of primary document the scanned file refers to—invoice, work completion certificate, and so on. After analysis, this robot sorts the received files by document type and saves them in different folders. At the same time, the first robot does not always scan and save the document in the orientation it should be. If the document is saved upside down, the third robot automatically turns it over so that users can read it comfortably.

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Inside of the third robot, there are two neural networks—one to rotate, the other to classify documents. It took 40 thousand hand-marked scans to train the networks. The fourth robot recognizes the contents of scanned and sorted documents, identifying their number, date of compilation, organization name, and so on. For recognition, it used the software from the company ABBY. After recognition, the file is saved in a format suitable for further processing. And only after that the finished scan with all the recognized details gets to the accountant. Major financial and audit companies have also announced the start of work in the field of AI. At the beginning of 2016, KPMG and IBM announced a partnership to use the IBM Watson AI system (IBM Newsroom, 2016). According to a KPMG representative, “[Watson] can very quickly read thousands of pages of contracts and agreements and almost instantly issue a summary based on the criteria you set for it.” On the same day, Deloitte announced that machine learning technologies from Kira Systems will perform similar tasks to audit various contracts of Deloitte customers (Deloitte, 2016). It is impossible to say for sure that AI will create new jobs, but it can be expected that it will significantly simplify the work of audit companies. The auditing company Raedan uses the Xero online platform integrated with Receipt Bank, which allows machine learning to be used, and the predictive tool Fluidly, based on AI technologies, is used by them to estimate cash flows. Expensify is used for analyzing and processing customer costs. Although there are already certain developments in the field of development of AI and ES systems (parallel distributed networks, neural networks, hybrid algorithms, evolutionary computing), the creation and commissioning of information systems of such a high class is a matter of the future. AI is generally very useful and able to help in a number of situations with manual data processing, and this is a very significant part of traditional audit work. Data retrieval, collation, validation are examples of where this is possible. AI significantly speeds up digitization of the traditionally manual data entry or extraction processes, which significantly reduces the time required for their preliminary preparation for verification. Compared to humans, machines will always be unsurpassed in performing repetitive routine tasks. The process of obtaining and processing data using high technologies can be automated and streamlined, which once and for all relieves auditors of their time-consuming tasks, such as finding the right data, extracting them from documents, converting them into digestible formats. People will only analyze the information. In any large company, accounting systems generate a huge amount of information that the auditor has to check. At the same time, automated systems are capable of analyzing such amounts of data much faster than a

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person, as well as searching for various patterns, connections, suspicious, and abnormal transactions. And due to the time saved, the auditors will be able to better analyze the key questions about the full picture of the client’s business. Ready-made solutions managed by AI will be able to quickly identify unusual jumps in incoming orders, strange looking expenses or suspiciously favorable conditions for leasing equipment in the case of a particular supplier, which may signal collusion. All these machines can be taught. The use of AI-based audit automation systems will in the near future allow achieving 100% of array checks, rather than random data verification, as was done traditionally to save time and auditor resources. When this is achieved, the auditors will be able to pay more attention to more important aspects related to the analysis of customer business performance. But most customers win. Indeed, at the most basic level, process efficiency means that customers need to spend less time and resources on responding to auditors’ requests for the right documentation, and this is especially critical in the context of tight deadlines. For their part, the auditors, having the opportunity to devote more time to areas where their professional judgment is required, will make these judgments more reasonably and make fewer mistakes, which means an increase in the quality of the audit. Today, many large audit companies are betting on the development of the use of information systems and AI to automate the most routine processes. For example, the auditing companies EY and PwC are implementing a pilot project on the use of AI for image recognition, performing part of accounting tasks, and other types of routine technical activities that employees now perform (Financial Times, 2018). The British edition notes that all the big four companies, PwC, EY, KPMG, and Deloitte, are actively increasing their investments in new technologies, including AI, in order to optimize their operations and to avoid human factor errors. Among the most promising areas of application of AI are the acceleration of filling in applications from and for clients, the recognition of “anomalies” in financial transactions and the analysis of data for possible external interference and fraud. “It’s becoming more and more obvious to us that AI will have a huge impact on our business, so we’re already trying to make the most of it in this area,” said John Andrews, head of PwC UK for technology and investment. “Most likely, will grow exponentially” (Financial Times, 2018, p. 5). In turn, KPMG plans to introduce a system that will evaluate credit information based on the analysis of loan portfolios in commercial banks. In addition, AI can be used for predictive analysis and building probabilistic models. For the introduction of these technologies, KPMG has been cooperating with IBM’s Watson project and several startups working in the field of AI for more than a year.

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FT notes that the big four auditors are actively introducing AI because they are trying to defend their positions in the EU market, where competition has noticeably increased recently: New EU rules, according to which the company is obliged to announce an audit and tender every 10 years to change his auditor at least once in 20 years, significantly increased the number of tenders for an external audit. Thus, the number of such tenders among companies in the FTSE 250 index increased from five in 2012 to 50 in 2016 (Financial Times, 2018). “Expectations in the audit industry are growing noticeably as regulators become more and more demanding,” notes Stephen Giggs of Deloitte. “Audit firms are trying to stand out from the crowd with their approach to innovation and their own developments, which should provide them with a competitive advantage” (Olacom.ru , p. 6). CONCLUSIONS Thus, we can conclude that at the present stage of development of information systems and AI to automate accounting and auditing, their functionality is reduced to the digitalization of primary documents, their grouping, sorting, and general analysis. A more complex task associated with professional judgment remains for the person. There are various reasons for this. The first reason is complicated accounting standards that increasingly rely on the use of professional judgment by an accountant. The second reason is that the current level of robotization of accounting is very far from the day when the machines completely replace people. The most frequent technological development in the industry is the recognition of the scanned client’s first documentation and its folding into folders for further processing by an accountant. That is, the robot is an assistant to the least qualified accountant, whose functionality is easily algorithmized. According to the analysis of the world’s research in this area, it was revealed that in the near future more than 80% of accountants and auditors can be replaced by automated accounting and auditing systems. At the same time, in the area of accounting, their work is based on recognizing and sorting documents for further processing, and in the field of auditing, to a wider audit sample and more rapid analysis of information. The research also revealed that audit automation systems are mainly developed and implemented by large foreign companies, in particular, from the “big four,” together with major software developers, such as, for example, IBM. The research revealed that Russia also develops automation systems; for example, in the Skolkovo fund, robots were created to automate various accounting processes.

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In conclusion, it can be concluded that automation and the introduction of AI in accounting and auditing will help ease the work of people by performing routine tasks for them, and highly skilled professionals who are able to effectively apply their professional judgment are not threatened by machines (yet). REFERENCES Balashova, N. N., & Vardanyan, S. A. (2015). Internal audit in the agricultural sector: Condition, problems, and prospects of development. Izvestiya Nizhnevolzhskogo agrouniversitetskogo kompleksa: Nauka i vysshee professional’noe obrazovanie, 3(39), 246–250. Chui, M., Manyika, J., & Miremadi, M. (2016, July 8). Where they could replace humans—And where they can’t (yet). Retrieved from: https://www.mckinsey.com/ business-functions/digital-mckinsey/our-insights/Where-machines-could -replace-humans-and-where-they-cant-yet Deloitte. (2016, March 8). Deloitte forms alliance for artificial intelligence in the workplace. Retrieved from https://www.prnewswire.com/news-releases/deloitte-forms -alliance-with-kira-systems-to-drive-the-adoption-of-artificial-intelligence-in -the-workplace-300232454.html Frey, C. B., & Osborne, M. A. (2013). The future of employment: How susceptible are jobs to computerisation? Retrieved from http://www.oxfordmartin.ox.ac.uk/ downloads/academic/The_Future_of_Employment.pdf Financial Times. (2018). Auditing to be less of a burden as accountants embrace AI. Retrieved from www.ft.com/content/0898ce46-8d6a-11e7-a352-e46f43c5825d GAAP.ru. (2018, May 18). Artificial intelligence in the view of professional accountants. Retrieved from https://gaap.ru/news/155725/ IBM Newsroom. (2016). KPMG announces agreement with IBM Watson to help deliver cognitive-powered insights. Retrieved from https://www-03.ibm.com/press/us/ en/pressrelease/49274.wss Korotkova, T. (2016, September 21). AI from the “Knopka” allows you to automate bookkeeping. Retrieved from http://www.cnews.ru/news/line/2016-09-21_ii_ot _knopki_pozvolyaet_avtomatizirovat_vedenie Olacom.ru. (n.d.). Messages intellect board hi tech. AI has already conquered the world whether we like it or not. What are smartphones? What is MANET. Retrieved from https://olacom.ru/en/ssd-nakopitel/soobshcheniya-intellect-board-hi-tech -ii-uzhe-zavoeval-mir-hoteli-my-etogo-ili-net-chto/ Vardanyan, S. A. (2017). Basic vectors of accounting and audit development based on blockchain technology in the conditions of the digital economy. Nauchnoe Obozrenie: Teoriya I Praktika, 11, 23–27.

CHAPTER 60

CLUSTERING OF THE CENTRAL FEDERAL DISTRICT REGIONS BY THE QUALITY OF LIFE OF THE POPULATION Vera I. Menshchikova Tambov State Technical University Elena Y. Merkulova Tambov State University named after G. R. Derzhavin Sergey P. Spiridonov Tambov State Technical University IrinaA. Andreeva Tambov State Technical University Anatoly N. Berezhnoy Volsky Military Institute of Material Support

Meta-Scientific Study of Artificial Intelligence, pages 535–540 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

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ABSTRACT This chapter aims to assess the quality of life of the population in the Central Federal District of Russia and to highlight relevant regional clusters. The authors propose a hypothesis that there is a significant differentiation of the regions by the quality of life of the population, necessitating the need for individual approaches to solving social problems in these regions. The research methodology is based on the use of statistical and economic mathematical methods: the cluster analysis, the principal components method, Ward’s method, as well as tabular and graphical methods of information processing. The gap among the regions in the per capita income (PCI) of the population exceeds 2.4 times, the gap in the average per capita gross regional product is more than 3.6 times. The study revealed that the main sources of the quality of life of the population are productivity enhancement, personal income growth, development of economy, and creation of conditions for social development and a healthy lifestyle.

The socioeconomic policy of any country is aimed at ensuring a high quality of life for the population, that is, providing guarantees of people’s rights and freedoms, satisfaction of people’s needs for various goods and services, and so on. The development of guidelines for the implementation of the socioeconomic policy of the government requires a comprehensive and reliable assessment of the quality of life of the population in the context of individual regions as there is often a sharp differentiation in the level of socioeconomic development. The purpose of this chapter is to assess the quality of life of the population using the example of the regions of the Central Federal District of Russia and to select the regional clusters by the level of quality of life. METHODOLOGY Statistical and economic mathematical methods, such as the cluster analysis, the principal components method, Ward’s method, and tabular and graphical methods of information processing were used in the research. Calculations and data processing were performed using Microsoft Excel and IBM SPSS Statistika software packages. The research is based on the hypothesis that a significant differentiation of regions by the quality of life of the population necessitates the development and application of individual approaches to solving social problems in these regions.

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RESULTS To assess the quality of life of the population, first of all, it is necessary to reveal the factors affecting it. In general, there are various approaches to systematizing these factors. The researchers (Neretina, Salimova, & Salimov, 2004) identified the level of income, satisfaction with living conditions, food availability, quality of medical care, and a number of subjective criteria, including family happiness, spiritual comfort, respect of others, confidence in the future, religious beliefs, and so on, as the main determinants of the quality of life of the population. According to Bezyazychny and Shilkov (2004), the quality of life of the population depends on social status, health, education, family relations, and other similar factors. We consider the quality of life of the population through the system of basic human needs that are vital for human activity. These include consumption of basic foodstuffs in compliance with rational consumption norms approved by the Ministry of Health of the Russian Federation, provision of basic types of material goods (availability of durable goods), availability of living space and housing, and communal services (water pipes, sewage systems, electric stoves; Genkin, 2013; Kravchenko, 2017; Mukhacheva, 2012; Shabashev, & Glushakova, 2015). Using the statistical data from the Central Federal District (CFD) regions of Russia for 2017 (Rosstat: gks.ru), we assessed the quality of life of the population and allocated the relevant clusters. To assess the availability of basic foodstuffs to the population of the Central Federal District regions, an integral indicator was calculated on the basis of the geometric average by comparing the actual consumption with rational nutritional standards of basic foodstuffs. The analysis of the cluster distribution of the Central Federal District regions by the level of compliance of the actual consumption of basic foodstuffs with rational consumption standards conducted by Ward’s method showed that there were two groups of regions—the first one (Belgorod, Kursk, Tver, Bryansk, Voronezh, Lipetsk, Moscow, Tula, Yaroslavl regions) demonstrated the consumption exceeding the nutritional standards, while the second one (Vladimir, Ivanovo, Kaluga, Oryol, Ryazan Kostroma, Smolensk, Tambov regions, and Moscow) demonstrated a discrepancy between rational consumption rates. Further, the integrated indicator of the availability of living space and utility services to the population of the Central Federal District regions was calculated in the same manner. As with the previous parameters, the integral indicator of the availability of living space and utility services was the highest in Moscow. The results for the Belgorod and Moscow regions were also higher than the average regional parameters in the CFD. The six regions—Bryansk, Voronezh, Kaluga, Kursk, Lipetsk, Yaroslavl regions—had

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the most favorable situation in terms of the availability of living space (on average 28.6 m2 per person). According to the data of 2017, in these regions, water supply amounted to 78%, water disposal reached 74.8%, availability of centralized heating was 85.4%, hot water supply was 66.5%, baths (shower facilities) was 67.4%, and gas was 88.8%. In the remaining nine regions of the CFD, these figures were below the average for the Central Federal District. At the next stage of the research, the satisfaction of spiritual needs was quantified through the attendance rates of museums, theaters, libraries, sports facilities, the availability of broadband Internet, the availability of computers and communications and other gadgets. The integral indicator of satisfaction with cultural and sporting goods in Moscow and the Moscow Region was three times higher than that in other regions of the CFD. The least level of satisfaction was in the Ivanovo, Kostroma, Kursk, and Oryol regions. In most regions of the CFD, the number of viewers in theaters per 1,000 people was 183 people, the number of museum visitors per 1,000 people was 756, the attendance rate of gymnasium per 1,000 residents was 736 visits, the attendance rate of swimming pools remained low enough per 1000 residents—58 visits; at the same time in Moscow and the Moscow region, this figure was close to 330 visits. A rather high level of coverage with analogue and digital television broadcasting was observed in all regions of the CFD and reached 97%. It should be noted that a high anthropogenic load was observed in the city of Moscow and the Moscow Region: the emission rate of harmful substances was 158,000 tons, which was twice as high as the average for the regions of the Central Federal District and negatively affected the quality of life in these regions. The indicators of the demographic situation in the Central Federal District regions were quite problematic. On average, the natural decline in the population was four people per every 1,000 people. At the same time, in the Belgorod and Moscow regions, the natural decline in the population was slightly more than one person per 1,000 people; in the other regions the natural population decline was about five people per 1,000 people. And only in Moscow, the increase was almost two people per every 1,000 people. The increase in population in the regions of the CFD was due to migration growth, with an average of 25 people per every 1,000. The highest inflow was observed in the Belgorod and Moscow regions (93 people per 1,000 people), and the lowest was observed in the Bryansk, Vladimir, Kostroma, Kursk, Lipetsk, Oryol, Ryazan, Tambov and Tula regions (11 people per 1,000 people). The highest life expectancy was in Moscow, and the Belgorod and Moscow regions, and accounted to 77 and 73 years, respectively; in other regions of the CFD the life expectancy was around 71 years. Availability of doctors contributes to the increased life expectancy. In Moscow, it was 55 doctors per 10,000 people, but in other regions of the Central Federal District there was an average of

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44 doctors per 10,000 people. The number of crimes per 100,000 people in the Central Federal District was 1,259 cases. The lowest level of crime was observed in the Belgorod, Ryazan and Tula regions, and the highest level was observed in the Tver and Kaluga regions. The economic factors influencing the quality of life of the population were considered by the level of average per capita incomes of the population, the decile indicator of income, the poverty rate, and the unemployment rate (Ayvazyan, 2001). The indicator of gross regional product per capita in Moscow in 2017 amounted to 1.1 million rubles, and for the rest of the regions there was an average of 300,000 to 350,000 rubles. The average unemployment rate in the CFD in 2017 was 4.7%, the highest unemployment rate was observed in the Smolensk, Oryol, and Yaroslavl regions (more than 6%), the lowest rate was in Moscow (1.8%) and the Moscow region (3.3%). The average per capita income of the population in Moscow was 59,203 rubles, and in the Bryansk, Vladimir, Ivanovo, Kostroma, Kursk, Oryol, Ryazan, Smolensk, Tambov, and Tver regions, there was an average of 24,399 rubles, that is, the differentiation exceeded by 2.4 times. In the abovementioned regions, the largest share of the population with incomes below the subsistence minimum (13.4%) was observed. At the same time, in the CFD regions, the differentiation between the 10% of the rich and the poor population was six times on average. According to the results of the study, clustering of the Central Federal District regions by the quality of life of the population was carried out; the findings are presented in the consolidated typological grouping of the Central Federal District regions (see Table 60.1). Allocation of the four regional clusters in the Central Federal District of Russia is a methodological basis for the development of guidelines for the implementation of the socioeconomic policy of the government and regions, taking into account the development of each group of regions. At the same time, the main sources of ensuring the quality of life of the TABLE 60.1  Typology of the Central Federal District Regions of Russia by the Quality of Life of the Population The Quality of Life of the Population

Regions

Very high

Moscow

High

Belgorod and Moscow regions

Medium

Voronezh, Ivanovo, Kaluga, Smolensk, Tver, Yaroslavl regions

Low

Bryansk, Vladimir, Kostroma, Kursk, Lipetsk, Oryol, Ryazan, Tambov, Tula regions

Source: Compiled by the authors according to the research findings.

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population in all selected clusters are productivity enhancement, income growth, improved facilities for social development and a healthy lifestyle. CONCLUSIONS Thus, the research resulted in the confirmation of the hypothesis proposed there is a significant differentiation between the regions by the quality of life of the population, necessitating individual approaches to solving social problems in these regions. In conclusion, it should be noted that the assessment of the quality of life of the population of the Central Federal District of Russia and the allocation of four regional clusters can provide guidelines for the implementation of the socioeconomic policy of the government and regions, taking into account the specifics of development of each group of regions. ACKNOWLEDGMENTS The chapter was supported by the Russian Fundamental Research Foundation and the Tambov region in the framework of the research project 18-410-680010 p_a REFERENCES Ayvazyan, S. A. (2001). Comparative analysis of the integral characteristics of the quality of life of the population of the constituent entities of the Russian Federation. Moscow, Russia: CEHMI RAN. Bezyazychny, V. F., & Shilkov, E.V. (2004). Quality of life. Rybinsk, Russia: RGATA. Genkin, B. M. (2013). Man and his needs. Moscow, Russia: Infra-M. Kravchenko, A. I. (2017). Socio-economic and legal foundations of economic development. Ufa, Russia: OOO Omega Sajns. Mukhacheva, A. V. (2012). The quality of life of the population as a scientific category: Theoretical approaches to the definition. Bulletin of KemSU, 1, 4(52), 303–307. Neretina, E. A., Salimova, T. A., & Salimov, M. Sh. (2004). Subjective indicators of quality of life in the region. Standards and quality, 11, 52–55. Shabashev, V. A., & Glushakova, O. V. (2015). The quality of life of the population: The basis of methodology, theory, management. Economy and social policy, 5, 31–37.

CHAPTER 61

MODELING OF INNOVATIVE DEVELOPMENT OF THE BANK IN THE CONDITIONS OF COMPETITION AND INFLATION Alexander P. Gorbunov Pyatigorsk State University Tatiana V. Kasaeva Pyatigorsk State University Alexander P. Kolyadin Pyatigorsk State University Leyla D. Tokova North-Caucasus State Academy of Humanities and Technology

Meta-Scientific Study of Artificial Intelligence, pages 541–547 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

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ABSTRACT The processes of strengthening the role of market competition and the transition of the economy to an innovative path of development require the formulation and solution of new complex management tasks. Characteristic features of such tasks are large amounts of primary information, unreliability and inaccuracy of data, instability of the environment, and uncertainty of internal relationships. The basis for their solution is the developed models using modern software and program complexes. An urgent task in this situation is the development of adaptive models for effective management of the bank. A special place among the theoretical and practical problems of banking is the problem of financial management of the bank. Its essence is that for the effective functioning of the bank in the conditions of instability of market processes, increasing bank risks, risk management should ensure its financial stability in the present and projected future. The situation of financial imbalance, current characteristic for Russian banks, is largely a consequence of weak governance based on inadequate assessment by the bank of its economic condition.

A bank is a complex dynamic system characterized by a set of possible states, each of which is described by a set of its specific parameters. The existing methods of assessing the economic condition of the bank are not sufficiently informative, as they allow assessing the state of the bank at a particular time and do not establish a link between the previous and subsequent periods. Thus, the statistical series is interrupted, and the dynamics of the bank’s development is displayed by a number of independently calculated indicators, which in themselves are not informative, which narrows the possibilities of analysis and forecasting. To solve this problem, it is necessary to radically change the existing approaches to the management of the bank and the introduction of methods based on the application of modern economic and mathematical modeling. METHODOLOGY An effective system of financial management of the bank should be created through the formation of information-driven flows for the collection, processing, analysis, and presentation of information for each structural unit in compliance with the following principles: • the use of methods of recognition of crisis situations, and • management of present and future financial activities of the bank on the basis of reliable information.

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The basis of this approach is a system that integrates timely assessment of the economic condition of the bank, constant monitoring of operating conditions, forecasting of crisis situations, and allows developing the best options for the development of the bank in an unstable market environment. As part of this work, a one-product dynamic model “stability of the bank in the conditions of competition and inflation” was developed and tested, which allows to assess the state of the bank, take proactive measures, and predict its trajectory in the future, taking into account these factors. The proposed method can be used in the practical work of the federal and regional regulatory bodies in the development of programs and measures to improve the stability of banks. RESULTS The economic system is a complex, self-organizing, and self-developing system that is able to overcome internal constraints and achieve balance with the external environment. The system of relations of pressure and subordination, competition, and compromise agreement of economic entities ensures coherence of actions of a huge number of people producing material benefits. This is the property of the economy as an open system. An objective assessment of sustainability requires a detailed analysis of the financial condition. From this point of view, we note the approach of Balabanov (2015), who determines that an economic entity can be considered as financially stable, as fully able to cover the funds invested in assets (fixed assets, intangible assets, working capital) from its own resources, does not allow unnecessary accounts payable and receivable, and pays on time for its obligations (Kantorovich, 1960). Financial stability of an economic entity implies a combination of four favorable characteristics: • high solvency—the ability to properly pay for their obligations; • high liquidity of the balance sheet—sufficient coverage of debt liabilities by assets; • high creditworthiness—sufficient ability to repay loans with interest and other financial costs; and • high cost-effectiveness—significant profitability, ensuring the necessary development of the company, a good level of dividends and maintaining the stock price. Among the important aspects of the problem of sustainability is the definition of the boundaries of financial stability of an economic entity. This is due to the fact that insufficient financial stability can lead to a lack of funds

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for the development of production and insolvency. Excess financial stability will impede the development and burden the cost of excess inventory and reserves. In this regard, Zhdanov (1999) proposes to use mathematical and graphical models for determining the break-even point, which allow identifying the dependence of the critical volume of production on the controlled parameters of the economic mechanism. Among them, the author calls the following: the range of products and the balance of its output; the parameters of product quality, variable costs, fixed costs, correlation of price, and its quality and profit; and its distribution, the amount of loans, and borrowings and their distribution (Dolyatovsky, Kardash, & Sergeenko, 1998). In the work of Kasaeva, stability of the bank is analyzed in the conditions of competition and inflation. Analysis of the impact of these factors allows us to say that the investment policy of the bank is formed depending on competition and inflation. The income of the bank in this case changes in accordance with the influence of these factors (Kasaeva, 2002). When developing the model “stability of the bank in the conditions of competition and inflation,” we proceed from the following provisions: the demand for the bank’s investments depends on the bank’s interest rate, the investment offer depends on the bank’s interest rate, the interest rate reacts to the interest rates of competing banks and the level of inflation, the amount of the bank’s investments varies depending on the change in the interest rate, the growth of the interest rate on the bank’s investment resources causes a reduction in demand, that is, a decrease in the value of the bank’s investments (Kardash, 2002). The bank is a special economic institution that carries out the accumulation of free funds and savings, provision of credits, cash settlements, issue of money, and so on. The main function of the bank is to mediate in lending, that is, mobilization of temporarily free funds and providing them for temporary use in a reimbursable, chargeable, and time-bound manner, to enterprises, the state and the population. Thus, credit resources are the main product offered by the bank. The effectiveness of banking activities depends on the effectiveness of investments. Therefore, the impact of competition and inflation on the bank will be viewed in terms of their impact on investment. Investments reflect the demand existing in the market of credit resources, and the bank’s income reflects the supply of the bank. The total supply in the banking market consists of the proposals of competing banks. The aggregate demand is formed on the basis of the needs of firms and households for the bank’s credit resources. Changes in investment dynamics are related to the interest rate. The interest rate is calculated as the ratio of the annual income on the loan value to its absolute value. The movement of the average interest rate is determined by the ratio of demand and supply of loan capital in the market. The level of interest rate for each particular loan depends on many

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factors. When determining the interest rate, first of all, the loan term is taken into account (Marshall & Bansal, 1998). In practice, the following is generally observed: the longer the loan term, the higher the interest rate. The interest rate depends on the size of the loan. This is due to the fact that with large amounts of the loan increases the risk, the value of which is estimated by the size of the lender’s losses from the insolvency of the borrower. The interest rate also depends on the security of the loan. The rate on loans secured by promissory notes, commodities, and receivables, securities is lower. In addition, the rate varies depending on the purpose of the loan, the borrower, as well as the form of the loan (Petrov, 1993). Interest rates in Russia are currently quite high. This is due to a number of factors that affect their value. The main ones are the following: • constant inflation—banks are increasing interest rates on loans in line with their inflation expectations to conserve their resources, and quarterly rates are revised in commercial banks; • expansion of demand for loans in order to obtain additional means of payment to pay the debt—uncertainty in the economic prospects reduces the interest in raising funds on a long-term basis and increases the demand for short-term loans; • tight monetary policy of the Central Bank of the Russian Federation aimed at curbing the growth of the money supply means a reduction in the supply of loan capital; and • the state budget deficit—for covering the budget, the government of the Russian Federation and local authorities are turning to the loan capital market, increasing the demand for it. Fluctuations in the average market interest rate depend on the stage of the industrial cycle. At different stages of the industrial cycle, the average rate of interest undergoes different variations as described below. At the beginning of the industrial recovery, the rate of interest remains low, despite a significant increase in the rate of return, since at this stage producers use mainly equity rather than debt capital; demand for debt capital is very small, and supply increases (Tokova, 2008). The model of stability of the bank under the conditions of competition and inflation is based on the identity—equality of income and expenditures of the bank.

Yf = C + I f + R , (61.1)

where C = F + G + T is the bank’s expenditures.

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Y = the bank’s income, Yf = income of the bank adjusted for inflation and competition; F = operating expenses of the bank; G = the costs of administration; T = taxes paid by the bank to the budget system; R = the bank’s profit; If = investment, taking into account competition and inflation.

Let us assume that the impact of competition and inflation caused the bank to increase the interest rate on loans. The amount of the bank’s expenses and profit remains unchanged. The income of the bank in this case will be less than the potential income of Y > Yf . This is due to the fact that the growth of the interest rate reduces the growth rate of investment, I > I f under the conditions of competition and inflation. As a result of mathematical transformations, we obtain a linear homogeneous differential equation of the second order,

(1 − s f )τxf + s f − b f a f +(1 − s f )τb f x f + sb f x f = 0, (61.2)

which we will write in a known form:

xf + 2kx f + ω2f x f = 0 , (61.3)

or

x + 2kx + (sb /(1 − s )τ ) x = 0.

(61.4)

where k = an indicator of bankruptcy, taking into account competition and inflation. ω = frequency of banking cycles under the conditions of competition and inflation. CONCLUSIONS The mathematical model of stability of the bank under the conditions of competition and inflation allows • to assess the sustainability of the bank under the conditions of competition and inflation, objectively, and in a timely manner; and

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• to forecast the state of the bank under the changing conditions of competition and inflation. Based on this model, the parameters of stability of the bank Yf , C, If , and R are adjusted for the competition and inflation. Indeed, the decline in investment growth rate occurs as a result of rising interest rates and declining income of the bank. As a result, the efficiency of investment decreases and the propensity to save decreases. In such a situation, it is important to manage the bank’s costs and revenues in order to maintain the stability of the bank under the conditions of competition and inflation. REFERENCES Balabanov, I. T. (2015). Financial analysis and planning of economic entity (2nd ed.). Moscow, Russia: Finance and Statistics. Dolyatovsky, V. A., Kardash, V. A., & Sergeenko G. S. (1998). Models and methods of stochastic control of the firm on the basis of the functioning of intellectually active system. Scientific Notes, 3, 138. RSEU. Kantorovich L. V. (1960). Economic calculation of the best use of resources. Moscow, Russia: SA. Kardash V. A. (2002). Market equilibrium of macroeconomic systems: A constructive approach. Izvestiya VUZov. North Caucasus region, Natural Sciences, 1, 19–23 Kasaeva L. D. (2002). Single-product models of operational management of financial activities of the Bank (Thesis for the degree of candidate of economic sciences). Karachaevo-Cherkessk State Technological Academy, Cherkessk. Marshall, G. F., & Bansal, V. K. (1998). Financial engineering: A complete guide to financial innovation. Moscow, Russia: INFRA-M. Petrov, A. (1993). Mathematical models of economics: Theory and experience of the time of economic reforms. Knowledge–Power, 3. Tokova, L. D. (2008). The nature of circularity in theories of economic equilibrium. Bulletin of the University Sociology and Personnel Management, 6, 136. Zhdanov, S. A. (1999). Methods and market technology of economic management. Moscow, Russia: Business and Service.

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CHAPTER 62

THE ROLE OF OPPORTUNITY COSTS IN THE ORGANIZATION AND PRODUCTION OF MEDICAL AND HEALTH SERVICES USING BLOCKCHAIN TECHNOLOGIES Ekaterina A. Pogrebinskaya I. M. Sechenov First Moscow State Medical University Galina A. Rybina Bauman Moscow State Technical University Valentina V. Kuznetsova Lomonosov Moscow State University

ABSTRACT This chapter is aimed at systematization of opportunity costs of blockchain technologies implementation in medicine and healthcare, including trans-

Meta-Scientific Study of Artificial Intelligence, pages 549–556 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

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550    E. A. POGREBINSKAYA, G. A. RYBINA, and V. V. KUZNETSOVA action costs of blockchain application. The examples show the relationship between the transaction costs to ensure the use of blockchain technologies in medicine and health care and safety. An institutional approach to balancing the interests of blockchain stakeholders in medicine and health care in order to minimize costs is proposed.

The organization of production of services in medicine and health care based on the use of blockchain technologies is very promising due to the exponential growth in the volume of information. The very essence of the blockchain as a systematic database stored not on one server, but replicated on thousands of computers, is in line with the tasks of modern medicine. An additional advantage is the accumulation of information in the information blocks, thus making it possible to create a chain of interdependent blocks which are duplicated on all computers of the system. The information is only multiplied, but is not lost or distorted. And the organization of all medical processes and technologies involves positive externalities of networks: 1. independence from time and round-the-clock access (any doctor can receive the necessary information about the medical history and medical policy, immediately transmit information directly to the insurance funds, synchronize the queue for treatment, and receive the necessary data on the diagnosis based on the history of medical appointments and developments in the field of medicine); 2. independence from the location of the computers on which the processed information is stored; 3. resistance to modifications of the data (reliable provision for data protection for management of digital medical records with the results of tests, examinations, treatment, vaccinations, and medicines used); 4. integrity (despite the fact that some parts of the networks operate independently, but in a single information space, the blockchain blocks changes in time retroactively); and 5. high speed of data transmission. METHODOLOGY If we use the analogy in the sphere of public services and banking, in the long term, the application of blockchain should lead to increased availability and reduction of the price of medical services, as well as to the release of a huge number of personnel and the reduction of the army of intermediaries in the medical and paramedical markets. There will be a possible reduction in the transformational costs of using blockchain technologies in medicine and health care.

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However, such advantages of the organization of medical services are accompanied by high opportunity costs, especially transaction costs of ensuring the safety of blockchain users. The modern theory of neo-institutionalism asserts that transaction costs are inherent in all types and mechanisms of activity coordination. In relation to blockchain technologies, the classification of types of transaction costs by neo-institutionalists is as follows: (a) the cost of using a market mechanism to coordinate the interests of users of blockchain technologies, (b) intra-organizational costs of medicine and health, and (c) political transaction costs. The first type of cost is associated with positive network externalities, where each additional user of network benefits increases the utility for others (Koblova, 2015). The greater the number of participants in the blockchain network, the more its value and the information it accumulates. It will be less valuable if the users of the blockchain are only clinics, only pharmacies, or only patients. In the classical theory of network benefits, it is believed that the willingness to pay each subsequent user increases as new and new participants connect to the network. But after reaching its maximum value, the willingness to pay falls, since everyone who was ready to pay more has already joined the network, that is, the network has reached its optimal size. In accordance with the law of Metcalfe (1993), the value of any network for the user is equivalent to the square of the number of connection nodes. If the network has n users, and the value of the network for each user is proportional to the number of other users, the total value of the network is proportional to the value determined by the equation (n + 1) = n² + n. The most difficult question is which mass can be considered a critical one. In addition, the information accumulated using blockchain technologies can be represented abstractly as a large file stored by all users of the blockchain network. This “file-base” records all transactions; it is constantly growing with the addition of new users. This provides such a blockchain element as the peer-to-peer networking. Costs of the second type can be considered as an approximation of the trap effect—the effect of moving costs from one industry to another. This is due to the growing interdependence of industries, the undoubted growth of transformation costs, and even greater growth of transaction costs. The main causes of the trap effect were described by American economists Shapiro and Varian. In the case of medicine and health care, they also take place (Starr, 1982). 1. Existing contracts and agreements (termination of which is related to additional costs). Since the beginning of the 20th century, doctors have limited their use of information to studying the symptoms and medical records of their patients in order to understand the

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2.

3.

4.

5.

picture of diseases. At the beginning of the 21st century, as in the 20th century, doctors continue to maintain the patient’s medical records, performing a medical examination, collecting anamnesis and physical examination results in the patient’s file. Having established a preliminary diagnosis and excluded other possible ones, doctors refer patients for selected tests and procedures of the clinical laboratory, radiology and other clinical diagnosis and support services. After reviewing the information received from these services, doctors usually make a more specific diagnosis and then prescribe appropriate treatment. In the case of an unusual disease or a complex medical problem, doctors can refer the patient to the appropriate clinic, where specialists can also use evidence-based reports on the relevant treatments described in the relevant medical literature and bibliographic databases (Frawley, Piatetsky-Shapiro, & Matheus, 1992). Training (requires time and increases costs during the transition from a conventional system to a blockchain system). This entails not only the introduction of more advanced requirements for the competence of doctors (with the ability to work with a global database of diagnoses), but also training for using technical support and AGILE. Positions held sometimes by independent contractors and temporary workers, are characterized by an abundance of short-term and nonrecurrent work, often allowing them to work away from the office on the basis of remote access to digital platforms. Conversion of blockchain information (transition to another file format). In case the database is hacked and the information is lost or damaged, it is necessary to back up the data and, preferably, with the use of other copy formats. Connection costs are directly associated with additional time and personnel for entering information, with the solution of the problem, whom to entrust it, how to avoid errors when entering, and how, if they are still made, to correct them. The cost of loss of loyalty (the consumer is deprived of discounts and preferences in relation to the previously supplied goods, the individual attitude of the doctor, the sympathy from the medical staff and civil servants, especially in the case of serious diseases). Patients fully recognize the risks of creating false medical files to sell drugs for their own enrichment. Medical and health care services turn from credence goods to standardized, impersonal (when the computer diagnoses the patient and the robot does the surgery).

The problem of the third type of costs is not so obvious. This is not the result of chance: Digital technologies are considered the most current

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official direction of development, supported by the Ministry of Health of Russia, including the unified state information system of health. Also, the Federal Law No. 323-FZ, “On the basics of health protection of citizens of the Russian Federation” focuses on the standards of medical care on the basis of electronic statistics. However, the blockchain itself is a fairly cheap technology that significantly reduces the cost of medical transactions. The introduction of blockchain can cause an incomparable opportunistic attack of officials, not only receiving huge salaries, but also appropriating administrative markup. RESULTS Transaction costs that arise in ensuring the safety of the use of blockchain technologies in medicine and health care can be considered in the example of the list of honore (Kapelyushnikov, 1990). Since each of the powers has some value in the list of honore, their consideration explains the behavior of economic agents and the motivation of all blockchain stakeholders: 1. The right of ownership, the essence of which is in the protected possibility of physical control over the object of ownership, in the case of blockchain in medicine—the entire mass of useful information. In 1958, in the article “Federal Communications Commission,” Coase considered the issue of influence of transaction costs on property rights (Coase, 1959). 2. The right to use, which is to derive personal benefit, involves the cost of controlling authorization and, in a global sense, the maintenance of facilities and protection against unauthorized access. 3. The control right, which includes the ability to determine the direction in which this object can be used, as well as the circle of persons and the order of access to the “blockchain file,” is blurred due to the above effects. 4. The right to income that can result from the direct use of blockchain technology is very difficult to expropriate and protect directly. Therefore, there is an externalization and an increase in transaction costs. 5. The right to capital in relation to the “blockchain file” is also ambiguous. On the one hand, the blockchain in the field of medicine and health care should be a public good, but changing its form or destruction can be used by an attacker with the necessary capacities. 6. The right to security or immunity from expropriation. Proof of the precariousness of the blockchain structure in relation to expropriation is the example of the Hollywood Presbyterian Medical Center

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hospital, whose management in 2016 paid an analog of $17,000 in bitcoins to ransomware that infected the hospital’s computers with a cryptographer, since this was the fastest and most painless way to solve the problem. Similarly, in 2018, the administration of the Hancock Health hospital in America paid cybercriminals $55,000 to unlock systems after being infected with a ransomware program SamSam in order to save time that would have taken to decrypt. In Israel, in 2018, an attack by the worm WORM_RETADUP on some clinics led to the transfer of information about the system to external sites, the failure of some types of equipment, and the WannaCry virus had a devastating impact on the UK health system. Security for blockchain users is the first topic that is discussed among its disadvantages in medicine and healthcare. And there are no options to reduce transaction costs. 7. The right to transfer a thing by inheritance is transformed into an understanding of blockchain in this industry as an element of social capital, and the continuity of the whole society in its growth. 8. The timeless nature, which means the absence of any time limits in the exercise of powers. With the regard of the blockchain, the longer the time horizon, the higher its value for society as a whole. The visible limitation is the capacity required for exponentially growing information. In recent years, the increase in data has been exponential (Henke et al., 2016). The UN defines high-quality data as “a source for decision-making and a raw material for reporting” (UN Data Revolution Group, 2014). Data management transforms business models by enhancing personalization of services and products, communication, sharing of assets and collaboration, as well as pricing based on data usage (Stelios, Ladas, & Loch, 2016). The explosive increase in data requires new ways of managing it, not only at the company or organization level, but also in society as a whole. 9. The prohibition of harmful use is related to a “negative” right that does not allow the use of an object in a way that is related to damage to the property of other agents. Medical databases create the possibility of bio-power, the concept of which was introduced by M. Foucault in 1976 (see Foucault, 2011). The transaction costs, ensuring this right, are almost limitless, since they require the connection of total tools and control mechanisms, which is impossible in an uncontrollably growing network of users.

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CONCLUSIONS The spread of blockchain in medicine and health care reduces the practice of medical errors and their denial, to contribute to fundamental research and R&D, coordinating the actions of researchers and making information about the latest achievements concrete, accessible, and transparent. On the other hand, databases and blockchain can be used by global biological terrorism: abuse of pharmaceutical TNCs, development of genetically oriented viruses, directed anthropogenic sabotage can lead not only to huge economic damage, but also put humanity on the brink of survival. Individual threats are no less perilous, have deep socioeconomic roots and can reach a gigantic scale: pirate donation and kidnapping “on a tip,” reputational losses, economic losses, pressure of aggressive advertising with violation of consumer immunity. For example, the problem of trafficking in human organs is a huge gap between the demand for transplantation services and the legal supply of donor organs. But even within the “nonmonetary” economic relations that accompany the provision of information security of medical databases and blockchain, there are a lot of contradictions. Creation and development of databases in modern health care is a process that requires continuity and consistency. This entails large costs. However, blockchain technologies should be as transparent and diffusible as possible, taking the form of a public good. The contradiction lies not only in the answer to the question, “Who should pay for these databases and why?” but also in the distribution of the cost burden between the users of databases who are residents of different countries and different social categories. Ensuring continuous access to these databases is another problem, connected with information, technologies, and economic aspects. An essential element of the functioning of the system of specifications of the rights of the blockchain in medicine and minimizing the transaction costs of its users is the behavior aimed at rental capture, or rent-oriented behavior. The effect of income, or wealth, creates incentives to invest in the decision-making process for the distribution of legal rights, which in the context of positive transaction costs leads to a decrease in the value of the blockchain, since the appropriation of the benefits to one can be accompanied by costs for many. Conversely, the situation with the appropriation of benefits from blockchain technologies by society as a whole may entail opportunity costs of a scale that no government or TNC can cope with. The only visible solution at the moment is to limit the number of institutional participants involved: insurers, clinics, and pharmaceutical manufacturers.

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REFERENCES Coase, R. (1959). The Federal Communications Commission. Journal of Law and Economics, 2(1). https://www.jstor.org/stable/724927 Foucault, M. (2011, October 8). Security, territoire, population: Lecture cycle. Retrieved from http://gtmarket.ru/laboratory/doc/6693 Frawley, W. J., Piatetsky-Shapiro, G., & Matheus, C. J. (1992). Knowledge discovery in databases: An overview, AI Magazine, 57–70. Henke, N., Bughin, J., Chui, M., Manyika, J., Saleh, T., Wiseman, B., & Sethupahty, G. (2016, December 7). The age of analytics: Competing in a data-driven world. Retrieved from https://www.mckinsey.com/business-functions/mckinsey -analytics/our-insights/ the-age-of-analytics-competing-in-a-data-driven-world Kapelyushnikov, R. I. (1990). Economic theory of property rights. Moscow, Russia: IMEMO. Koblova, Yu. A. (2015). Evolution of mental models in the information network society. Information Society, 2–3, 32–38. Metcalfe, B. (1993, August 16). Wireless computing will flop—Permanently. InfoWorld, 15(33), 48. Retrieved from https://books.google.ru/books?id=q jsEAA AAMBAJ&lpg=PA34&dq=x-terminal&as_pt=MAGAZINES&pg=PA48&redir_ esc=y#v=onepage&q=x-terminal&f=false Starr, P. (1982). The social transformation of American medicine. New York, NY: Basic Books. Stelios, K., Ladas, K., & Loch, C. (2016, October). The transformative business model. Harvard Business Review. Retrieved from https://hbr.org/2016/10/ the-transformative-business-model UN Data Revolution Group. (2014). A world that counts (Independent Expert Advisory Group on a Data Revolution). Retrieved from http://www.undatarevolution.org/report/

PART VI MODERNIZATION OF MANAGEMENT OF PRODUCTION AND DISTRIBUTION PROCESSES AND SYSTEMS BASED ON ARTIFICIAL INTELLIGENCE

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CHAPTER 63

STUDY OF RESTRUCTURING STRATEGIES Decentralization of Management and Enterprise Structure Ekaterina P. Garina Minin Nizhny Novgorod State Pedagogical University Elena V. Romanovskaya Minin Nizhny Novgorod State Pedagogical University Natalia S. Andryashina Minin Nizhny Novgorod State Pedagogical University Elena P. Kozlova Minin Nizhny Novgorod State Pedagogical University Anastasia D. Efremova Minin Nizhny Novgorod State Pedagogical University

Meta-Scientific Study of Artificial Intelligence, pages 559–565 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

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ABSTRACT The research topic is relevant because of the need to study and implement modern organizational and managerial strategies, for a restructuring of industrial enterprises. Improvement of organizational and production structure, which allows to minimize inside costs, save resources, and improve the quality of management of the company and its efficiency production structure. This is possible on the basis of the disaggregation of enterprises, their structural and functional reorganization, and formation of new forms of production units and associations. We have studied forms of management proposals for the implementation of strategies and algorithms for determining the choice of organizational strategies for the effective development corporations. The experience of a downsizing of advanced enterprises, developed schemes, methods, packages of standard regulatory materials, and regulating the activities and relationships of production units, makes it possible to build a strategy, not from scratch, but adapting proven principles and downscaling technologies to their own specific conditions. The scientific problem addressed by this study is to identify the theoretical contradiction between the existing practice of reforming domestic enterprises and modern strategic approaches in realizing the competitive advantages of production development.

The main objective of restructuring in the strategic plan is the development of business in the industry and the creation of a system of effectively and sustainably functioning enterprises, capable, at the same time, of active and harmonious interaction with the market environment. The restructuring of the enterprise does not fully fit into any of the existing types of strategy—the traditional ones include product market, resource market, technological, integration, financial investment, social, and management strategies. At the same time, according to scientists (Garina, Kuznetsova, Garin et al., 2017), there is no need to assign it to an independent form. By the term restructuring strategy, economists propose to refer to a group of interrelated decisions, adoption of which in the course of restructuring may affect strategic positions and plans. According to some scientists, the process of institutional restructuring of industry in Russia has been actively developing since the early 1990s. At the same time, in their opinion, the dominant direction of modern restructuring is the tendency of diversification disintegration, by disaggregation of industrial enterprises and creation of branched financial and industrial structures of the planetary type. Such structures include a number of productions, trade, and services such as small enterprises like satellites of an industrial joint-stock company. The structures are formed by a decision of the management of the parent enterprise, and the establishment of small related enterprises allows the main to solve a significant number of their problems such as financial or supply. The objective of the study is the development of conceptual and theoretical provisions of the implementation

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of the purposeful process of managed strategic restructuring at domestic industrial enterprises (Andrashina & Garin, 2016). METHODOLOGY Modern trends in the reform of domestic production can be explained by the low level of competitiveness of Russian industrial enterprises. According to Bengt Karlöf (Karlöf & Lövingsson, 2005), the lower a level of pressure of competitors, the less efficiency of operations and the greater a temptation to diversify. Thus, traditionally protected industries of Russia, such as air transportation, energy, telecommunications, and so on, perform work in such a way that, in case of strong competition in a market, it is delegated to specialized companies. Foreign experience shows that in order to ensure competitive advantages, Russian enterprises at the present stage need to refuse integration of production. It is necessary to create a more effective and flexible management system through the disintegration of the joint stock parent company, through the implementation of diversification disintegration strategies (Mizikovsky, Miloserdova, & Sofin, 2014). However, the feasibility of disintegration is equally ambiguous. By themselves, processes of disintegration and integration of a particular company require as part of the practiceoriented, process-driven strategic restructuring of compulsory study of organizational and economic restructuring mechanisms, functioning structures created, as well as economic consequences (Chelnokova & Nabiyev, 2015). The choice of an optimal strategy (from the existing integration and disintegration) and the scope of the restructuring of a particular enterprise, according to Kleiner (Andrashina & Kozlova, 2016), should be preceded by the process of his economic positioning. However, as noted by Ayvazyan, Balkind, and Basnin, such sequence is rarely implemented in practice. This is due to an insufficiency of methodical support of the process of restructuring enterprises and weakness of financial capacities of enterprises. According to Garina, Garin, and Efremova (2016), A common situation is when enterprises prepare a new structure of redistribution of management functions without elaboration of a comprehensive strategy that takes into account features of functioning enterprise. This can lead to instability of the structure and, as a consequence, to degradation of the functions of the enterprise.

In the course of developing a restructuring strategy, according to experts Markova and Narkoziev (2018) and Garina, Kuznetsova, Romanovskaya, et al. (2017), each enterprise solves two main problems: a selection of the best organizational structure and functional structure, that is, a division of

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production and management functions among units (Garin, Garina, Sokolova, & Emelyanova, 2018). A separate problem in the development of a strategy of restructuring of the enterprise is the distribution of management functions by divisions of the enterprise, in particular: functions of management of assets and human resources of the enterprise; relations of the enterprise with external social and economic subjects and environment; relations of the enterprise with owners; management of the target and motivational sphere of the enterprise (definition of the mission of the company, formulation of strategy, goal-setting, education and consolidation of corporate values, etc.). RESULTS Business reform in Russia implies the creation of a system of effectively and sustainably functioning enterprises capable of adapting to changes in external administrative and business, social, and economic environments (Mizikovsky, Druzhilovskaya, Druzhilovskaya, Garina, & Romanovskaya, 2018). According to experts, as a result of the restructuring process, a place of the enterprise in a structure of market relations should be determined, the dominant direction of restructuring (disintegration or integration), and should solve three main tasks: 1. establishment of rational boundaries of the enterprise, including composition and volume of material, financial, and human resources; 2. determination of internal organizational and managerial structure of the enterprise and organizational and economic mechanism of management and interaction of allocated business units; and 3. formation of a friendly integration environment of the enterprise (Schätz, 2016). According to scientists, Russian business firms in their development go through several stages, each of which has certain characteristics. Table 63.1 summarizes the main characteristics of each cluster. The first cluster is the stage of formation, which is characterized by a smallsized company, a high level of centralization, lack of formalization; the age of the company is no more than 4 years. The second cluster is the stage of growth, the companies at this stage are older (up to 10 years) and the first signs of formalization appear; the size of the company and the number of hierarchical levels increase (Kuznetsov & Romanovskaya, 2011). The third cluster is the formalization phase, it includes companies aged 10 to 15 years, and most of them demonstrate a higher level of formalization, larger size, and lower level of centralization in relationships with increasing the size and number of hierarchical links (Garina, Shpilevskaya, & Andrashina, 2016).

Study of Restructuring Strategies    563 TABLE 63.1  Main Characteristics of Companies by Cluster Cluster 1 The Stage of Formation

Cluster 2 Growth Stage

Cluster 3 Stage of Formalization

Age

1–4 years

4–10 years

10–15 years old

Size

Small companies from 3 to 100 people

Medium-sized companies from 100 to 250 people

Medium and large companies from 250 to 500 people

Level of Formalization

Low

Medium, appear first documents

High

Number of Hierarchical Levels

1–2

2–3

2–4

Simple, functional

Functional, mixed

Functional, divisional, matrix

Rank Centralization

High

Average

Poor

Key Tasks of Development

Creation of reputation, solving personnel issues

Ensuring stability, reputation creation

Ensuring stability, uniqueness

Change of Market Share

Small increment

Essential increment

Slight increment either reduction

Sales Volume

Less than 50–100 thousand $

From 50 thousand to 2 million dollars

From 500 thousand to 3 million dollars

10–30%

30–50%

10–30%

Local, regional

Local, regional, national

Regional, national, international

Criteria

Frequently Used Organizational Structure

Volume Growth of Sales Markets

CONCLUSIONS There are some generally accepted, though not absolute, recommendations based on the study of business patterns. For example, a division structure is the most commonly used for diversified companies. Mono-product companies and firms with a dominant product prefer a technological or functional structure (Lebedeva & Garin, 2017). A choice between these types of structures depends on national traditions and peculiarities of national psychology. The comparative analysis by country and the dynamics of organizational structures in Japan, the United Kingdom, and the United States from the 1950s to the 1980s is shown in Table 63.2. The table shows that the share of purely functional structures in all countries is declining, the most dramatic in the United States.

564    E. P. GARINA et al. TABLE 63.2  Comparative Analysis and Dynamics of Types of Organizational Structures at Enterprises of a Number of Countries Japan Structure Type

1970

1980

Great Britain 1990

1970 7

1990

United States 1970

1990 11

Functional

53

45

42

2

63

Functional division

—

—

—

—

—

13

9

Territorial division

780

137

80

710

20

—

—

Product divisional

40

43

44

20

76

72

72

Holding company

0

0

0

30

21

4

2

According to researchers, the holding form, which is now considered as one of the most promising in Russia, is focused not so much on meeting the needs of a certain part of the market and profit from it, but focused on personal power and a group of leaders. It seems that after some time, this form will no longer be a priority for Russia. Enterprises focused more on current market needs than on technological advantages tend to use a divisional type. If the competitive advantage is to be derived from a high level of technology or intellectual resources, a horizontal organization is desirable. It should also be noted that the divisional organization is very sensitive to changes in the product range (if a divisional-product structure is adopted) or markets (divisional-market structure). As for the distribution of management functions by enterprise divisions, in the course of structuring, first of all, it is necessary to answer the question: What is a possible object of redistribution? REFERENCES Andrashina, N. S., & Garin, A. P. (2016). Evaluation of the complex development of the product on the basis of modern methods of quality management (on the example of separate production). Economic and Humanities, 4(291), 74–88. Andrashina, N. S., & Kozlova, E. P. (2016). Rationalization of production as a way to sustainable development of the enterprise. Scientific Review, 21, 173–176. Chelnokova, E. A., & Nabiyev, R. B. (2015). Tutor activity of the teacher to ensure successful adaptation of university students. Bulletin of Mininsky University, 3(11), 23. https://vestnik.mininuniver.ru/jour/search/search Garin, A. P., Garina, E. P., Sokolova, K. K., & Emelyanova, A. M. (2018). Studying the peculiarities of the organization of production systems by domestic enterprises. Economics and Entrepreneurship, 7(96), 1050–1053. Garina, E. P., Garin, A. P., & Efremova, A. D. (2016). Research and generalization of design practices of product development in the theory of sustainable development of production. Humanities and Socio-Economic Sciences, 1(86), 111–114.

Study of Restructuring Strategies    565 Garina, E. P., Kuznetsova, S. N., Garin, A. P., Romanovskaya, E. V., Andryashina, N. S., Suchodoeva, L. F. (2017). Increasing productivity of complex product of mechanic engineering using modern quality management methods. Academy of Strategic Management Journal, 16(4), 8. Garina, E. P., Kuznetsova, S. N., Romanovskaya, E. V., Garin, A. P., Kozlova, E. P., & Suchodoev, D. V. (2017). Forming of conditions for development of innovative activity of enterprises in high-tech industries of economy: A case of industrial parks. International Journal of Entrepreneurship, 21(3), 6. Garina, E. P., Shpilevskaya, E. V., & Andrashina, N. S. (2016). Studying approaches to the definition of high-tech product in production. Bulletin of Mininsky University, 1–1(13), 3 Karlöf, B., & Lövingsson, F. H. (2005). The A–Z of management concepts and models. London, England: Thorogood. Kuznetsov, V. P., & Romanovskaya, E. V. (2011). Analysis of methods of the restructuring of industrial enterprise in modern conditions. Bulletin of Cherepovets State University, 2(29), 59–62. Lebedeva, Y. M., & Garin, A. P. (2017). Disintegration of material production: Applied level. In The collection: Social and technical services (p. 315–318). Presented at III All-Russian Scientific-Practical Conference. Nizhny Novgorod State Pedagogical University, Nizhny Novgorod, Russia. Markova, S. M., & Narkoziev, A. K. (2018). Industrial training as an integral part of professional training of future workers. Bulletin of Mininsky University, 6(1). https://doi.org/10.26795/2307-1281-2018-6-1-4 Mizikovsky, I. E., Druzhilovskaya, T. Y., Druzhilovskaya, E. S., Garina, E. P., & Romanovskaya, E. V. (2018). Accounting for costs and expenses: Problems of theory and practice. Advances in Intelligent Systems and Computing, 622, 152–162. Mizikovsky, I. E., Miloserdova, A. N., & Sofin, A. A. (2014). Formation of the decision-making process on the organization of auxiliary works. Modern Problems of Science and Education, 5, 297. Schätz, C. A (2016). Methodology for production development (Doctoral thesis). Norwegian University of Science and Technology, Trondheim, Norway.

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CHAPTER 64

THE PRINCIPLE OF SUBJECTIFICATION IN ASSESSMENT OF THE MANAGEMENT PERFORMANCE OF THE ORGANIZATION IN TERMS OF THE DEVELOPMENT OF ARTIFICIAL INTELLIGENCE Natalia A. Zhuravleva Emperor Alexander I St. Petersburg State Transport University Martin G. Grigoryan Emperor Alexander I St. Petersburg State Transport University

Meta-Scientific Study of Artificial Intelligence, pages 567–573 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

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ABSTRACT This chapter provides an overview of various means of artificial intelligence (AI) widely used in the field of economics and management. The analysis of publications on the use of AI reveals the problem of determining the modalities for assessing the management performance of the organization, which should be taken into account in the design of intelligent information systems to ensure the credibility of such an assessment of stakeholders. The research methods used in the chapter, such as comparative analysis, structuring of processes, and logical analysis of cause-and-effect relationships, allowed formulating the principle of differentiation of the assessment of the effectiveness of management of business organizations. A key point in assessing the effectiveness of management of transport organizations from the standpoint of regional interests is highlighted, as the activity of these organizations is a tool of regional integration, the rational use of which to some extent depends on both the socioeconomic integrity of the region and the reliability of its interactions in the external environment.

The problems of finding adequate systems for assessing the effectiveness of business organizations to ensure management decision-making based on the technologies of the new technological mode are the subject of active scientific discussions. The field of study of intelligent decision support systems (IDSS) was identified in the 1980s of the 20th century, but with the development of intelligent information systems, the validity of such assessments increases significantly. In particular, a number of authors such as Phillips-Wren, Mora, Forgionne, and Gupta (2019) proposed a model with the use of an AI system, the implementation of which allows the user both to obtain the necessary information with its help, and to make effective decisions in the field of urban infrastructure management (Phillips-Wren et al., 2009). Significant developments are being made in the area of big data analysis and the use of the information obtained to improve the management of organizations. In particular, Jarrahi (2018) and Bratasanu (2018) believe that with the use of robotics, analytical tools are being actively expanded, while noting that AI should multiply the result without replacing the intellectual contribution of man. In recent years, there has been an emerging body of research aimed at the possibility of using AI in the analysis of information about the state of the financial market. Thus, the paper Marwala and Hurwitz (2017) examines the impact of AI on the efficient market hypothesis. There the authors apply AI in the development of methods of portfolio theory (asset management), which allowed us to propose a model of changes in portfolio prices with greater accuracy than in the mathematical formalization of Markowitz. It is noted that in the proposed model of portfolio price dynamics, the set of incoming assumptions is expanded, and the information obtained as a

Assessment of the Management Performance of the Organization    569

result of modeling ensures that the management of companies makes effective decisions in choosing the optimal portfolio of assets. The results of numerous studies in the field of AI systems and robotics have led to the formation of a new wave of technological innovation. For example, a smart factory will be able to use a fully integrated corporate production system that allows in real time to respond to changing production conditions, the requirements of supply chains, and to satisfy customers’ needs. In this case, the management performance of the organization will depend on the position of the organization and its products on the market compared to other organizations and their products. Problems arising in the process of organization of smart manufacturing are identified and disclosed by Sjodin, Parida, Leksell, and Petrovic (2018). In general, the presented brief review shows that various means of AI are widely used in the field of economics and management. However, there is an open question about the conditions for assessing the management performance of organizations, which should be taken into account in the design of intelligent information systems to ensure the credibility of such an assessment of stakeholders. METHODOLOGY In accordance with the above premise, the purpose and methods of research used in the preparation of the chapter were determined. The purpose of the study is to identify and disclose the key conditions for a differentiated assessment of the effectiveness of transport organization management, which should be taken into account in the design of intelligent information systems. In the course of work on this chapter, the authors used such research methods as comparative analysis, structuring of processes, grouping, expert analysis of publications on the use of artificial intelligence, and logical analysis of cause-and-effect relationships. Special attention was paid to the content analysis of methodological materials on the problems of measuring the management performance of transport organizations. The synthesis of theoretical developments and practical experience allowed critically rethinking the possibility of setting up intelligent information systems, as well as to justify proposals for a differentiated assessment of the effectiveness of management of the transport organization, which should be taken into account in the design of intelligent information systems to correspond to the economic positions of stakeholders.

570    N. A. ZHURAVLEVA and M. G. GRIGORYAN

RESULTS One of the difficulties in assessing the effectiveness of the organization’s management is that it should not consist only in determining the value and dynamics of its financial performance. If we confine ourselves to such an assessment, we can, of course, draw a general conclusion about the effectiveness of management, but it is impossible to determine the area of management where the greatest number of unsuccessful or particularly successful management decisions are made, to detail the content of these decisions and, as a result, to develop a specific program for improving management efficiency (ISO Consulting, 2015). Due to the limited size of the publication, we will consider only a few examples of the choice of indicators used in the differentiated assessment of the performance of the organization’s management, correlating this choice with the purpose of evaluation. 1. If the general purpose of the evaluation is to select the most effective methods to improve the functional stability of management, then at the first stage of evaluation it is advisable to pay attention to the performance of each of the main functions of management (organization, motivation, control, etc.). If analysts have information that allows them to predetermine the priorities of the analysis, then the management function that is most relevant to the organization at the time of evaluation may be subject to assessment. The system of evaluation indicators should be formed taking into account the industry specifics. 2. In the implementation of the principles of behavioral economics, which implies the focus of evaluation which is directed to identify stuffing capacities for performance improvement of management, you should pay attention to the results of the work of individual performers or individual management services. 3. In the case where the improvement of organization’s sustainability is the criterion for assessing the effectiveness of management, the degree of management impact which ensures the preservation and development of the basic system properties of the organization is determined (Grigoryan & Kononova, 2015). 4. Fairly traditional indicators of efficiency of the management are used in the evaluation of individual segments of management activities, that is, performance indicators related to property management of the organization, personnel management, cost control, and so on. 5. Let us consider in more detail the features of assessing the management effectiveness of the organization performed from the posi-

Assessment of the Management Performance of the Organization    571

tion of the interested party. As a concerned party may be clients of the organizations, organs of state regulation of transport activities, credit organizations, tax service, and, of course, the region within which the organization operates. In this regard, the following grouping of performance indicators seems to be appropriate: • A group of indicators reflecting the situation with regard to life safety in the region. This includes the scale and nature of pollution of the environment of the region resulting from transport activities; the number of road accidents, compared with the size of region’s population; the level of reliability of transport services in crisis situations (the need for urgent medical care, accidents in the urban system, natural disasters, etc.). • a group of indicators that can be used to assess the degree of satisfaction of the transport needs of the population of the region, which is to some extent related to the solution of the problem of social integration. These are indicators such as the regularity of public transport, the waiting time for transport in course of different types of trips, the average speed of trip, the quality of services of suburban, intercity, and international transport. • A group of indicators, the analysis of which allows to determine how the management performance of transport organizations affects the economic potential of the region. These are the timeliness and speed of delivery of goods to enterprises of other industries (regional and located in other territories), the safety of goods, ensuring the regulatory functioning of transport corridors, the reliability of regional logistics systems, achieving the competitiveness of regional transport organizations in the interregional and international transport markets. The indicators listed here characterize the probability of disruption of economic communication channels, both within the region and the region’s communication with the subjects of its external environment. • A group of indicators characterizing the impact of transport activities on the comfort of the technosphere in the region. This group can include such indicators as: the timeliness of waste removal service, the quality of cleaning of residential areas, and the adaptability of vehicles to serve people with disabilities. Selected signs for grouping the indicators analyzed in assessing the effectiveness of the management of organizations from the standpoint of regional interests do not contradict the provisions adopted by the UN member

572    N. A. ZHURAVLEVA and M. G. GRIGORYAN

states in formulating the goals of sustainable development of social and economic systems (United Nations General Assembly, 2015). CONCLUSION In conclusion, we note the following. The review carried out in the chapter shows that at the moment the means of AI are widely used in various fields, in particular in the management of business organizations. In this regard, the question remains about the conditions for assessing the management performance of organizations, which should be taken into account in the design and further configuration of intelligent information systems to ensure the credibility of such an assessment of stakeholders. When determining the conditions for assessing the management effectiveness of transport organizations, whose activities are significantly changing under the influence of intelligent technologies, it was taken into account that such an assessment should be differentiated depending on the purpose and subject of the assessment. According to this thesis, such type of assessment should be referred to the subjective one, which differentiation is based on the principle of relativism. When monitoring the performance indicators of management of transport organizations from the position of regional interests, it is advisable to justify the sample of analyzed indicators in view of the relevance of the problems of the region and taking into account the degree of their information openness. It should be borne in mind that in the transition to a new technological mode, transport organizations are undergoing many changes. This is the transformation of the consumer into a dynamic network of transportation needs, a departure from intermodal competition to cooperation with the widespread continuous generation of data that become a key intangible asset necessary to create added value. Intelligent technologies, first of all robots and robotization are a significant part of the change in the activities of transport organizations, which fundamentally changes both business processes and the effectiveness of their management. REFERENCES Bratasanu, V. (2018). Leadership decision-making processes in the context of data driven tools. Quality-Access to Success, 19, 77–87. Grigoryan, M. G., & Kononova, G. A. (2015). Choice of methods of balanced management of transport organization. Transport business of Russia, 6, 119–122.

Assessment of the Management Performance of the Organization    573 ISO Consulting. (2015). Quality Management Systems: Fundamentals and dictionary. Moscow, Russia: Standartinform. Retrieved from http://www.isoconsulting. ru/images/legislation/GOST_R_ISO_9000-2015.pdf Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-Al symbiosis in organizational decision-making. Business Horizons, 61(4), 577–586. https://doi.org/10.1016/j.bushor.2018.03.007 Marwala, T., & Hurwitz, E. (2017). Artificial intelligence and economic theory: Skynet in the market. Cham Switzerland: Spring. https://doi.org/10.1007/978-3-319-66104-9 Phillips-Wren, G., Mora, M., Forgionne, G. A., & Gupta, J. N. D. (2009). An integrative evaluation framework for intelligent decision support systems. European Journal of Operational Research, 195(3), 642–652. https://doi.org/10.1016/j .ejor.2007.11.001 Sjodin, D. R., Parida, V., Leksell, M., & Petrovic, A. (2018). Smart factory implementation and process innovation: A preliminary maturity model for leveraging digitalization in manufacturing moving to smart factories presents specific challenges that can be addressed through a structured approach focused on people, processes, and technologies. Research-Technology Management, 61(5), 22–31. https://doi.org/10.1080/08956308.2018.1471277 United Nations General Assembly. (2015). Transforming our world: The 2030 Agenda. Retrieved from https://sustainabledevelopment.un.org/about/dsd

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CHAPTER 65

QUALITY ASSESSMENT AND IMPROVING THE EFFICIENCY OF RESOURCE MANAGEMENT OF THE INDUSTRIAL ENTERPRISE Ekaterina P. Garina Minin Nizhny Novgorod State Pedagogical University Victor P. Kuznetsov Minin Nizhny Novgorod State Pedagogical University Alexander P. Garin Minin Nizhny Novgorod State Pedagogical University Natalia S. Andryashina Minin Nizhny Novgorod State Pedagogical University Elena V. Romanovskaya Minin Nizhny Novgorod State Pedagogical University

Meta-Scientific Study of Artificial Intelligence, pages 575–581 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

575

576    E. P. GARINA et al.

ABSTRACT The relevance of this research topic is due to the fact that cash management of process activities during the implementation of investment projects is an important part of the management system of an industrial enterprise and has a significant impact on the formation of final economic indicators. A typical manufacturer spends 60% on material support for project activities, working with counterparties, therefore, even relatively small improvements in this area, such as management of receivables, work improvement with contractors, rationing and standardization of logistic flows, development of documented procedures for process activities, and others, can bring significant benefits. The object of the research is the Gorky Automobile Plant (GAZ) Group, a leading manufacturer of commercial vehicles in Russia, one of the 10 largest European manufacturers of commercial vehicles. The subject of research is the management system of the group in terms of methods, techniques, and tools, as well as controlling cash and other resource flows in process activities. The main goal of the present study is to develop practical recommendations for improving the management of resource flows in process activities in investment projects, as well as controlling as an instrument for effective management of the industrial enterprise sector in mechanical engineering.

Management of process activities is accompanied by tracking the issues of identifying and minimizing risks in creating an industrial product, including 1. support for investment projects, control of process outsourcing service providers to create a product, and prevent possible damage when working with design documentation; and 2. proposals for work improvement. The monitoring system for managing cash flows of process activities in the implementation of investment projects is based on the following main areas: organization of effective control over the execution of regulatory documents on investment projects implementation; analysis of the rational use of financial, material, intellectual resources, making suggestions for improvement of work; verification of activities of structural units related to the creation and implementation of investment projects; verification of received information about the inadequate implementation of decisions on the investment program, abuse of official position, irresponsibility, and incorrect actions of those responsible for the implementation of investment projects (Schatz, 2016). The main task of cash flow management of process activities is the prompt support of the implementation of investment projects and timely informing management about identified risks. Risks are caused by uncertainties in each project (Andryashina, 2014). Risks can be “known”—those defined, evaluated, for which planning is possible. Risks “unknown” are those that are not

Quality Assessment and Improving the Efficiency of Resource Management    577

identified and cannot be predicted. Risk identification will not be effective unless it is carried out regularly throughout the project (Deberdieva, 2016). Although all projects start with full confidence in their implementation and almost all have well-developed business plans and sufficient budgets, their completion is often postponed indefinitely, and costs are repeatedly exceeded. METHODOLOGY The basic research concepts, such as types of risks, receivables and others, were considered in the study on a theoretical basis. It is determined that the main types of risks are risk of under-financing the project, risk of failure of suppliers and contractors, increase in the cost of the investment project, and in terms of time, financial risks and others (Eckert et al., 2015). To minimize risks in the implementation of projects, managers should include in the contracts concluded within the framework of investment projects the following mandatory conditions: (a) inclusion in supply contracts of conditions of liability of suppliers for refusal of delivery and untimely delivery of raw materials, (b) reduction of amounts of advance payments, if possible to make payment upon fact delivery of products, (c) insurance of products during transportation, (d) conclusion of contractual relations directly with the manufacturer, (e) coordination in contracts for supply responsibility of a supplier for price stability for duration of the contract, and (e) control over the execution of concluded contracts (Garina, 2016). It is determined that • under accounts receivable refers to the obligations of legal and natural persons that are part of the working capital of the enterprise, withdrawn from circulation as a loan to the counterparty, an amount of which is able to influence the financial position and performance of an entity associated with a risk of non-repayment of debt; and • receivables management process—a set of actions that regulate the occurrence, movement, and the repayment of a particular customer based on the existing enterprise regulations set targets (Garina, 2015). In modern conditions, controlling process includes the following steps: 1. setting standards to be achieved in order to achieve the goals of the manufacturer in the end—the standards contain criteria for the effectiveness of processes, final products (Garina, 2017a); 2. measurement of actual performance—measured relative to the target; comparison of the actual efficiency with the standard value; and

578    E. P. GARINA et al.

3. corrective actions—initiated by the management in the course of correcting deficiencies in the actual work of the enterprise (Garina, 2017b). RESULTS The object of research is an economic activity of GAZ Group in terms of tracking its resource provision. GAZ is a leading manufacturer of commercial vehicles in Russia, one of the 10 largest European manufacturers of commercial vehicles. The main technical and economic indicators of the company for 2016–2017 are shown in Table 65.1. On the example of a separate project, the organization of the system of management of cash flows in process activity is studied, and it is determined (see Tables 65.2 and 65.3) that 1. there are persistent risks and facts of violations of cost rationing in the field of activities for resource support of project activities of the company, and 2. identified risks of financial losses of business units of the group in the conditions of crisis (Mizikovsky, 2011). TABLE 65.1  Technical and Economic Indicators of Individual Production of the Group Years 2016

2017

Absolute Deviation

Growth Rate (%)

Revenue from sales, goods, products, works, services (million rubles)

20,803.3

21,619.3

816.0

103.9

3.9

Cost of production (million rubles)

18,092.7

19,549.3

1,456.6

108.1

8.1

Gross profit (million rubles)

2,710.6

2,070.0

–640.6

76.4

–23.6

Average number of employees (the list of employees + external parttime workers + working under contracts) (people)

7,813

7,643

–170.0

97.8

–2.2

Average annual cost of fixed assets (million rubles)

5,256

5,099

–157

97

–3

703

860

157

122

22

Indicators

Average annual working capital balance (million rubles)

Increment Rate (%)

Quality Assessment and Improving the Efficiency of Resource Management    579 TABLE 65.2  Results of Cash Flow Management in the Process Activity of a Separate Division of the Company by Main Performance Indicators for 2017 Source Data

Division

The economic effect of measures on economic security in accordance with report 7.3. (thousand rubles) Total balance of maintenance of DZR (without the cost of ORR) (thousand rubles)

315,124.4 72,331.3

Scheduled number of inspections ORR

1,315

The fact of checks ORR

1,242

Identified defects

3,630

Fixed defects

3,620

Number of measures taken (orders for punishment, criminal cases, court decisions)

325

Number of violations detected in the direction of providing protection against economic threats

334

TABLE 65.3  Preliminary Results of Protection of Resources of a Separate Division of the Company by Key Indicators for 2017 (KPI) KPI Indicator

Actual Achievement

(Economic effect of DZR from measures on economic security in accordance with report 7.3) / (total balance of maintenance of DZR) * 100

300%

436%

Quality index of security activity

90%

97%

Ensure implementation of the practice of taking appropriate, including organizational measures, in the direction of providing protection against economic threats

Number of measures taken (orders on punishment, dismissal of guilty persons) / number of employees were punished by the Code of GAZ Group on disciplinary liability * 100%

95%

97%

Ensure that GAZ Group costs are not exceeded

(actual costs for the functionality of the GAZ Group/DZR Costs) * 100%

100%

99%

Key Indicators

Calculation Formula

Compliance of work results of DZR with the maintenance cost of DZR

Creation of conditions of preservation for inventory items and inadmissibility of theft

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Calculation of avoided damage of the enterprise due to the implementation of control showed the need to • work activation in the direction of protection of resources from economic threats in terms of carrying out service checks; • guidance in the work of the principle of the inevitability of punishment for violators in accordance with the “code of the GAZ Group on disciplinary responsibility and measures of material influence,” (Kuznetsov, 2016) rigidity in the adoption of managerial decisions to punish responsible workers; • compensation of material damage caused by their actions; and • preventive measures aimed at eliminating causes and preconditions of causing economic damage to the enterprise in terms of –– development of documented procedures for managing and controlling the company’s operations, and –– procedure for dealing with receivables of GAZ Group enterprises, with other aspects of economic activity of enterprises that cause damage to the group in the forecast period (Mizikovsky, Miloserdova, & Sofin, 2014). CONCLUSIONS The analysis showed that the elimination of the risks of financial losses for the group in this case is possible through • constant control over the implementation of the regulatory base with selection of counterparties and performance of contractual obligations on their part; • constant monitoring execution of contracts in close cooperation with lawyers, financiers, and those responsible for the implementation of relevant activities in the areas (construction, supply, etc.) person’s business units; • inspections of work performance according to concluded contracts: control of completeness of the volume and quality of work performed, compliance of the actual expenses spent by a contractor and material expenses under the act of performed works (rendered services); • planning measures to control all areas of financial and economic activity of business units of the group, taking into account all economic risks; and • intensification of work for overcoming crisis phenomena.

Quality Assessment and Improving the Efficiency of Resource Management    581

REFERENCES Deberdieva, E. M. (2016). Management of complex economic structures of the oil and gas sector of the economy in the conditions of transformation of the hydrocarbon market. Tyumen, Russia: Tyumen State Oil and Gas University. Eckert, C., Albers, A., Bursac, N., Chen, H. X., Clarkson, J., Gericke, K., Gladysz, B., Maier, J., Rachenkova, G., Shapiro, D., & Wynn, D. (2015, July 27–30). Integrated product and process models: Towards an integrated framework and review. Presented at 20th International Conference on Engineering Design. Milan, Italy. Garina, E. P., & Efremova A. D. (2016). Evaluation of the efficiency of operation of the enterprise on the basis of intensification of use of its production potential. In The collection: Topical issues of economics, management, and innovation materials of the International Scientific-Practical Conference (pp. 112–115). Nizhny Novgorod State Technical University, Nizhny Novgorod, Russia. Garina, E. P., Klyueva Yu. S., & Sevryukova A. A. (2015). Approaches to formation of competitive strategies of organizations by branches. Kazan Science, 10, 120–122. Garina, E. P., Kuznetsova, S. N., Garin, A. P., Romanovskaya, E. V., Andryashina, N. S., & v Suchodoeva, L. F. (2017a). Increasing productivity of complex product of mechanic engineering using modern quality management methods. Academy of Strategic Management Journal, 16(4), 8. Garina, E. P., Kuznetsova, S. N., Romanovskaya, E. V., Garin, A. P., Kozlova, E. P., & Suchodoev, D. V. (2017b). Forming of conditions for development of innovative activity of enterprises in high-tech industries of economy: A case of industrial parks. International Journal of Entrepreneurship, 21(3), 6. Kuznetsov, V. P., & Garin A. P. (2016). Organization of production of complex product through coordination of technical systems and processes of the enterprise: Izvestiya VUZ. Food Technology, 2–3(350–351), 6–9. Mizikovsky, I. E. (2011). Harmonization of indicators of internal contro. Audit Statements, 12, 62–66. Mizikovsky, I. E., Miloserdova, A. N., & Sofin, A. A. (2014). Formation of the decision-making process on the organization of auxiliary works. Modern problems of science and education, 5, 297. Schätz, C. A. (2016). Methodology for production development (Doctoral thesis). Norwegian University of Science and Technology, Trondheim, Norway.

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CHAPTER 66

PROSPECTS FOR MARKETING MANAGEMENT IN THE CONTEXT OF ARTIFICIAL INTELLIGENCE DEVELOPMENT Ekaterina S. Kovanova Kalmyk State University named after B. B. Gorodovikov Oksana N. Momotova North Caucasus Federal University Ilyas Z. Batchaev Pyatigorsk State University Irina V. Sklyarova Pyatigorsk State University Elena A. Ponomareva North Caucasus Federal University

Meta-Scientific Study of Artificial Intelligence, pages 583–590 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

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ABSTRACT The most important elements of marketing management are the analysis, planning, implementation, and control of activities designed to meet the consumer needs of target groups of buyers. The chapter deals with the issues of artificial intellectualization of marketing, when artificial intelligence (AI) is considered as a useful component of marketing management. Aspects of the use of AI in marketing are revealed. Marketers use AI to develop a marketing strategy to enhance the effectiveness of marketing campaigns, and it helps them understand and meet the needs of each client in each situation. As a direction of modernization, it is proposed to introduce a component in marketing management that provides intellectual and informational support for making strategic marketing decisions based on a balanced system of indicators. An algorithm for evaluating the readiness of marketing processes for intellectualization is proposed, which consists in the complex application of methodological tools of marketing management. The uniqueness of the authors’ approach is to clarify and highlight the key stages used in the design of AI marketing and understand their content; namely, to assess the degree of digital integration of marketing in the company, to determine the level of development of digital marketing culture in the company, to identify barriers to digital transformation of marketing processes and to establish the desired results from digital transformation of marketing processes.

Digital transformation in entrepreneurship has now expanded its presence in all business areas. Today, we perceive digitalization as a technology for conducting modern business and as a new business model. Prospects for marketing management in the context of AI development, based on the use of digital technologies, are a widely discussed topic in the professional community, requiring practical approval and acceptance by the business community. AI is now becoming a major part of marketing. Marketers use AI to develop a marketing strategy, to enhance the effectiveness of marketing campaigns, and it helps them understand and meet the needs of each client in any situation. Marketing management in the context of digital transformation acquires a deep significance, is not limited to the concept of automation, and dictates the requirements of large-scale penetration of AI in marketing. The formation of a balanced system of indicators by using the capabilities of AI contributes to the concentration of promising elements and technologies of digital marketing (Kusakina, Vorontsova, Momotova, Krasnikov, & Shelkoplyasova, 2019). AI marketing solves the problems of intellectual segmentation of the consumer audience, the formation of e-marketing infrastructure, personalized advertising on digital platforms, support for marketing solutions, improving demand forecasting, and the effectiveness of assortment and pricing policies.

Prospects for Marketing Management in the Context of AI Development    585

METHODOLOGY The research hypothesis is based on the scientific assumption that the development of marketing management under the influence of AI is based on perfecting the principles, conditions and methods of company management to improve the quality of consumer satisfaction, enhance the competitiveness of the marketing complex, and thereby increase the profitability of the company. Artificial intellectualization of marketing requires specialists to solve nonstandard cases, has a creative imprint on the functionality, and frees them from solving simple tasks. The term AI is perceived by many experts as a marketing term. Therefore, when thinking about the artificial intellectualization of marketing, we consider many specific concepts, including computer vision, neural networks, machine learning, and so on. In Russia, the National Strategy for the Development of AI, until 2030, provides the following definition of the latter: “AI is a complex of technological solutions that allows simulating human cognitive functions (including self-learning and finding solutions without a predetermined algorithm) and obtaining results comparable to the results of human intellectual activity when performing specific tasks” (Decree of the President of the Russian Federation of October 10, 2019 No. 490, para. 5a). Interest in marketing solutions based on AI has been growing rapidly recently. According to the McKinsey Global Institute (MGI) report, “Digital Globalization: A New Era of Global Flows” (Manyika, Bughin, & Woetzel, 2016), 84% of marketing organizations will implement or expand AI and machine learning in 2018. 75% of businesses using AI and machine learning will increase customer satisfaction by more than 10% and 3 out of 4 organizations that implement AI and machine learning will increase sales of new products and services by more than 10%. (as cited in Odell, 2018, para. 1)

RESULTS Various solutions based on AI technologies help to expand the customer base, reduce operating costs, improve the financial and business planning system, and optimize relationships with contractors. Russian experts name marketing and advertising, retail, banking, telecommunications, and the industrial complex as the leading sectors in the implementation and use of AI. More than half (58%) of the surveyed Russian experts named optimization of business processes as the main advantages of using AI, slightly

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less (49%)—highlighted the development of new products and services, and 41% expect an increase in labor productivity after the introduction of AI. Thirty-three percent of respondents expect to improve the quality of products and services as a result of AI implementation, and 32% expect to improve customer interaction. Among the main priorities for using AI were setting the right goals (32%), developing business ideas (26%), identifying market opportunities (25%), and making decisions (23%; The Russian Association for Electronic Communications [RAEC], 2019). Digital marketing is the use of digital or social channels to promote a brand or reach consumers. This type of marketing can be carried out in social networks, search engines, the Internet, mobile devices, and other channels. This requires new ways of marketing to consumers and understanding the impact of digitalization on their behavior (Manyika, Bughin, & Woetzel, 2016). To ensure that software and hardware implementation of a system for making strategic marketing decisions could provide the effect of intellectualization of marketing operations, should be taken into account that the millions of suppliers and millions of buyers looking for suitable options to meet their needs, be it in selling or buying. To balance the search system as much as possible, it is necessary not only to digitize it, but also to apply machine learning technologies that can integrate and optimize data from different functional blocks of marketing activities (Khitrova & Chernikov, 2015). Let us consider the main indicators of digital transformation of the marketing mix of the 4C model (consumer, cost, convenience, communication). The set of indicators is based on the authors’ analysis in the course of desk research and expert interviews with marketers of companies engaged in food production (Ostrovskaya, Lapshin, Ponomareva, & Yurchenko, 2017). In order for the company to conduct a qualified selection of indicators for digital transformation of the marketing mix, assess their priority, formulate strategic goals, link them to each element of the 4C and determine the target values, it is necessary to have a system algorithm. Such a system can be a balanced scorecard (BSC). It can help to determine the most important indicators. To generate variants of the BSC, it is necessary to collect expert points of view (Khanova & Shubina, 2011). Moreover, the BSC makes it possible to transform the strategy into the actions of specific performers, that is, it explains what the performer should do in their daily work to implement the strategy of artificial-intelligent marketing. The rationale for the marketing mix in the context of the formation of a AI marketing strategy determined the internal structure of a BSC based on four balanced parameters: customer, convenience, cost, and communication (see Figure 66.1).

Prospects for Marketing Management in the Context of AI Development    587 Creating a minimally viable product with a basic set of qualities. The open development environment of the product. Building consumer loyalty through digital interaction. The digital process of product improvement. Digital product development that combines digital and technological innovations in an adaptive cross-functional approach.

Omni-channel sales. Improving demand forecasting, product range efficiency, and pricing. Improving the accuracy of scoring. Identification of current and prospective clients. Forcing an ideal client profile. Automation of merchandising.

Consumer

Convenience

AI marketing strategy

Cost Digital value chain. Factoring in the overall pricing strategy in the product line. Determining more competitive contextually relevant prices. Dynamic pricing. Individual pricing. Determining the price elasticity of each product.

Communication Personalization of ads on digital platforms. Optimizing targeting accuracy. Creating a digital partner network for product sales. Improve customer experience and support.

Figure 66.1  Balanced scorecard for digital transformation of the marketing mix 4s.

The BSC makes it possible to generate priority marketing solutions based on AI algorithms and eliminates misunderstandings about the benefits of using it (Machine Learning on AWS, 2020). McKinsey found that using a consistent approach to applying AI and machine learning in the retail value chain can significantly increase the effectiveness of assortment policy by half and increase online sales using dynamic pricing by a third (RAEC, 2019). Automation with the support of AI optimizes the activities of the entire organization and ensures its smooth operation and transforms functional and industry processes for the transition to intelligent digital activities of the organization (Akulich, 2018). It covers four main stages of process design (see Table 66.1).

588    E. S. KOVANOVA et al. TABLE 66.1  Evaluating the Readiness of Marketing Processes for Intellectualization Design Stages

Rating, Points

Assessment of the degree of digital integration of marketing in the company   marketing is not integrated

10

  marketing is partially integrated

5

  marketing is mainly integrated

3

  marketing is highly integrated

0

Determining the level of development of digital marketing culture in the company   the desire for permanence

10

  the desire for change

5

  striving for innovation

3

  striving to develop digital skills

0

Identifying barriers to digital transformation of marketing processes  financing

2

 competencies

2

 time

2

  no need

2

  failed previous attempt

2

Establishing desired results from digital transformation of processes   cost reduction

2

  increased revenue

2

  increasing market share

2

  improving products and services

2

  improving the quality of customer service

2

  improving products and services

2

  gaining experience

2

Interpretation of results:   (40–50 points) Choosing automation technologies and determining their compliance with the enterprise architecture   (30–40 points) Digitization of marketing processes   (20–30 points) Development of digital marketing architecture   (10–20 points) Large-scale penetration of AI marketing   (below 10 points) The creation of an ecosystem of intelligent marketing

Marketing intellectualization is an iterative process that involves a purposeful phased approach along with systematic analysis of the results obtained. Creating an ecosystem of intelligent marketing requires certain management decisions from the company that allow to coordinate all elements of the marketing mix based on artificial intelligence, taking into

Prospects for Marketing Management in the Context of AI Development    589

account the requests and preferences of target consumers. It should be based on establishing emotional contacts with clients and forming a paradigm of partnership relations (Kulagin, Sukharevsky, & Meffert, 2019). CONCLUSION The introduction of AI in marketing management allows the company to integrate the back office and front office, generate business reports to monitor sales dynamics, forecast demand and shortage of goods, and provides a number of other advantages. In conclusion, it should be noted that the digital component of marketing itself is rapidly developing and changing, which will lead to an increasing rate of emergence of marketing strategies based on the use of intelligent machines. AI will enhance human creativity and intelligence, not replace it. This will free you from routine tasks and makes it possible to show creativity, which is characteristic only to a human. It is always difficult for companies to build a high-quality strategy and form an interconnected, automated, and intelligent enterprise. Process modernization accelerates and strengthens digital transformation. The idea laid down in the BSC theory is not difficult to understand, but its competent implementation and subsequent effective evaluation depends on the high qualification of management personnel. Special attention should be paid to tools for monitoring and controlling the status of indicators, and AI provides extensive opportunities for their visual representation and analysis. REFERENCES Akulich, M. (2018). Artificial intelligences marketing. Moscow, Russia: Publishing Solutions. Decree of the President of the Russian Federation of October 10, 2019 No. 490, “On the development of artificial intelligence in the Russian Federation.” Retrieved from https://www.garant.ru/products/ipo/prime/doc/72738946/ Khanova, A. A., & Shubina O. V. (2011). Formation of a balanced system of enterprise indicators based on artificial neural networks (e.g., a cargo port). Bulletin of AGTU, 1, 187–194. Khitrova, T. I., & Chernikov, D. V. (2015). Modification of the marketing information system based on intelligent components. Electronic Scientific Journal of the Baikal State University of Economics and Law, 6(4). Retrieved from https:// cyberleninka.ru/article/n/modifikatsiya-marketingovoy-informatsionnoy -sistemy-na-osnove-intellektualnyh-komponent/viewer Kulagin, V., Sukharevsky, A., & Meffert, Y. (2019). Digital@Scale: A desktop book on business digitalization. Moscow, Russia: Intellectual Literature. Kusakina, O. N., Vorontsova, G. V., Momotova, O. N., Krasnikov, A. V., & Shelkoplyasova, G. S. (2019). Using managerial technologies in the conditions of

590    E. S. KOVANOVA et al. digital economy: Perspectives on the use of new information and communication technology in the modern economy. Advances in Intelligent Systems and Computing, 726, 261–269. Machine Learning on AWS. (2020). Retrieved from https://aws.amazon.com/ru/ machine-learning/?nc2=h_ql_prod_ml Manyika, J., Bughin, J., & Woetzel, J. (2016, February 24). Digital globalization: The new era of global flows. Retrieved from https://www.mckinsey.com/ business-functions/mckinsey-digital/our-insights/digital-globalization-the -new-era-of-global-flows Odell P. (2018, March 1). 10 ways machine learning is revolutionizing marketing. Retrieved from https://www.chiefmarketer.com/10-ways-machine-learning -revolutionizing-marketing/ Ostrovskaya, V. N., Lapshin, V. Y., Ponomareva, L. V., & Yurchenko, T. (2017). Marketing strategies of cluster development in the retailing sector. Contributions to Economics, 9783319454610, 31–38. The Russian Association for Electronic Communications. (2019). AI in retail: Russian business practice (The study of the IRIS/HSE supported by Microsoft). Moscow, Russia: RAEC. Retrieved from https://raec.ru/activity/analytics/11479/

CHAPTER 67

EXPERIENCE IN IMPLEMENTATION OF CROWDSOURCING TECHNOLOGIES IN AN ADVERTISING CAMPAIGN Evgeny E. Egorov Minin Nizhny Novgorod State Pedagogical University Tatyana E. Lebedeva Minin Nizhny Novgorod State Pedagogical University Maria P. Prokhorova Minin Nizhny Novgorod State Pedagogical University Sergey V. Semenov Minin Nizhny Novgorod State Pedagogical University Dmitry Yu. Vagin Minin Nizhny Novgorod State Pedagogical University

Meta-Scientific Study of Artificial Intelligence, pages 591–598 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

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ABSTRACT Research presented by authors contains analysis and understanding of the experience of preparation and implementation of crowdsourcing projects in conditions of formation of an advertising campaign of a commercial organization. The chapter reflects original results of the use of crowdsourcing technologies for the development of a new company logo. Authors also formed and described related organizational and economic models. Here is a study of modern theoretical approaches to crowdsourcing and features of its application in Russian conditions. The main attention is paid to the development of a model of crowdsourcing process for various management purposes and, in particular, a formation of an advertising campaign. In detail, authors considered a problem of choosing an online platform for the implementation of crowdsourcing projects for the selection of a company’s logo. This chapter provides a comprehensive analysis of advantages and disadvantages of the use of specialized platforms designed for implementation of crowdsourcing technologies. And then an algorithm of preparation of a technical specification for development of a logo and results of project implementation is described.

It is now widely recognized that innovation is vital for a company which wants to maintain a proper level of competitiveness and have a number of advantages in relation to competitors (Vakulenko, Egorov, & Proskulikova, 2015). Crowdsourcing just allows organizations to use a creative force of a crowd for their own benefit to create innovative products. Daren Brabham, a researcher at the Massachusetts Institute of Technology who was also one of the first to study crowdsourcing, proposed his definition of this phenomenon in 2008, where he focused on problem-solving and production models as primary sources of crowdsourcing (Brabham, 2008). Authors of the chapter suggest the following understanding of this definition, suitable for purposes of this study: Crowdsourcing is an online, distributed production and problem-solving model that uses the collective intelligence of online communities to achieve specific organizational goals. Online communities, also known as crowds, are given the opportunity to respond to crowdsourcing activities promoted by an organization, and they are also motivated to respond for various reasons and participate in them. On the territory of the Russian Federation, crowdsourcing is just beginning its development, and in our country, the use of crowdsourcing is slightly different than in Europe. First, there is little knowledge about crowdsourcing. Secondly, it is used to solve simple marketing tasks—to generate a name, slogan, or logo. Thirdly, it is expensive. The problem of research lies in the need to develop a sequence of crowdsourcing projects, which will allow more widespread use of this technology in Russia.

Crowdsourcing Technologies in an Advertising Campaign    593

METHODOLOGY Theoretical basis of the research consists of research of a number of authors and companies, which in different years tried to define crowdsourcing as a phenomenon and suggested typology of its activity (Brabham, EstellesArolas, Estelles-Miguel, Fielt, Garrigos-Simon, Geiger, Gil-Pechuan, Gonzalez-Ladron-de-Guevara, Grier, and Howe), as well as studies that consider the application of crowdsourcing in business processes, innovation cycle of companies Golubev (2014), Korablinova (2014), Sivaks (2015), Antunes (Thuan, Antunes, & Johnstone, 2018), Aris (Aris, Arshad, Hassan, Janom, & Salleh, 2017), Bal (Bal, Weidner, Richard, & Mills, 2016), Geiger (2016), Schultz (2018) and selected areas of human activity (Lebedeva [Khizbullin et al., 2017], Kaznacheeva [Kaznacheeva, Chelnokova, Bicheva, Smirnova & Lazutina, 2017], Kireev [Kireev, Serbskaya, Ivanov, Vakulenko & Lezhebokov, 2017]); psychology studies addressing an individual’s self-awareness, self-identification, and motivation (Kaznacheeva & Bondarenko, 2016); selfidentification and motivation studies, Internet users, and direct crowdworkers (Naderi, 2018); and research of collective intelligence and the phenomenon of wisdom of a crowd in particular, as well as a participatory culture of the modern Internet. Theoretical analysis, systematization of sources, and sociological methods, as well as modeling, were used in this chapter. RESULTS At the first stage of the study, specifics of crowdsourcing activities were clarified, model and stages of the crowdsourcing process were developed (see Figure 67.1).

Stage 1

Stage 2

Stage 3

Stage 4

• Involvement of caring citizens in a process of problem solving

• Organization and promotion of generating offers

• Selection of the best offers by participants themselves

• Selection of the best participants based on their contribution to solving problems

Figure 67.1  Stages of the crowdsourcing process.

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Development of a company logo in a sphere of crowdsourcing is usually carried out by crowdcasting. That is, a competition is announced between participants of a project, where the best solution in the end receives a reward. For this type of activity, there is also quite a clear algorithm: 1. Crowdsourcer decides to develop a logo by the crowd and makes a brief task, which indicates all the necessary information for developers, as well as the amount of remuneration. 2. The crowdsourcing organization chooses a site for projects. It can be created by the organization itself (website), and be an already ready solution (specialized platforms). 3. Crowdsourcer publishes a project on a site. It contains a task and all necessary information. 4. Crowdsourcer receives examples of a design of a company’s logo. 5. Crowdsourcer selects from proposed concepts design, which meets requirements of a task and purpose of a project, and filters out inappropriate concepts. 6. Crowdsourcer selects a winner or winners of the contest, after which he rewards him for work, while the winner transfers all necessary files and copyrights to crowdsourcer. 7. Crowdsourcer company integrates new logo into the corporate style. At the next stage—approbation of a developed crowdsourcing model— an object was chosen as the logo of an advertising campaign. Selection of crowdsourcing platform. In making decisions about a platform for crowdsourcing pilot projects, it was decided not to create it from scratch and to begin to take advantage of already existing specialized platforms. Based on the analysis of feedback and functionality, four foreign sites and two sites from the Russian-speaking segment of the Internet were selected: 99designs, CrowdSpring, DesignCrowd, Logo Arena, Godesigner, and Dizkon. The best platform designed for design through crowdsourcing, according to experts, is 99designs. It is followed by CrowdSpring; it differs from the previous smaller scale (only 200,000 designers work here, and about 50,000 projects were successfully carried out against 440,000 in 99designs). But, despite the attractiveness of abovementioned platforms, the choice fell on one of two domestic crowdsourcing services—Dizkon. Dizkon is very easy to handle and pretty carefully helps crowdsourcer to make a project assignment and has a well-thought-out privacy and intellectual rights policy tailored to specifics of Russian legislation. As a reward for the author of the best logo was chosen, firstly, a monetary reward in the amount of 15,000 rubles, which in general is approximately equal to the amount requested by freelance designers on average

Crowdsourcing Technologies in an Advertising Campaign    595

for development of high-quality logo from scratch, as well as much cheaper prices of specialized design studios. Fifty people aged 18 and over, participated in the study. A preliminary study found that the majority of respondents were not satisfied with the design of a company’s website. Moreover, studies also showed that less than half of respondents (44%) are satisfied with design of a website, also among respondents was a high percentage of those who find it difficult to answer this question. Company’s website respondents rated generally well, although a percentage of those who rated satisfactorily are quite high—28%. At the same time, the majority of respondents are young people aged from 22 to 25 years (34%) and aged from 26 to 29 years (40%). That is, those who actively use modern Internet technologies both in everyday life and in doing business. The main provisions of technical specifications for a crowd platform were: 1. defining the final concept of the logo, 2. color range (blue and orange color), and 3. inclusion of brief information about a scope of work and activities of the company, as well as 4. a number of indirect technical parameters: resolution, size. The project lasted 16 days. The total cost of a project was less than 30,000 rubles. Fifity-four people responded to an application (geography of the turkers is shown in Figure 67.2). The number of participants by project days is shown in Figure 67.3. Turkers presented five projects for the final consideration; as we see not all the participants who responded were able to provide a result of their work. Evaluation of projects was carried out on a 5 point scale; results are presented in Figure 67.4.

Estonia Finland Russia Lithuania Germany Belorussia Austria 0

2

4

6

Figure 67.2  Geography of Turkers.

8

10

12

14

16

18

596    E. E. EGOROV et al. 6 5 4 3 2 1 0 0

2

4

6

8

10

12

14

16

18

Figure 67.3  Number of crowdsourcing project participants for company logo development by project days.

Economy Additional information File format Resolution Colors Name Minimalism 0

1

2 5

3 4

4 3

2

5

6

1

Figure 67.4  Evaluation of projects by criteria.

Results of the research are confirmed in the latest publications on crowdsourcing—in 2017, a team of researchers from the University of Technology of Malaysia published a poll of crowdworkers employed in a number of crowdsourcing projects of Malaysian organizations (Bal, Weidner, Hanna, & Mills 2016). We will formulate the main reasons hindering the development of crowdsourcing in our country: 1. limited number of proposed crowdsourcing projects, crowdsourcing works; 2. limited number of crowdworkers; 3. lack of necessary skills among crowdworkers; 4. the perception of crowdsourcing organizations and platforms by crowdworkers; and 5. security of a payment mechanism.

Crowdsourcing Technologies in an Advertising Campaign    597

CONCLUSIONS Based on results of the work carried out on the use of crowdsourcing technologies in the company’s activities, it is possible to draw a number of conclusions that are important for further use of such technologies for business. Firstly, crowdsourcing as a technology is not currently widespread in Russia, which is due to a number of reasons of objective nature (e.g., a limited number of platforms for crowdsourcing) and subjective character (mistrust of Internet platforms, lack of knowledge about this technology in managers of companies, lack of skills to use technology in specialists). Secondly, development of crowdsourcing is directly related to presence of the company employee’s skills of managing crowdsourcing projects: determination of needs, formation of technical specifications, legal registration of property rights to the results of crowdsourcing activities, schemes of attracting employees to crowd projects, and payment for their services. Third, procedures for evaluating the effectiveness of products and results of crowdsourcing activities are not sufficiently developed, as tasks and projects related to search for creative and innovative solutions, for which crowdsourcing is intended primarily, involve expertise of specialists who are able to evaluate them from the perspective of strategic development of companies. Fourthly, the potential of crowdsourcing technology for Russian companies is associated, in our opinion, with its characteristics such as relatively low cost, ability to choose a solution from several options, implementation of the procedure remotely, and nonstandard approaches and methods of solving problems. REFERENCES Aris, S. R. S., Arshad, N. H., Hassan, H., Janom, N., & Salleh, S. S. (October, 2017). Conceptual model for a sustainable crowdsourcing ecosystem. Pertanika Journal Science & Technol, 25(S), 89–98. Bal, A. S., Weidner, K., Hanna, R., & Mills, A. J. (2016). Crowdsourcing and brand control. Business Horizons, 60(2), 219–228. https://doi.org/10.1016/j. bushor.2016.11.006 Brabham, D. (2008). Crowdsourcing as a model for problem solving. Convergence, 14(1), 156–173. Geiger, D. (2016). Personalized task recommendation in crowdsourcing systems. Cham, Switzerland: Springer. Golubev, E. V. (2014). Crowdsourcing project as a system: The necessary elements, their interconnection, limitations and ways to overcome. Journal “Naukovedenie,” 5(24). Retrieved from http://naukovedenie.ru/PDF/57EVN514.pdf Kaznacheeva, S. N., & Bondarenko, V. A. (2016). Guerrilla marketing as an effective tool for promoting goods to the market. Vestnik of Minin University, 1–1(13), 6.

598    E. E. EGOROV et al. Kaznacheeva, S. N., Chelnokova, E. A., Bicheva, I. B., Smirnova, Z. V., & Lazutina, A. L. (2017). Worldwide management problems. Man in India, 97(15), 191–199. Khizbullin, F. F., Sologub, T. G., Bulganina, S. V., Lebedeva, T. E., Novikov, V. S., & Prokhorova, V. V. (2017). The direction of transformation of information and communication technology (ICT) at the present stage of development into an electronic and information society. Pertanika Journal of Social Sciences and Humanities, 25(July), 45–58. Kireev, V. S., Serbskaya, O. V., Ivanov, S. Y., Vakulenko, R. Y., Lezhebokov, A. A. (2017). Aspects of organization and management of enterprise marketing activities. International Journal of Applied Business and Economic Research, 15(12), 63–72. Korablinova, I. A. (2014). Crowdsourcing in modern enterprises: Theoretical and methodological aspects. Universum: Economics, 1(2). Retrieved from http://7universum.com/ru/economy/archive/item/823 Naderi, B. (2018). Motivation of workers on Microtask crowdsourcing platforms. Cham, Switzerland: Springer. Thuan, N. H., Antunes, P., & Johnstone, D. (2018). A decision tool for business process crowdsourcing: Ontology, design, and evaluation. Group Decision and Negotiation, 27, 285–312. https://link.springer.com/article/10.1007/ s10726-018-9557-y Schultz, E. J. (2016, January 4). How “Crash the Super Bowl” changed advertising. Retrieved from http://adage.com/article/special-report-super-bowl/crash-super-bowl -changed-advertising/301966/ Sivaks, A. N. (2015). Crowdsourcing as a way to optimize the functioning of enterprises. Internet Journal “Naukovedenie,” 7, 1. Retrieved from http://naukovedenie.ru/PDF/52EVN115.pdf Vakulenko, R. Ya., Egorov, E. E., & Proskulikova, L. N. (2015). Study of the effectiveness of the enterprise. Vestnik of Minin University, 4(12), 3.

CHAPTER 68

MAKING MARKETING DECISIONS IN AN UNSTABLE ECONOMIC ENVIRONMENT Nataliia N. Muraveva Southern Federal University Lyudmila V. Belokon North Caucasus Federal University Milena A. Ignatova Peoples Friendship University of Russia (RUDN University) Alexander V. Shuvaev Stavropol State Agrarian University Natalya N. Yakovenko North Caucasus Federal University

Meta-Scientific Study of Artificial Intelligence, pages 599–607 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

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ABSTRACT The chapter presents an analysis of current features and difficulties in developing and making marketing decisions in the framework of enterprise management in unstable economic conditions. To a large extent, these difficulties are related to the transition of Russian socioeconomic systems from a traditional development model to a transformational one. Socioeconomic transformations are accompanied by crisis phenomena and processes in which enterprises are developing. Their effective functioning in the interests of economic growth requires the introduction and further development of marketing systems based on the principle of mutual improvement of the economic entity, the consumer and the external market agent, that is, the creation of anti-crisis marketing. The purpose of the study is to determine the specifics of making integrated marketing decisions and their nuances in the modern economic space. The main result of the study is to identify the impact of a difficult period in the functioning and development of the national economy on the transformation of marketing activities in enterprises. It is concluded that it is necessary to create a model for making anti-crisis marketing decisions, which will be characterized by its nonstandard nature, will be aimed at taking comprehensive, necessary measures and evaluating their effectiveness, taking into account both the short-and long-term prospects for the development of the system.

In modern economic conditions, the leading direction of increasing the level of development of the enterprise and its competitiveness is the improvement of the management system, an integral part of which is the marketing complex. As it is known, currently there is a large-scale digitalization of the economy and consumers’ behavioral changes, which give impetus to the transformation and diversification of ideas about basic marketing solutions aimed at increasing the profitability and sales growth. These trends require the formation of new marketing strategies that affect the corporate components of the management system: basic functions such as planning, organization, and motivation can no longer be used to achieve economic efficiency without ensuring effective demand for the services or products offered. METHODOLOGY The asymmetry of marketing activities of enterprises in the Russian Federation is caused by the lack of stability in the national economy in which they operate and develop. This factor serves as a catalyst to resolve emerging internal and external dissonances: enterprises are forced to enter into negotiation processes with market participants. The purpose of this chapter is to consider the specifics of making integrated marketing decisions in unstable

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economic conditions, including the period of crisis. As a result, it is expected to improve the methodological tools of the marketing decision-making process. The methodological basis for writing this chapter is the following: observation method, comparison method, analysis and synthesis methods, marketing and statistical analysis methods, and modeling. RESULTS In the Russian Economy, at present, there is a transition from a traditional model to a transformational one, the essence of which is that “there will inevitably be a restructuring of entire industries, many productions and assets will be devalued, the demand for professions and competencies will change, and competition will also increase in both traditional and emerging markets” (Shchepakin & Kuznetsova, 2017, p. 914). During the transformation period, the national economy is going through a difficult stage of its functioning and development. As of 2019, the essence of these difficulties is linked with two main factors: • sanctions against Russia imposed by the United States and the EU, which include many prohibitions and restrictions that create a negative trend for the further growth of the Russian economy; and • insufficient development of the production sector, reduction of production of consumer goods for various purposes, as well as limited financing of research activities, significant decrease in innovative developments, and so on (Ayvazyan & Kirichenko, 2017). Changes in any socioeconomic system are accompanied by crisis phenomena and processes that manifest themselves in different ways. Within the Russian economy, the most significant was the crisis of 2008–2010, which had the greatest negative effect: the cumulative decline was about 9% against 3–5% in the EU and the United States. In 2014, GDP growth, according to Rosstat, was only 0.7%. In 2015, the assets profitability did not exceed 6%, and the Central Bank’s refinancing rate was 15%. The profitability of production was almost half the interest on loans. The International Monetary Fund at the end of July 2015 estimated the prospects for a 9% drop in Russian GDP. In the period 2016–2017, there was a significant inflationary leap of 5.8%. This all contributed to the formation of social tension in society, due to the decrease in real incomes of the population, which for 2 years decreased by 13%. Only in 2018–2019, the Russian economy becomes relatively stable, without undergoing large negative leaps (Karpova & Chupina, 2015).

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In the modern economic space, a complex of marketing solutions based on an enterprise should go beyond “stability”: various kinds of problem situations and random deviations are inevitable in the conditions of market cyclicality. This complex should take into account sudden leaps and crisis situations, while minimizing their negative effect, increasing the value of the organization in the long term (Kusakina, Vorontsova, Momotova, Krasnikov, & Shelkoplyasova, 2019). Before examining the features of developing and making complex marketing decisions in the framework of enterprise management in unstable economic conditions, some consideration will be given to the process of building a model of marketing decisions in conditions of stability (based on the experience of Russian enterprises), which consists of the following successive iterations: • analysis of the external and internal environment (market situation); • formulating goals and objectives on the basis of which alternative strategies are developed; • implementation of the selected strategy; and • monitoring the implementation of the strategy and evaluating the results achieved (Bodrunov, 2015). However, in conditions of economic instability, marketing solutions require more detailed consideration (see Table 68.1). TABLE 68.1  Features of Making Marketing Decisions in an Unstable Economic System Features of Making Marketing Decisions Unstable environment

Stable environment

Unstable

Crisis

Typical, everyday

Empirical

Survival

Category of situation

Certainty

Incomplete certainty, with the presence of risk

Uncertainty

Method of making marketing decisions

Formal-logical

Creative

Intuitive

Conditions for developing solutions

Relatively stable, favorable

Emergency

Crisis

Parameter Type of problems to solve

Level of informativeness

Sufficient

Insufficient

Excessive

Reliability of information

Reliable

Relatively reliable, but out of date

Unreliable

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On the basis of these features, a model was built for making complex marketing decisions in an unstable environment, taking into account the provisions made by Korotkov (2017): • conducting market research in order to obtain up-to-date information about the market environment and consumers; • implementation of demand measurement, subsequent market segmentation, and product positioning, that is, direct selection of target markets; • creating a marketing concept consisting of product development and further promotion, pricing, and so on; • developing a marketing program plan, its further detailing, and so on; • collection of additional information about the state and position of the company on the market (SWOT analysis); • preparation of a system of measures in accordance with the instability of the economic environment; • creating alternative marketing solutions to reduce negative impact; • choosing the most appropriate marketing solution that will help to mitigate the situation, removing the company from the position of “uncertainty”; and • control of marketing activities in order to identify further ways to develop the company and stabilize its position in the market environment. In unstable economic conditions, enterprises have a clearer picture of further marketing actions, in contrast to the period of crisis, characterized by a sharp stagnation of production activity. Taking into account the principles of marketing, it should be noted that the crisis is equated with non-competitiveness, which proceeds gradually: It is characterized by the deterioration of such economic indicators as sales, profit, profitability, and so on, which are further aggravated by the general economic crisis (Ostrovskaya, Lapshin, Ponomareva, & Yurchenko, 2017). Over the past 10 years, the region has demonstrated fairly stable dynamics of economic development. Thus, the growth of the region’s GRP over 10 years in comparable prices amounted to 145%, with the national average of 133%. Since 2009, the average annual increase in GRP in the region was about 3%, and reductions in the physical volume index were observed in 2015, based on the 2014 crisis (Strategy for Socioeconomic Development of the Stavropol Territory Until 2035, n.d.). As a region with 17% of its GRP in agriculture and 22% of its industrial output in the food industry, the Stavropol territory has benefited somewhat from protective sanctions, the fall in the ruble exchange rate, and support for import substitution policy. The index of physical volume for agriculture

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was positive until 2016 and exceeded the average Russian level; only in 2017 did it decrease to 98.2%, and in 2018, it will be estimated at 101.1%. At the same time, in terms of GRP per capita, the region ranks last among the compared regions. In 2016, the per capita GRP of the region was 230 thousand rubles/person, with the national average of 450,000 rubles/person. Since 2010, labor productivity has been growing in the chemical and electronic industries, while most food industry enterprises have shown zero growth, and so on (http:// www.gks.ru/). Thus, we see that the socioeconomic situation in the Stavropol territory is unstable, there are crisis situations, although the dynamics of economic development is quite stable. In order to review the processes of developing and making anti-crisis marketing decisions in the current economic conditions of the Stavropol territory, we turn to the activities of JSC Stavropolsakhar, which is one of the most important enterprises of the Izobilnensky district of the Stavropol territory in the field of sugar production from sugar beet, processing of raw sugar. At the moment (according to the Interfax website), this company is now undergoing a crisis situation associated with the instability of the economic environment in the Russian Federation (Kochetkova, 2015). We can conclude that the main signs of the current crisis are: • decrease in sales profit and, consequently, sales volumes; • reduction of the average annual cost of fixed assets; • decrease in own resources and reserve funds, decline in the organization’s solvency; • increase in short-term and long-term liabilities; and • a sharp reduction in the organization’s net profit. Now consider the indicators of profitability of JSC Stavropolsakhar. The most important risk in this situation is the suspension or interruption of production activities, due to the fact that the working capital of the organization is aimed at paying off increased accounts payable entailing a state of insolvency. In a difficult financial and economic situation, JSC Stavropolsakhar should focus on the development of marketing as a component of anticrisis management: • Marketing research will help to answer the question whether it is necessary to maintain the functioning of this organization or to reduce production or stop operations. • Marketing will determine the effective (profitable) type of activity, its volume, and so on. • Marketing identifies the organization’s opportunities in the market and its way out of a crisis situation with the lowest costs and losses.

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The system of anti-crisis marketing solutions should be built as a system of the following interrelated actions: • diagnostics of the company’s situation, which includes recognition of the crisis, assessment of threats and their scale, as well as monitoring of potential consequences; • defining goals: setting immediate goals that gradually turn into building long-term goals, further adjusting the organization’s development plans, and so on; • identifying ways to achieve goals: it is necessary to connect a modified marketing mix system; • in relation to the organization’s products—implementation of modifications that allow adding new components to the product range; • pricing—a price base should be created that will allow to liquidate products from warehouses without destroying the image of the organization; • promotion—advertising, public relations, commercial incentives (discounts), direct marketing; • improvement of marketing system by obtaining the most promising and new distribution channels; • reorganization (restructuring) of the company, in particular, with the expansion of the marketing department and the involvement of more employees in anti-crisis marketing; • evaluation of the effectiveness of the proposed solutions: direct testing of the developed measures and assessment of their implementation; and • adjustment of short- and medium-term marketing decisions. Features of development and making integrated marketing decisions within the framework of JSC Stavropolsakhar in an unstable economic environment, based on the current situation, will be built in the form of subsequent stages (Shchepakin & Khandamova, 2018). An important stage is the adoption of innovative solutions. The introduction of marketing innovations is always a risky move, especially in a crisis. However, this step is the only way for JSC Stavropolsakhar to remain on the market. Before introducing innovations, it is necessary to evaluate the company’s capabilities and carefully analyze its target audience, understand how its views and preferences have changed, and what it needs today (Osobenkov, Shchegortsov, Taran, & Shchegortsov, 2017). It should be pointed out that in the framework of anti-crisis marketing, fundamental principles also play a special role: timeliness, efficiency, flexibility (i.e., rapid response and adaptation to market conditions), and so on.

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CONCLUSIONS In a view of the transformation period of the Russian socioeconomic system, which is going through difficult stages of its development, the authors define the following features of making integrated marketing decisions that should be taken into account by the top management of the enterprise in conditions of instability: • significant dependence on the time factor (time resources); • uncertainty and weak structuring of information used in decisionmaking; • the need for mobility and dynamic decision-making when using limited resources; • specific criteria for choosing solutions (minimizing losses, taking into account risk factors, preventing severe consequences, etc.); • the need for preliminary study of options for management decisions and assessment of their consequences; and • the possibility of obtaining the effect of a crisis shift (both positive and negative) (Demchuk & Efremova, 2015). In general, we can conclude that anti-crisis management is characterized by nonstandard, extreme conditions for the operation of the management object that require urgent, forced measures. Increased unpredictability of the situation and significant changes in the environment of the entities are the reasons for the constant emergence of new marketing problems that require urgent decisions and assessment of their effectiveness both in the short-term and in the long-term development of the system (YakovlevaChernysheva, 2014). It is possible for an organization to achieve a favorable position in the current socioeconomic conditions by developing a marketing system that meets the desired effectiveness of interaction between subjects that is being formed under the influence of purposefully coordinated efforts of many market participants. In our opinion, the proposed adjustments to the marketing decision-making system in the study will allow consistency in achieving the set goals, while minimizing the negative consequences. REFERENCES Ayvazyan, Z., & Kirichenko, V. (2017). Anti-crisis management: Decision-making on the brink. Management and marketing, 4, 94–100. Demchuk, O. N., & Efremova, T. A. (2015). Anti-crisis management. Moscow, Russia: Flinta, MPSI.

Making Marketing Decisions in an Unstable Economic Environment    607 Karpova, S. V., & Chupina, Ya. V. (2015). Features of development and adoption of complex marketing decisions in unsteady management systems. Marketing Management, 3, 63–69. Kochetkova, A. I. (2015). Fundamentals of management in conditions of uncertainty (chaos). Moscow, Russia: Reed Group. Korotkov, E. M. (2017). Anti-crisis management. Moscow, Russia: Yurayt. Kusakina, O. N., Vorontsova, G. V., Momotova, O. N., Krasnikov, A. V., & Shelkoplyasova, G. S. (2019). Using managerial technologies in the conditions of digital economy: Perspectives on the use of new information and communication technology in the modern economy (pp. 261–269). Cham, Switzerland: Springer. Osobenkov, O. M., Shchegortsov, V. A., Taran, V. A., & Shchegortsov, M. V. (2017). Russian economy: Management and marketing. Moscow, Russia: Novosti Printing House. Ostrovskaya, V. N., Lapshin, V. Y., Ponomareva, L. V., & Yurchenko, T. (2017). Marketing strategies of cluster development in retailing sector. Contributions to Economics, 9783319454610, 31–38. Shchepakin, M. B., & Khandamova, E. F. (2018). Modulation of marketing impacts of a business entity on participants of the marketing communication space. Economics and entrepreneurship, 4–2(57–2), 912–915. Shchepakin, M. B., & Kuznetsova, O. A. (2017). Formation of the concept of rational and socially equitable resource management in developing socio-economic systems. Economics and entrepreneurship, 12–3(65–3), 238–245. Strategy for Socio-Economic Development of the Stavropol Territory Until 2035. (n.d.). Retrieved from https://www.economy.gov.ru/material/file/fcb7d966 464bd532dc8187c2e733dc15/CK_2019.pdf Yakovleva-Chernysheva, A. Yu. (2014). Marketing in business as an object of management. Humanization of Education, 15, 62–68.

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CHAPTER 69

SPECIAL ASPECTS OF MODERN PRODUCTION SYSTEMS ORGANIZATION Evgeny A. Semakhin Minin Nizhny Novgorod State Pedagogical University Ekaterina P. Garina Minin Nizhny Novgorod State Pedagogical University Elena V. Romanovskaya Minin Nizhny Novgorod State Pedagogical University Natalia S. Andryashina Minin Nizhny Novgorod State Pedagogical University Dmitry S. Mokerov Minin Nizhny Novgorod State Pedagogical University

Meta-Scientific Study of Artificial Intelligence, pages 609–615 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

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ABSTRACT We conducted analysis of content and key elements of the production systems of Toyota, Ford, and GAZ Group companies. It was determined that general principles of the production system buildup and its philosophy are established at this stage of development. However, when replicating the experience of the industry leaders, the system in its pure form cannot be “transferred” to another economic environment. Therefore, in the context of domestic practice, the experience in building up production systems requires further development and adaptation to the existing environment.

In the modern economic paradigm of Russia, starting from 1990, efforts for development of integrated adaptive production systems (Khairullin, 2014) and technologies for mastering new products, in replicating and projecting the best international practices in this field, in development of industrial production flexibility and its operational capabilities that best suited for adaptation to the changing demands of market agents (Gupta & Krishnan, 2009), play the dominant role in production management. At the same time, the results of European experience replication by domestic enterprises in the last decade have shown that research and development of foreign scholars on issues of economics and management cannot be transferred to domestic conditions in their pure form due to a variety of differences in economic and social systems. Development of large industrial complexes as business systems on the whole represented by a set of elements—lower level subsystems interconnected in organizational, economical, and technological aspects, in particular, production systems and systems for product development is also impossible without an effective system of regulation and working out of the issue with the use of scientific basis for production and technological management (Kundakchyan & Mokichev, 2014). Solution of the designated tasks determines the relevance, both in theoretical and practical terms. The article in question deserves attention in several aspects: systematization of problems and determination of development trends of the systems for creation of a high-tech product by domestic industrial enterprises through the development of production systems. METHODOLOGY Various interpretations of the concept production system are suggested: • a set of tangible objects, groups of people, industrial, scientific, and technical information processes intended to release final products and to ensure an efficient implementation of the production process (Pahl & Beitz, 1988);

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• a business system, the management model of which is determined by the type of production, equipment used, materials, technology, component parts supply chain, and so on (Nenad, 2008); • philosophy of the company, which is physically implemented as a set of elements: process equipment, intangible assets, information, and power subsystems. The company philosophy implementation results in transformation of raw materials (subject of labor) to the finished product; and • production potential of the company, nominal capacity of which can be measured by machine hours, production output, rate of quality products production (Noda & Bower,1996). The authors’ conclusions are as follows: • Production systems of the industry enterprises have a certain set of “top-level” functions for control over the implementation of production processes. • Business systems with discrete-continuous production processes require a well-organized components/modules supply chain. • Efficient operation of the production system requires a preliminary implementation of the Kanban system, which is a matter of some difficulty due to the fact that assembly facility and components suppliers have different levels of this system. Henry Ford and Taiichi Ohno are considered to be the creators and establishers of the production systems in automobile manufacture. The basis of a new method of production and labor organization developed by Ford was an assembly conveyor. The production system is based on the mass production workflow system, standardization, and unification of component parts (Oswaldo & de Menezesa, 2018). The idea is to extend the concept of production flow from the main assembly line to all other processes, from mechanical processing to stamping. Adjusting the flow connecting not only the main assembly line, but also all other processes, reduced total production time. Let’s consider the production system of the company Toyota, which is focused on the complete elimination of losses and production of small series. The basic principles of this production system are: • Just-in-time, which means that during the production process the parts necessary for assembly are available on the production line at the right time and in the right quantities. • Autonomation or automation with application of artificial intelligence. Most of the equipment at all plants of the company, both new and

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old one, is equipped with such devices, as well as various safety devices, maintenance fixtures for quick changeover. Machines are given the element of the human mind. Autonomation changes the nature of machine operation. Thus, the number of operators is reduced and production efficiency is increased. • Balanced production—uniform distribution of operations between production areas allows optimal process utilization and maximum reduction of operating time for each cycle. RESULTS When studying Ford and Toyota production systems, it should be concluded that each of them reflects the philosophy of business management and individuality of people managing the enterprise and that the system is adaptive to internal and external environment of the particular production with certain conditions and sufficiency of resources supply, as well as certain balance of the processes of all system participants. For this reason, the system in its pure form cannot be “transferred” to another economic environment. Therefore, in the context of domestic practice, the experience of the industry leaders in building production systems requires further development and adaptation to the existing environment. Production system of domestic engineering companies was formed by combining elements of the production systems of Ford, Toyota, and their own experience. For example, the main tool for its implementation at the enterprises of GAZ Group was the activity in the field of Toyota Production System. Production systems of domestic industrial enterprises are shown as a pyramid. At the initial stage of building the production system of domestic enterprises, standardized work was the main tool for increasing production efficiency, that is, a set of measures that has been implemented to reduce the cycle time for all operations of the process (Dassisti, 2010): initial timing of operations was conducted, problems were identified, the basics of kaizen were introduced, the results of problems solution were evaluated, and retiming of operations was conducted. As a result, some operations were excluded from the production chain, and as a consequence, cycle time was reduced (Kuznetsov, Romanovskaya, Vazyansky, & Klychova, 2015). Practical solution of operational capabilities of the domestic company production system has been realized with the use of the following tools: • Kaizen—culture of improvements, continuous improvements in small steps, in which each process is evaluated and improved in terms of such indicators as time required, resources used, quality of finished products.

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• Kanban—philosophy of continuous improvement implemented in the form of development of “pull system for delivery of parts and components to conveyor” (Kuznetsov, Romanovskaya, Vazyansky, & Klychova, 2015). In this case, defective parts must not go beyond the zone of origin; the number of produced parts should be equal to the number of parts required by downstream users, and so on. • System 5S—lean manufacturing tools. • SMED (single minute exchange of die) method—changeover of process equipment for switching between production of two different types of parts in the shortest possible time. • 5 Why—a tool for identification of the problem root cause. To find out what is the objective cause of the problem, we should consistently ask the question, “Why?” until the root cause is found (Porter, 2008). The first priority in creating a corporate production system is standardization of the operations performed, which is implemented through the use of the System 5S and the Kaizen philosophy. Let’s consider the operation hood lock attachment on the vehicle GazelBusiness for analysis of operation execution at the production site. For ease of measurement, the operation was divided into individual actions, and the duration was measured using the chronometer. As a result of measurement it was determined that the main steps are performed within 1–4 seconds with slight fluctuations. This indicates the stability of the operation execution by the worker. The unnecessary movements in the shop for collection of new parts were also eliminated. The workflow of operations at this level of production organization was as follows: Take the hood lock, approach the cabin, take the mascot of the vehicle, go to the cabin, install the hood lock, install the mascot, and return to the workplace. The cycle duration is also affected by the imperfection of fixing welded nuts on the cabin, which showed an increase in the time of execution of actions by the worker in a number of measurements. The Kaizen proposal was considered to optimize this cycle, such as tool pallet for nine sets for a hood lock, and a mascot was created. This proposal results in reduction of unnecessary movements of the worker along the production site. Penalty points systems have been proposed to maintain optimized processes; it allowed this defect to be removed in the subsequent work. Proposed changes to the organization of production allowed for a decrease in the operation duration, which provided the reduction of time for execution of the operation. Economic indicators in terms of production output and respectively in terms of wages increased, which further encouraged the worker to quality performance of work.

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When building the production system by domestic companies using the Kanban system, first of all, the operation of conveyor was organized starting from area development for warehouses of expeditions. The Kanban system allowed for arrangement of expeditions in close proximity to the conveyor, near the areas, where the installation on the vehicle is performed. It resulted in feeding as many parts as required for 2 hours of work to the working area. As a result, the problems of misgrading of stock items, delivery of parts to work positions, and saving of time resources were solved. The Kanban system was started to be applied when working with suppliers. The result was a 30% reduction in transport costs and optimization of warehouse stock from 4-day supply level to 2-day supply level. Furthermore, the introduction of effective methods of motivating suppliers to develop and implement measures aimed at preventing defects allowed the reduction of reclamations for the main bought-in components. Jidoka is a tool for prevention of problems, and implemented in the production system of the company in the following way, each operator controls the operation performance quality at his workplace and is responsible for it. This reduces the number of defective parts at the level of the workplace and, consequently, quality control costs and the possibility of passing the defective product to the consumer is eliminated. Another tool of the production system of the domestic company started to be applied was SMED method, which resulted in reduced stock, partial elimination of the need for setup time and errors occurring during setup, and increase of equipment utilization rate. The result of this method was increased production flexibility. A plan of phased project promotion has been developed for effective implementation of the production system in the practice of the company. In particular, the idea of reference production areas for the assembly of Gazel dropside trucks’ cabins was implemented with the following main objectives: effective workplace arrangement, reduction of production stock level, and optimization of material flows. The main objectives included creation of a 2-hour in-process stock of materials and their supply to the conveyor through the Kanban system and elimination of the activities that do not bring added value. Initial project specifications were assembly of 186 cabins per day. Production stock at the workplaces was built up for 4–7 days. Implementation of the project resulted in an almost two times increase of labor efficiency—output of cabins increased up to 408 per day, first pass yield of which amounted to 91.1%, and stock at the workplaces reduced to 2 hours level. Furthermore, the number of conveyors reduced after optimization. The success of reference areas has proved the efficiency of the production system developed by the company, so at the next step, it was decided to establish reference areas in all subdivisions. At that time, 53 reference

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areas were established in the company, work groups for quality control, and process optimization were functioning. These groups are currently working at production costs reduction. CONCLUSIONS Summarizing the results, it should be noted that in today’s economy the increase in the efficiency of material production by achieving the goal of creating a product meeting the highest customer needs with minimal costs is possible through production process organization. According to the experience of the leading engineering companies, the desired effect can be brought only by retrofitting and upgrading of production, supported by the production system improvement and changes in corporate philosophy. It is important to develop a business system of all others through personalization of production, increase of production flexibility by concurrent design of products and processes, and gradually move to the integral use of tools of production system, abandoning the idea of reference areas. REFERENCES Dassisti, M. (2010). HY-CHANGE: A hybrid methodology for continuous performance improvement of manufacturing processes. International Journal of Production Research, 48(15), 4397–4442. Gupta, S., & Krishnan, V. (2009). Integrated component and supplier selection for a product family: Production and oper. Management, 8(2), 163–181. Khairullin, A. (2014). Multisectoral integrated structures key competences. Regional Aspect Mediterranean Journal of Social Sciences, 5(24), 307–312 Kundakchyan, R. M., & Mokichev, S. D. (2014). Methodology of innovative economics. Mediterranean Journal of Social Sciences, 5(24), 327–330. Kuznetsov, V. P., Romanovskaya, E. V., Vazyansky, A. M., & Klychova, G. S. (2015). Internal enterprise development strategy. Mediterranean Journal of Social Sciences, 6(1), 444–447. Nenad, P. (2008). Conceptual modelling of complex production systems. JIOS, 32(2), 115–122. Oswaldo, A. N., & de Menezesa, B. (2010). Manufacturing strategies in action. Brazilian Journal of Operations & Production Management, 7(1), 9–35. Pahl, G., & Beitz, W. (1988). Engineering design: A Systematic Approach, 12(8), 126–138. Porter, M. E. (2008). The five competitive forces that shape strategy. Harvard Business Review, 86(1), 79–93. Noda, T., & Bower, J. L. (1996). Strategy making as iterated processes of resource allocation. Strategic Management Journal, 17, 159–192. https://doi.org/10.1002/ smj.4250171011

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CHAPTER 70

IMPACT ON RISK FACTORS OF INDUSTRIAL ENTERPRISES Yaroslav S. Potashnik Minin Nizhny Novgorod State Pedagogical University Ekaterina P. Garina Minin Nizhny Novgorod State Pedagogical University Elena P. Kozlova Minin Nizhny Novgorod State Pedagogical University Svetlana N. Kuznetsova Minin Nizhny Novgorod State Pedagogical University Alexander P. Garin Minin Nizhny Novgorod State Pedagogical University

ABSTRACT Uncertainty determines a possibility of deviations of actual results of economic activity of industrial enterprises from target values. To ensure that an amplitude of these deviations is consistent with an acceptable level, exposure is carried out

Meta-Scientific Study of Artificial Intelligence, pages 617–623 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

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618    Y. S. POTASHNIK et al. on risk factors. This chapter is devoted to clarification of a sequence and content of the main stages of influence on risk factors of economic activity of industrial enterprises. The content of impact on risk factors is shown. A brief description is presented of the basic options for responding to risk factors, including increase, acceptance, evasion, redistribution, and reduction. Sequence and content of the stages of influence on risk factors of economic activity of industrial enterprises are proposed. It is proposed to distinguish regulated and unregulated risk factors, and their characteristics are given. Recommendations on a choice of response options depending on a regulation of risk factors and relationship between their initial and acceptable levels are formulated. A complex of scientific methods was used in the work, including a systematic approach, logical analysis and synthesis, study of scientific literature, and formalization.

Management of the economic activities of industrial enterprises is carried out under conditions of uncertainty, which is understood as the state of total or partial absence of decision makers, and precise knowledge of future conditions for their implementation. The main causes of uncertainty are an inadequacy and unreliability of the available information, inability of responsible persons to correctly process and interpret it, the non-determinism of many processes and counteraction (accidental or conscious; Gracheva & Lyapina, 2010). Due to uncertainty in implementation of plans by enterprises, actual results may deviate from desired values. These deviations are usually characterized as the effect of uncertainty on objectives and referred to as risks. If deviations exceed acceptable for an enterprise (e.g., if an actual negative deviation of profitability was 20%, and acceptable—5%), then an activity becomes economically unattractive. Therefore, at a planning stage, it is important to design and then implement measures to reduce negative deviations of results within an acceptable range for an enterprise. Russia has adopted regulatory and legal documents regulating certain aspects of risk management of industrial enterprises, as well as standards containing relevant guidelines and recommendations (see Table 70.1). At the same time, in our opinion, some methodological aspects of risk management need further elaboration. In particular, a sequence and content of the main stages of exposure to risk factors of economic activity of industrial enterprises require clarification, which is what this study focused on (Allan, 2010). METHODOLOGY Risk factor is an internal or external event to an enterprise that may adversely affect an achievement of objectives of an enterprise (Garina et al., 2017). Typical risk factors for industrial enterprises are a decrease in demand for manufactured

Impact on Risk Factors of Industrial Enterprises     619 TABLE 70.1  The Regulatory Framework of Risk Management in the Russian Federation Name of the Regulatory Document

Content of a Document in Terms of Risk Management Regulation

Federal Law No.184 dated 27.12.2002 “On Technical Regulation”

Regulates a management of technical and production risks

Article 21-22 of the Federal Law of October 29, 1998 No.164 “On Financial Lease (Leasing)”

Regulates a distribution of risks between parties to a lease agreement, insurance of business risks

Decree of the Government of the Russian Federation of 22.11.2011 No. 964 “On the procedure for carrying out activities for insurance of export credits and investments against business and political risks”

Rules for ensuring business risks of domestic organizations operating abroad, related to export of goods and services and investment in foreign assets abroad

International Financial Reporting Standard (IFRS) 7 “Financial Instruments: Disclosure”

Characteristics of disclosing information on credit, market, liquidity risk and analyzing the sensitivity of an enterprise to each type of market risk

GOST R 51897-2011. ISO 73.2009 Manual

Translation into Russian of terms and definitions of the International Standard ISO 73.2009

GOST R ISO/MEK 31010-2011. Methods of risk assessment

Translation into Russian of concepts and main stages of a risk assessment process, and methods

ISO 31000-2018. Risk Management

Translation into Russian of risk management principles, main stages of risk management process

Source: Vokhmintsev, 2016

products, an increase in prices for resources, a violation of partners’ obligations, headhunting of key employees, deterioration of macroeconomic conditions, theft, fires, and so on. In view of a negative impact on the performance of enterprises, in relation to risk factors, management measures, including identification, analysis, and impact, are carried out (ISO 31000, 2018). Identification of risk factors involves identifying them, causes, and negative effects, describing other essential characteristics. Identification of risk factors is carried out at a stage of planning economic activity (development of solutions; (Garina, Klyueva, & Sevryukova, 2015). Analysis of risk factors is carried out in order to diagnose levels of individual risk factors, acceptability of levels, ranking them from a point of view of priority impact on them. Level of a risk factor is generally defined as a combination of probability and negative consequences of an occurrence of a factor (Hardy, 2015). For each risk factor, initial (first, before influence) and acceptable (second, maximum allowable) levels are determined. On the basis of comparison of initial

620    Y. S. POTASHNIK et al. Options for Responding to Risk Factors

Promotion

Acceptance Redistribution

Promotion Reducing

Figure 70.1  Options for responding to risk factors.

factor and second, draw conclusions about a priority of considered risk factors in terms of implementation in relation to him of modifying influences. The greater the excess of an initial factor above a second, then priority is higher (Markova & Narkoziev, 2018). The purpose of responding to risk factors is to ensure that their levels meet acceptable values (Chelnokova & Nabiyev, 2015). Five main options for responding to risk factors can be identified (see Figure 70.1). Enhancement involves the implementation of actions that increase, within acceptable limits a likelihood and/or negative consequences of a risk factor occurrence. For example, reducing to a certain level the cost of advertising a new product can increase the risk of not reaching targeted sales volume. However, if a residual level of a risk factor does not exceed an acceptable level, enterprise will benefit from savings. Acceptance does not imply any impact on the probability, and consequences of the occurrence of the risk factor is carried out in two main ways: active, when a reserve is created to compensate for possible losses, and passive, when action is taken only in the event of a negative situation. Evasion implies an abandonment of activities leading to occurrence of a considered risk factor. Redistribution involves the implementation of actions to transfer or otherwise distribute consequences of a risk factor. Risk diversification involves a simultaneous implementation by an enterprise of different directions, which helps to reduce negative consequences of problems in one direction (Garina et al., 2018). Reductions are when an enterprise takes actions to reduce a probability and/or consequences of a risk factor. An example of actions to reduce a probability of risk is a careful elaboration of the concept and individual stages of implementation of an innovative project, formation of a qualified team of managers, and use which reduces a probability of project failure. An example of reducing effects of formation and development of company plans of action in various emergency situations.

Impact on Risk Factors of Industrial Enterprises     621

RESULTS In our view, planning an impact on identified risk factors of industrial enterprises can be carried out in three stages. In the first stage, risk factors fall into two categories: regulated and unregulated (Kuznetsov, 2016). Regulated are risk factors—the value of which an enterprise is able to change in the desired direction. Unregulated risk factors—the value which a company cannot influence. For example, the significance of the level of risk factors such as mistakes in designing a new product or fires in production can usually be changed with measures taken by the company. Levels of such risk factors as a decrease in the purchasing power of target customers or an emergence of additional legislative restrictions cannot usually be modified by a company. In the second stage, one or more response options are selected for each risk factor, depending on a level of regulation and ratio between initial and acceptable levels (Mizikovsky, 2016). In this case, you can take advantage of the recommendations suggested by the authors, presented in Figure 70.2. An increase may be applied to manage risk factors when a value of initial level is lower than acceptable and an increase is economically feasible. Acceptance can be applied to both regulated and nonregulated risk factors when a value of their initial level is less than or equal to the value of an acceptable level. It can also be used if the value of the initial level of risk factors is slightly higher than the value acceptable, but the use of other response measures is either impossible or impractical. At the same time, an acceptable level is raised to the initial level. Evasion can be applied to both regulated and nonregulated risk factors if the value of their initial level is critical, significantly higher than the value of an acceptable level, and there are no alternative effective ways of responding. Risk factors with a level value not exceeding the acceptable value

Risk factors with a level value exceeding the acceptable value

Regulated risk factors

Enhancement, acceptance

Acceptance, evasion, redistribution, reduction

Unregulated risk factors

Acceptance

Acceptance, evasion

Figure 70.2  Matrix of risk response options.

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In the third stage, specific responses to risk factors are being developed. If for certain risk factors, response measures are mandatory, established by the government, regulators, or standards, then they are necessarily included in the plan for influencing risk factors. If alternatives exist among these measures, then those that allow reaching an acceptable level of risk factors with minimal costs are selected. If no strict requirements have been established regarding the response to risk factors, a company develops measures based on the principles of expediency and effectiveness (Potashnik, 2016). Measures in relation to identified risk factors may include both individual response options, and their complex of redistribution and reduction. Measures should correspond to values of risk factors and an enterprise should have sufficient resources to implement them. They also should not only bring risk levels to acceptable values, but also achieve the profitability and competitiveness objectives of an enterprise. To ensure a timely response to unidentified risk factors, it is recommended that a reserve of resources be established (e.g., in the amount of 5–10% of the total costs associated with exposure to risk factors). During a subsequent identification of these factors for determining measures against above procedure may be applied. CONCLUSIONS In the course of the study, the sequence and content of the stages of impact on risk factors of economic activity of industrial enterprises were clarified. It is proposed to distinguish regulated and unregulated risk factors, and their characteristics are given. A brief description is presented of basic options for responding to risk factors, including increase, acceptance, evasion, redistribution, and reduction. Recommendations on a choice of response options depending on a regulation of risk factors and relationship between their initial and acceptable levels are formulated. REFERENCES Allan, G. (2010). Standardizing risk management—business enabler or the risk manager`s straitjacket? In J. Reuvid (Consulting Ed.), Managing business risk: A practical guide to protecting your business (pp. 23–30). London, England: Kogan Page. Chelnokova, E. A., & Nabiyev, R. B. (2015). Tutor activity of a teacher to ensure successful adaptation of university students. Bulletin of Mininsky University, 3(11), 23. Retrieved from https://vestnik.mininuniver.ru/jour/search/search Federal Law No. 184 Dated 27.12.2002, “On Technical Regulation.”

Impact on Risk Factors of Industrial Enterprises     623 Garina, E. P., Garin, A. P., Kuznetsov, V. P., Popkova, E. G., & Potashnik, Y. S. (2018). Comparison of approaches to development of industrial production in the context of the development of a complex product. Advances in Intelligent Systems and Computing, 622, 422–431. https://doi.org/10.1007/978-3-319-75383-6_54 Garina, E. P., Klyueva, Yu. S., & Sevryukova A. A. (2015). Approaches to formation of competitive strategies of organizations by branches. Kazan Science, 10, 120–122. Garina, E. P., Kuznetsova, S. N., Garin, A. P., Romanovskaya, E. V., Andryashina, N. S., & Suchodoeva, L. F. (2017). Increasing productivity of complex product of mechanic engineering using modern quality management methods. Academy of Strategic Management Journal, 16(4), 8. Gracheva, M. V., & Lyapina, S. Yu. (2010). Risk management in innovation: Textbook for university students studying in economic specialties. Moscow, Russia: UNITI-DANA. Hardy, K. (2015). Enterprise risk management: A guide for government professionals. San Francisco, CA: Jossey-Bass. ISO 31000 (2018). Risk management standards. Geneva, Switzerland: Author. Kuznetsov, V. P., & Garin, A. P. (2016). Organization of production of complex products through coordination of technical systems and processes of the enterprise: Izvestiya. Food Technology, 2–3(350–351), 6–9. Markova, S. M., & Narkoziev, A. K. (2018). Production training as a component of professional training of future workers. Bulletin of Mininsky University, 6(1). https://doi.org/10.26795/2307-1281-2018-6-1-4 Mizikovsky, I. E., Bazhenov, A. A., Garin, A. P., Kuznetsova, S. N., & Artemeva, M. V. (2016). Basic accounting and planning aspects of the calculation of intrafactory turnover of returnable waste. International Journal of Economic Perspectives, 10(4), 340–345. Potashnik, Y. S., & Sevryukova, A. A. (2016). Management of the level of risk of innovative projects of industrial enterprises. Scientific Review, 16, 202–204. Vokhmintsev, V. V. (2016). Normative and legal support of risk management—Central Russian Journal of Social Sciences, 3, 196–207.

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CHAPTER 71

INVESTMENT ATTRACTIVENESS OF ARTIFICIAL INTELLIGENCE TECHNOLOGIES IN INDUSTRIAL PARKS Victor P. Kuznetsov Minin Nizhny Novgorod State Pedagogical University Svetlana N. Kuznetsova Minin Nizhny Novgorod State Pedagogical University Sergey D. Tsymbalov Minin Nizhny Novgorod State Pedagogical University Elena V. Romanovskaya Minin Nizhny Novgorod State Pedagogical University Natalia S. Andryashina Minin Nizhny Novgorod State Pedagogical University

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ABSTRACT In this chapter, the authors address an issue of investment attractiveness of industrial parks. In conditions of the need to create a favorable investment climate, theoretical and practical problems of investment attractiveness acquire special importance. This study considers the investment contribution of industrial parks (IP) to the development of the Russian economy. It covers the time period from 1998 to 2017. The authors focus on the following issues. Formation of an innovationoriented model of the economy, ensuring economic growth in conditions of globalization and effective management of industries is impossible without investment support in priority areas. At the request of investors and localization of new productions in the Russian Federation, it is possible to study not only changes in the investment climate and geography of foreign economic relations but also the evolution of the approach to the creation of new industries. Key investors are Russian companies. Among foreign investors by volume of investments are companies from Germany, the United States, and Japan. Quantitative and qualitative methods were used in this chapter. A current trend is the replacement of plants with industrial parks. Intensive economic development affects the need to develop new approaches in order to attract investors to the regions. Industrial complexes are the best option as they focus everything you need: logistics and infrastructure, workplaces, and a comfortable environment that allows you to reduce costs, quickly control business, and establish a full output of specific products. In general, industrial parks were created with the focus on small and medium-sized companies that need modern warehouses and new production facilities. According to experts, industrial parks play an important role in the development of the country’s economy. This is due to the high demand from domestic and foreign companies in the territory where it would be possible to place their production, warehouses, and office premises. Therefore, experts pick industrial parks, which have developed infrastructure.

The goal of this study is to substantiate expediency of development of IP on the basis of large industrial enterprises as an instrument of investment attractiveness. The objectives of this study are to • offer a methodical approach for creation of investment attractiveness and • substantiate the method of efficiency of functioning of IP. The object of research is industrial enterprises of regions of the Russian Federation. The subject of research is organizational and economic relations that arise during the integration of enterprises into the structures of IP.

Investment Attractiveness of AI Technologies in Industrial Parks    627

The most effective tool to increase the investment attractiveness of the country is the formation of IP, allowing increase to the competitiveness of industries, finding a solution to the problem of overcoming imbalances in the domestic economy, and preventing capital outflows abroad. METHODOLOGY According to provided data from 166 IP, over the period from 1998 to 2017 on a territory of these sites, more than 1185 billion rubles were invested in the creation of new productions: • Largest number of investments—462 billion rubles in creation of productions invested by Russian companies. Other investment leaders are foreign companies from Germany, the United States, Japan, and Turkey. • Dynamics of investments in production since 2014 is positive; the total increase for 4 years amounted to 40% and reached 128 billion rubles in 2017 (Azatyan, 2017). • 99.6% of all investments in production were invested in 21 regions of Russia. At the same time, 70% of all investments made in investment parks on the territory of Kaluga region, Republic of Tatarstan, Lipetsk, Moscow, and Ulyanovsk regions (Arkhipenko, 2016). RESULTS Greenfield parks are focused on the placement of large and medium-sized companies with capital-intensive projects. Brownfield parks are located on their territory by medium, small, and micro enterprises, which at the same time together create a comparable number of jobs with large companies. The volume of investments in production in Greenfield parks is 20 times higher than the volume of investments in Brownfield parks. The ratio of attracted investments per one ruble varies between types of projects by nine times, an average cost of creating a workplace; by 17 times, an average cost of creating production; and by 40 times, but while an average number of jobs created varies only two times (Baronov, 2017). Specificity of a structure of the industry lies in the predominance of the public sector in Greenfield projects and private companies—in the management of Brownfield platforms. The government prevails in capital investments in industrial infrastructure—two thirds of all investments made in state industrial parks; three

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fourths of all residents are concentrated on the territory of private industrial parks (Garina, Kuznetsov, Romanovskaya, Andryashina, & Efremova, 2018). Private, government industrial parks show similar efficiency of the attraction of investments in production on one ruble investments in infrastructure. Industry structures of residents of Greenfield parks and Brownfield parks are different and differ as types of industries and a variety of types of industrial production (Potashnik, Garina, Romanovskaya, Garin, & Tsymbalov, 2018). One-third of all investments are made in the automotive industry and production of tires and plastics. Chemical industrial parks are the most striking example of specialized Brownfield sites in Russia (Borodachev, 2017). Agricultural parks are a new upward trend of our economy. In 2018, a territory of advanced development Komsomolsk was replenished with an agro-industrial cluster with a total investment of 941.5 million rubles and became an additional platform for the territory of advanced development Khabarovsk. The key issue for localization of production remains not only the degree of readiness of engineering infrastructure and investment climate but also the establishment of partnerships with buyers and suppliers (Kamdin, 2013). Industrial clusters are a territory on which exclusively processing production will be created, products will be widely demanded not only in the domestic market of the Russian Federation but also among foreign buyers (Kovalev, 2015). Entering foreign markets with competitive products of the Russian Federation is an opportunity to attract new investments. Investors should see the prospects for the sale of products. In addition, all projects emphasize the attraction of modern high technologies of processing and production of final products. Import substitution without prejudice to the interests of the buyer of products cannot be based solely on restrictive measures: It is necessary to create conditions for increasing the competitiveness of domestic production (Kolchina, 2018). Total foreign investments account for 61% of all investments in production; 95% of all foreign investments are invested in the creation of 300 large and medium-sized industries in the territory of Greenfield parks, which is 15% of all residents. The sectoral structure of foreign and Russian investments is different: Among foreign companies that are the leading investments in the automotive and food industry, Russian companies have made the largest investments in food production, metallurgical production, and industry of building materials (Kuznetsova, 2013). The average cost of creating a production facility, taking into account the construction of an industrial building, is 2.5 billion rubles.

Investment Attractiveness of AI Technologies in Industrial Parks    629

Creation of infrastructure supply on the basis of the existing raw material base, deepening of processing and production of final products, formation of a cluster of purchaser-suppliers—a real way for the development of all interested in renewing enterprises. And the presence of natural and historically established advantages and competencies will reduce the cost of creating new industries and make more rational use of funds for infrastructure projects (Kuznetsova, Romanovskaya, Artemyeva, Andryashina, & Egorova, 2018). Most of the productions were opened by Russian companies: 36 new plants have invested more than 18 billion rubles and created 1,773 jobs. New production facilities were opened in industrial parks located on the territory of 20 regions of Russia. The largest investments in production are • plant Bridgestone and industrial park Zavolzhye—13 billion rubles of investments (Yashin, Trifonov, Koshelev, Garina, & Kuznetsov, 2018); • production of engines for pumps, JSC RED, opened in the territory of industrial park Stankomash —more than 9 billion rubles of investments; and • plant PPG Industries, SEZ PPT Lipetsk—4.5 billion rubles of investments. The largest investment projects of 2017 were • the creation of the enterprise for the production of glucose-fructose syrup of the company Technokord, industrial park “Serdobsky,” Penza region—17 billion rubles of investments; • the Mersedes-Benz plant, Esipovo industrial park, Moscow region—15 billion rubles of investments; and • the lant for deep-processing of grain company Rustark on the territory of SEZ PPT Lipetsk—14.2 billion rubles of investments (Singatullina & Mingazova, 2018). In modern conditions, formation of IP is a necessary condition for the development of small- and medium-sized businesses in the field of industrial production. Therefore, IPs are necessary and should focus primarily on small and medium businesses. CONCLUSION In turn, creation and development of IPs allow for subjects of the Russian Federation to

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• increase the investment attractiveness of the region by creating a modern investment infrastructure; • attract investors to new sectors of the economy of the region, which were not represented in a structure of industrial production; • form a multiplicative effect, which consists of creation and development of related industries in relation to productions located in an industrial zone; • carry out technological upgrading of industry sectors and to create high-tech industrial clusters; • improve the productivity of land and quality of territorial planning in the region; and • ensure withdrawal of enterprises from regional centers and major cities (Kuznetsov, Romanovskaya, Egorova, Andryashina, & Kozlova, 2018). Economic advantages of an investment project are: • smaller investment in construction: –35%—an average discount of the cost of construction of 1 m2 of production premises in comparison with 1 m2 of office; up to 100 million rubles—a subsidy for technical connection; up to 300 million rubles; • savings in the operation of an object: 0.01%—land lease rate; and • tax savings: 15.5%—income tax; 0%—property tax; 0.7%—land tax amounts (Mizikovsky, Druzhilovskaya, Druzhilovskaya, Garina, & Romanovskaya, 2018). Thus, creation and development of IP as an important element of investment infrastructure is a mutually beneficial process, where the interests of business and government completely coincide, and this process can be built on the basis of public–private partnership. REFERENCES Arkhipenko, V. A. (2016). Selection and evaluation of the feasibility of innovative projects of an industrial enterprise on the basis of the criteria-based approach. Izvestiya of Taganrog State Radio Technical University, 59(4), 197–203. Azatyan, M. O. (2017, February). Analysis of structure and dynamics of direct foreign investments in the Russian Federation. In Proceedings of the VII International Scientific Conference (pp. 10–14). Krasnodar, Russia. Baronov, V. I. (2017). Free economic and offshore zones (economic and legal issues of foreign and Russian practice). Moscow, Russia: INFRA-M.

Investment Attractiveness of AI Technologies in Industrial Parks    631 Borodachev, I. M. (2017). The potential of public–private partnership as a factor of increasing the competitiveness of the economy. Economic sciences, 11(36), 65–68. Garina, E. P., Kuznetsov, V. P., Romanovskaya, E. V., Andryashina, N. S., & Efremova, A. D. (2018). Research and generalization of design practice of industrial product development (by the example of domestic automotive industry). Quality—Access to Success, 19(S2), 135–140. Kamdin, A. N. (2013). Special economic zones: Problems and features of functioning at the regional level. Young scientist, 5, 312–317. Kolchina, O. A. (2018). Implementation of the principle of competitive selection of investment projects in the investment program of the municipality. News of Southern Federal University. Technical Sciences, 87(10), 18–23. Kovalev, A. V. (2015). The main types and tools of industrial parks development in the regional economy of Russia (Dissertation, candidate of economic sciences). Krasnodar, Russia. Kuznetsova, S. N., Romanovskaya, E. V., Artemyeva, M. V., Andryashina, N. S., & Egorova, A. O. (2018). Advantages of residents of industrial parks (by the example of AVTOVAZ). Advances in Intelligent Systems and Computing, 622, 502–509. Kuznetsov, V. P., Romanovskaya, E. V., Egorova, A. O., Andryashina, N. S., & Kozlova, E. P. (2018). Approaches to developing a new product in the car building industry. Advances in Intelligent Systems and Computing, 622, 494–501. Kuznetsova, S. N. (2013). Development of organizational and economic mechanism of formation of industrial parks: In mechanical engineering enterprises (Dissertation, candidate of economic sciences). Ivanovo, Russia. Mizikovsky, I. E., Druzhilovskaya, T. Y., Druzhilovskaya, E. S., Garina, E. P., & Romanovskaya, E. V. (2018). Accounting for costs and expenses: Problems of theory and practice. Advances in Intelligent Systems and Computing, 622, 152–162. Potashnik, Y. S., Garina, E. P., Romanovskaya, E. V., Garin, A. P., & Tsymbalov, S. D. (2018). Determining the value of own investment capital of industrial enterprises. Advances in Intelligent Systems and Computing, 622, 170–178. Singatullina, L. A., & Mingazova L. R. (2018). Some problems of financial and tax support of industrial parks in the Russian Federation. Journal of Economics, Law and Sociology, 1, 138–142. Yashin, S. N., Trifonov, Y. V., Koshelev, E. V., Garina, E. P., & Kuznetsov, V. P. (2018). Evaluation of the effect from organizational innovations of a company with the use of differential cash flow. Advances in Intelligent Systems and Computing, 622, 208–216.

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CHAPTER 72

MACRO-PLANNING OF INNOVATIVE STRATEGIES IN BRICS COUNTRIES Study of Methodological Features of Evaluation, Comparative Advantages, Opportunities, and Challenges in the Light of Accelerating the Digitalization of the Economy and the Intensive Development of Artificial Intelligence Bahadyr J. Matrizaev Financial University Leyla M. Allakhverdieva Moscow State University of Humanities and Economics Muslima K. Sultanova Moscow University of Finance and Law

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ABSTRACT This chapter deals with a comparative analysis of the experience of macroplanning of innovative strategies and technological modernization of the (Brasil, Russia, India, China, and South Africa) BRICS economies. This comparative analysis is carried out on the basis of the author’s three-component approach, which presents the differences between the intensity of technological modernization, structural changes, and global interaction. The authors also present the statistical base developed by the authors on the basis of patent indicators to measure technological modernization and its application in the economies of the BRICS countries for the period 1980–2015. The data show that there is no single way of technological modernization. At the same time, the authors found several unique directions of macro-strategic planning of innovative development in these countries with different degrees of convergence between the intensity, structural changes, and the nature of global interaction. All BRICS countries are showing an increase in the formation of new points in advanced technological developments, despite the fact that China and Russia, taken separately, have significantly increased the intensity in some areas of advanced technology.

The development of information and communication technologies and digitalization in the post-industrial phase are increasingly critical for the economy and society as a whole. Lately, the traditional policy in the field of ICT has become more horizontal, and covers a wide range of issues—from the creation of enterprises and productivity growth to public administration, employment and education, health and population aging, and environment and development. Based on the conceptualization of technological re-equipment as a three-dimensional process, we have studied various ways of technological re-equipment of the BRICS economies. In our study, we made the difference between the intensity of technological modernization reflected in different types and levels of innovative potential, degree of technological modernization from the point of view of changes in the structure of technological knowledge and the role of global interaction in terms of inflow of foreign technologies and the interaction with domestic technological efforts. As a result, four general hypotheses were formulated on the characteristics of technological re-equipmnent of middle-income economies, such as BRICS. In our research, we applied this three-pronged approach to technology modernization using various patent indicators to test these hypotheses. METHODOLOGY Traditional models of technological development or modernization are based on either an exogenous growth model (Solow, 1957), or on the theory of endogenous growth (Romer, 1990). As is well known, the Solow model

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cannot explain technology and considers it as an inexplicable part of growth, making it a very limited relevance to our research (Aghion & Howitt, 1992; Bell, 2009; Bell & Pavitt, 1997; Imbs & Wacziarg, 2003). In endogenous growth theory, R&D is the main source of innovation and growth (Rostow, 1971; Sandven, Smith, & Kaloudis, 2005; Verspagen, 1995; Von Tunzelman, 2005; Von Tunzelman, 2005; Wang & Blomstrom, 1992)). As Lin and Rosenblatt (2012) point out in their studies, this may be less applicable to developing countries, and so the question arises whether endogenous growth theory pays sufficient attention to the economic catch-up effect? Our assumption is that technology modernization is a multidimensional process (Akamatsu, 1962; Dutrenit, 2000; Grossman & Helpman, 1991; Kruger, 2008). By this we mean that it is based on a broader understanding of innovation that goes far beyond R&D (Cohen & Levinthal, 1990; Fagerberg & Godinho, 2005; Hobday, 1995; Koopmans, 1947; Lall, 1992). It is also a multilevel process, that is, it has micro-, meso-, and macro-levels (Matrizaev, 2018a, 2018b; Sturgeon & Geref, 2009). It is based on structural changes in various dimensions: technological, industrial, and organizational (Lee & Kim, 2009; Makino, Lau, & Yeh, 2002; Nelson, 2018). Finally, its form was highly influenced by global forces embodied in international trade and investment flows, interacting with local strategies of host and government firms (Ernst, 2013; Radosevic & Yoruk, 2016; Radosevic & Yoruk, 2018). RESULTS The ICT policy is aimed at creating an enabling socioeconomic environment for development and growth. Since the second decade of the new millennium, most OECD countries and their partner countries have been actively involved in the race to develop national strategies to identify policy priorities related to the digital economy. Of the 340 countries that responded to the OECD questionnaire on “prospects for the digital economy-2015” (OECD, 2015), 273 have a comprehensive national digital strategy, many of which were developed or revised in the period from 2013 to 2014. However, some countries do not have a common strategy either because it is being developed or revised (Austria and Switzerland) or because their digital economy policy includes several strategies related to specific issues and/ or sectors that together form the national structure of the digital economy (The Russian Federation and the United States). National digital strategies tend to be cross-sectoral and, in many cases, directly aimed at improving the competitiveness, economic growth, and social well-being of countries. For example, the Danish ICT growth plan is designed to support “growth in the ICT sector as well as ICT-based growth in the private sector as a whole.” Germany’s digital strategy for 2014–2017

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emphasizes “expanding the potential of innovation to achieve further growth and employment” as a key objective (in addition to strengthening high-speed networks and confidence). Needless to say, the high activity in the development of the digital economy observed in the OECD countries is primarily caused by the fall in economic growth and the need for structural adjustment due to increasing competition in the global market after the 2008 crisis. As is well known, the crisis of 2008 led to the emergence of a new phenomenon in the global economic system—a new macroeconomic reality. According to OECD data and forecasts, the global economy center of gravity is moving to the South-East, that is, from OECD member countries to countries with fast-growing markets. In OECD studies, this phenomenon is referred to as the axis of shifting welfare (author’s translation). Among a small number of fast-growing economies, recent attention has been focused on the so-called BRICS countries—Brazil, Russia, India, China, and South Africa. However, it is not entirely convincing and obvious that the commonplace distinction between developed and developing economies will be appropriate to understand the future growth trajectories of developing economies. Similarly, the integration of all BRICS countries “into one basket” can greatly confuse us in understanding the differences in their growth trajectories. The sustainability of the growth of emerging market economies and BRICS countries, in particular, depends on the degree of their technological modernization, and this question cannot be answered unambiguously for all emerging market economies or for all BRICS countries. Will the initial openness of countries with fast-growing markets (countries of the so-called axis of shifting welfare) extend to the shifting welfare axis II or does sustained technological growth require a more detailed study of each of these countries separately? Our four general hypotheses on the characteristics of technological reequipment of middle-income economies, such as BRICS, allowed us to apply a three-pronged approach to technology modernization, using various patent indicators to test these hypotheses. Our data show that hypotheses one and three were confirmed in the case of BRICS economies, as there are general trends—an increase in the intensity of technologies reflected in the accumulation of innovative potential (the first hypothesis) and an increase in the diversification of technological knowledge (the third hypothesis). The second hypothesis about the change in the structure of technological knowledge (expressed by the increase in the share of high-tech and knowledge-intensive patent applications) was confirmed in all BRICS countries except Russia. The study showed that all BRICS countries have strengthened their activities in the field of advanced technologies. The fourth hypothesis concerns the global interaction in the process of technological modernization and the role of organizational capabilities. It is

Macro-Planning of Innovative Strategies in BRICS Countries     637

assumed that as countries’ incomes increase and technological capabilities upgrade, they can move from a stage where foreign companies play an important role in protecting and exploiting the commercial potential of national inventions to a process of collaborative knowledge generation and technology search from abroad. Indeed, our data show that before the globalization of the 1990s–2000s, the BRICS economy demonstrated relatively high dependence on foreign actors in its advanced technologies compared to developed economies. Over time, this dependence decreases. Also, the relative (not absolute) reduction in international joint inventions is interpreted as a decrease in the dependence of the BRICS economies on technology transfer. In this respect, the differences between the BRICS countries are very significant, indicating varying degrees of dependence on technology transfer in the development of advanced technologies, as well as on various international strategies in the process of their technological modernization. The BRICS countries have increased their ability to find technology suppliers, but we do not see any lag in this regard, nor an increase in organizational capabilities to attract technology from abroad. CONCLUSIONS In general, trends in global interaction indicate that the institutional capacity or complementary assets of the BRICS economies are still significantly low compared to the United States and the EU15. In this regard, our fourth hypothesis has not been fully confirmed. The novelty of our research lies not only in describing trends and ranking BRICS, but also in a deeper understanding of the profiles of their technological modernization over time, which in turn can help us understand the prospects for their long-term growth. The fifth hypothesis suggests that the interaction between the proposed three aspects of technology modernization leads to the development of specific national ways and profiles of technology modernization. Practice shows that there is no single way of technological modernization within the BRICS group. China is unique among the BRICS countries in terms of technological intensity (among leaders and those countries which try to catch up with them), very rapid increase in technological intensity, rapid structural changes in the direction of dynamic advanced technologies and technological diversification. A significant increase in China’s intensity of advanced technologies, as well as evidence of the diversification of technological knowledge in China, are similar to observations made by Lee for South Korea and Taiwan in earlier periods of successful and rapid technological modernization (Lee, 2013). Russia and South Africa showed relatively low dynamics with minor improvements in the introduction of advanced technologies, while increasing

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the breadth of technological modernization (although less than India). The significant difference between the two economies is that Russia (though much smaller than China) has increased the relative scale of “quasi-advanced” technological activity, which has almost disappeared in South Africa. In general, our analysis applies a new conceptual approach to the study of ways of technological modernization of middle-income economies on the example of the BRICS economies. We have developed a new statistical methodology that is suitable for examining the extent to which different ways of upgrading technology are the basis for long-term sustainable growth. As a multidimensional structure, it allows for a comparative analysis of technological progress while maintaining the link between indicators and concepts. This should make our approach a useful assessment tool to be used for innovation policy implementation. This is both conceptually and theoretically ambitious and can be further developed theoretically. The main limitation of our analysis is that it is based on general hypotheses relevant to middle-income economies. They are tested only in the BRICS economies and cover only innovative opportunities for technology modernization, but not research and applied opportunities. REFERENCES Aghion, P., & Howitt, P. (1992). A model of growth through creative destruction. Econometrica, 60(2), 323–351. Akamatsu, K. (1962). A historical pattern of economic growth in developing countries. Devoted Economy 1(1), 3–25. Bell, M. (2009). Innovation capabilities and directions of development (STEPS Working Paper). Working Paper, 33, 18–25. Bell, M., & Pavitt, K. (1997). Technological accumulation and industrial growth: Contrasts between developed and developing countries. In D. Archibugi & J. Michie (Eds.), Technology, globalisation and economic performance (pp. 25–45). Cambridge, MA: Cambridge University Press. Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35(1), 128–152. Dutrenit, G. (2000). Learning and knowledge management in the firm: From knowledge accumulation to strategic capabilities. Aldershot, England: Edward Elgar. Ernst, D. (2013). Industrial upgrading through low-cost and fast innovation—Taiwan’s experience (working paper). Honolulu, HI: East–West Center. Fagerberg, J., & Godinho, M. (2005). Innovation and catching-up. In D. C. Mowery, J. Fagerberg, & R. Nelson (Eds.), The Oxford handbook of innovation (pp. 514– 543). New York, NY: Oxford University Press. Grossman, G., & Helpman, E. (1991). Innovation and growth in the global economy. Cambridge, MA: MIT Press. Hobday, M. (1995). East Asian latecomer firms: Learning the technology of electronics. World Dev, 23(7), 1171–1193.

Macro-Planning of Innovative Strategies in BRICS Countries     639 Imbs, J., & Wacziarg, R. (2003). Stages of diversification. American Economy Review, 93(1), 63–86. Koopmans, T. C. (1947). Measurement without theory. Review Economic Statistics, 29(3), 161–172. Krüger, J. J. (2008). Productivity and structural change: A review of the literature. Journal of Economic Survey, 22(2), 330–363. Lall, S. (1992). Technological capabilities and industrialization. World Development, 20(2), 34–42. https://doi.org/10.1016/0305-750X(92)90097-F Lee, K., (2013). Schumpeterian analysis of economic catch-up: Knowledge, path-creation, and the middle-income trap. Cambridge, MA: Cambridge University Press. Lee, K., & Kim, B. (2009). Both institutions and policies matter but differently for different income groups of countries: Determinants of long-run economic growth revisited. World Development, 37(3), 533–549. Lin, J. Y., & Rosenblatt, D., (2012), Shifting patterns of economic growth and rethinking development. J. Econ. Policy Reform., 15(3), 171–194. Makino, S., Lau, C., & Yeh, R. (2002). Asset-exploitation versus asset-seeking: Implications for location choice of foreign direct investment from newly industrialized economies. Journal of International Business Study, 33(3), 403–421. Matrizaev, B. D. (2018a). Global innovation leadership: Macrocontures and modeling of its conceptual framework. Municipal Academy, 1, 85–91. Matrizaev, B. D. (2018b). Macrostrategies of innovative development and global economic growth: Macroeconomic analysis, trends, forecasts. Moscow, Russia. Nelson, R. R. (2018). Recent evolutionary theorizing about economic change. Journal of Economic Literature, 33(1), 48–90. OECD. (2015a). OECD compendium of productivity indicators 2015. Paris, France: Author. http://dx.doi.org/10.1787/pdtvy-2015-en Radosevic, S., & Yoruk, E. (2016). Why do we need a theory and metrics of technology upgrading? Asian Journal Technological Innovation, 24, 8–32. Radosevic, S., & Yoruk, E. (2018). Technology upgrading of middle-income economies: A new approach and results. Technological Forecast Social Change, 129, 56–75. Romer, P. M. (1990). Endogenous technological change. Journal of Political Economy, 98(5), 71–102. Rostow, W. W. (1971). The stages of economic growth: A non-communist manifest (2nd ed.). Cambridge, MA: Cambridge University Press. (Originally published in 1960) Sandven, T., Smith, K., & Kaloudis, A. (2005). Structural change, growth, and innovation: The roles of medium and low tech industries, 1980–2000. In H. HirschKreinsen, D. Jacobson, S. Laestadius, K. Smith, & P. Lang (Eds.), Low-tech innovation in the knowledge economy (pp. 31–63). Frankfurt, Germany: Peter Lang. Solow, R. M. (1957). Technical development and aggregated production function. The Review Economy Become, 39(3), 312–320. Sturgeon, T. J., & Geref, G. (2009). Measuring success in the global economy: International trade, industrial upgrading, and business function outsourcing in global value chains. Translated Corporations, 18(2), 1–35. Verspagen, B., (1995). A new empirical approach to catching up or falling behind. Structural Change Economic Dynamics, 2(2), 359–380.

640    B. J. MATRIZAEV, L. M. ALLAKHVERDIEVA, and M. K. SULTANOVA Von Tunzelmann, G. N. (1995). Technology and industrial progress: The foundations of economic growth. Aldershot, England: Edward Elgar. Von Tunzelmann, N., & Acha, V. (2005). Innovation in ‘low-tech’ industries. In J. Fagerberg, D. Mowery, & R. Nelson (Eds.), The Oxford handbook of innovation (pp. 407–432). Oxford, England: Oxford University Press. Wang, J., & Blomström, M. (1992). Foreign investment and technology transfer. European Economic Review, 36(1), 137–155.

CHAPTER 73

FEATURES OF PROCESS OF FORMATION OF INNOVATIVE ECONOMY ON THE BASIS OF ARTIFICIAL INTELLIGENCE Svetlana V. Panasenko Plekhanov Russian University of Economics Vyacheslav P. Cheglov Plekhanov Russian University of Economics Elena A. Mayorova Plekhanov Russian University of Economics Alexander F. Nikishin Plekhanov Russian University of Economics

ABSTRACT This chapter presents the results of a study aimed at determining and systematizing the key features of the process of formation of an innovative economy based on artificial intelligence (AI) and identifying problems of their implementation Meta-Scientific Study of Artificial Intelligence, pages 641–648 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

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642    S. V. PANASENKO et al. in practice. The following features were highlighted: the use of AI as a new factor of production and its significant impact on other factors, the transformation of all sectors of the economy based on artificial intelligence, improving communication with customers, the creation of personal product offers for customers, price optimization, inventory, and assortment management. The problems of implementation of AI in practice are identified as the following: the lack of practical experience and relevant professional skills, the need for a substantial initial investment, the inadequacy of the operating budget for implementation of innovations on the basis of artificial intelligence the complexity of assessment of its impact, and insufficient staffing. The conclusions about the need to popularize the ideas of artificial intelligence, the spread of best practices of its use, training the necessary specialists were made in the study.

The research topic of the process of formation of innovative economy based on AI is highly relevant and demanded in connection with global changes in modern economic systems both at the world and at the national levels on the basis of knowledge-intensive technologies that significantly transform many elements, including factors of production, consumer demand and behavior, communication channels with consumers, and so on. Innovations related to the use of AI are not evolutionary (gradual and smooth), but revolutionary (radical and strong) innovations that allow to leave those competitors behind who use the old (traditional) forms and tools of economic activity. The purpose of the authors’ research is to determine and systematize the key features of the process of formation of innovative economy based on AI and identify problems of their implementation in practice. METHODOLOGY The methodology of the study included clarification of the conceptual categorical apparatus of AI, the use of the system method, as well as methods of analysis and synthesis, which allowed us to consider different directions of the use of AI both separately and in their synergistic relationship. In addition, we used situational, statistical, comparative, and expert methods, monographic, functional benchmarking, graphical and tabular, as well as block grouping and systematization. RESULTS The definition of AI has different interpretations, but in the modern sense, most authors agree that this technology is a powerful tool for transforming economic systems through the creation of machine intelligence capable of

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performing creative activities, which is exclusively human prerogative in various fields, based on reasonable ideas under difficult conditions of rapidly growing information and processing a huge array of data (Averkin, 1992; Bae, 2017; Ferreira, 2016; Ilyin, 2017; Kwon, 2017; Russell, 2006; Shamshad & Sukrat, 2018, and others). At the state level in Russia in the strategy of development of information society in the Russian Federation for the years 2017–2030, AI is understood as the main direction of development of the Russian information and communication technologies (together with those such as convergence of communication networks and creation of communication networks of new generation; processing of large volumes of data; technologies of electronic identification and authentication, including in the credit and financial sphere; cloud and fog computing; Internet of things and industrial Internet; robotics and biotechnology; radio engineering and electronic component base; and information security). The pace of development of AI technology is in accordance with the exponential law, allowing economic systems to develop more quickly. In early May 2017, the analytical company Tractica published a forecast, according to which it is expecting the rapid growth of the world market of AI technologies from $1.4 billion in 2016 up to $59.8 billion by 2025. Based on the analysis of the activities of almost 300 companies associated with work in the field of artificial intelligence, the forecast of annual revenue growth from corporate AI applications from $3.7 billion worldwide in 2017 to $80.7 billion in 2025 was presented. Key features of the process of formation of innovative economy on the basis of AI can be combined in five groups: 1. The first group is a group of changes in factors of production (the emergence of a new factor and its influence on others). It should be noted that the factors of production in the economy as a whole (i.e., resources used to create life benefits) traditionally include land, human labor, capital, entrepreneurial activity (entrepreneurial abilities), and information. 2. The second group is connected with the transformational impact of AI on each sector of the economy. For example, the forms of influence on some of them are given in Table 73.1 (Panasenko & Kazantseva, 2018; Panasenko & Mkrtchyan, 2018). According to the authors’ study, on the one hand, the impact of AI occurs locally (in each industry separately), and at the same time, on the other hand, the rapid increase in such changes leads to a synergetic effect in the form of a new quality of the economic system, a new technological era (fourth industrial revolution, Industry 4.0).

644    S. V. PANASENKO et al. TABLE 73.1  The Impact of AI on the Economy The Name of a Branch

Forms of Influence of AI on the Economy

1

Medicine

The creation of various neural chips, new apparatus for medical diagnosis and expert work, medical operations, development neuromedicine equipment and prostheses, the application of neuroprostheses limbs and sensory organs, improvement in the treatment of diseases based on the medcines, integrating into subcellular structures, etc.

2

Security area

The creation of neurocomputers in the regulation of chemical reactions, control processes in civil and military aviation, development of new devices for luggage control and prevention of terrorist actions, creation of neuroprograms for recognition of faces and emotions of people in crowded places (airports, stations, ports, etc.), person identification systems, voice recognition, car numbers, analysis of aero-space images, monitoring of information flows, etc.

3

Trade, sales, marketing, logistics, entrepreneurship

The development of associative search for information, the use of digital assistants and agents, filtering information in the information systems and social networking sites (e.g., tracking of actual reviews on the product and their level of emotion), collaborative filtering, classification of news feeds, processing of handwritten checks, recognition of signatures, fingerprint and voice, forgery-detecting in payment systems, handling large amounts of data, security of transactions on plastic or neuro-cards, intellectual approaches to merchandising, etc.

4

Entertainment industry (eSports, show business)

The activation of the use of neuro interfaces in input and output devices, control of game characters based on the use of brain signals of participants, etc.

5

Education

The development of memory and intelligence, advanced training techniques, the use of neuro-compensatory devices for the training of persons with disabilities, implantation of neural chips, the creation of self-learning and self-adjusting neural networks, unique expert systems, etc.

3. The third group is associated with individualization of communicative interaction with the buyer. In this group, firstly, it should be noted the capacity of AI to create more personalized content for each client and individual product offers. This can be achieved by using chatbots, or by giving the opportunity to the buyer to purchase by voice (in fitting rooms, etc.). Secondly, AI can significantly intensify sales through mobile applications. Sales can increase significantly if, based on artificial intelligence, we offer the customer the good that serves his interest best. Thirdly, AI can considerably improve the omnichannel system. The number of metrics of the

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buyer’s behavior in different sales channels is growing rapidly and only AI technologies can deal with them. Fourthly, AI makes it possible to obtain additional information about customers, optimize segmentation processes, identify target customers and offer them unique products, make forecasts of demand and consumption of goods in the market, and more accurately determine consumer preferences (Kevorkova, Shinkareva, Panasenko, Nikishin, & Mayorova, 2018). Fifthly, taken into account the growth in information noise and considerable psychological pressure of intrusive advertising from manufacturers, individual offer, of course, significantly increases the likelihood of sale. Sixthly, the emergence of virtual assistants in communication with customers significantly has greatly facilitated the process of product search and purchase. 4. The fourth group is referred to as price optimization. The use of AI can help in automatic adjustments of the price based on market conditions and other data such as weather conditions, holidays, sales, competitors’ advertising campaign, and a huge number of other factors. 5. The fifth group is linked with inventory and assortment management. Inventory is the key to high efficiency and business sustainability. AI-based analytics help predict demand, maintain efficient and reliable inventory, and resupply it automatically. Practical examples include the following: Blue Yonder has developed a system that can analyze about 3 billion historical transactions and take into account 200 additional variables (such as weather, website requests, etc.) to predict future purchases. The accuracy of the prediction whether a particular item will be sold within a 30-day period reaches 90%. Such accuracy makes it possible to achieve a significant optimization of inventory and increase goods turnover volume. It is worth noting that this solution is used by the German retailer OTTO. Simbe Robotics creates robots that record the violation of planograms, the lack of goods, and even the nonconformity of the goods and the price tag. For small shops, instead of robots, you can use conventional cameras, the image from which will be analyzed in the cloud service and monitor the replenishment of the goods and the compliance of visual merchandising. The French company Vekia has developed an application for supply chain management and is already working with Leroy Merlen, Etam, Okaidi, and Jacadi. This application uses AI to manage and control the goods turnover for each store, performing a daily assessment of assortment by several parameters. It calculates the optimal stock level for each location several times a day. On the basis of the received data, Vekia is able to form an order. The Plan Optimization application helps to understand how different prod-

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ucts affect the overall product range. Diwo, for those products that show low sales, identifies the factors that led to that result and offers a variety of strategies that help determine the optimal time to start promotions or sales. The authors’ study showed that there are many business benefits of AI technology, and at the same time, there are various risks of using AI, some of them are listed in the centennial report on AI (Grosz, 2016). In addition, according to a survey conducted by Forrester, there are some obstacles to the practical application of AI. Figure 73.1 shows the obstacles for business companies (Press, 2017). According to surveys, the greatest difficulties in organizations for the practical use of artificial intelligence, presented in Figure 73.1, are the lack of such experience (42% of respondents), lack of clarity about which areas of AI should be used (39%, respectively), lack of relevant professional skills and abilities (33%), the need for significant initial investment (29%), insufficient budget (23%), and other reasons.

There is not enough understanding of what AI is

3

There is no access to required data

8

A lot of hype around AI

11

There are no proper management processes

13

Lack of proof of AI systems

14

There is no knowledge of what is needed to implement the AI system

19

Insufficient expenditure budget

23

First, investment in the modernization of the data platform is required

29

There no skill required

33

It is not clear how AI can be used

39

There is no specific experience

42 0

5

10 15 20 25 30 35 40 45

Figure 73.1  Results of the study of obstacles to the practical use of artificial intelligence, %.

Features of Process of Formation of Innovative Economy on the Basis of AI    647

It should be also noted the problems associated with the lack of funding for AI development, testing, and implementation. There are great difficulties arising from the lack of competent personnel (specialists in artificial intelligence). CONCLUSIONS Thus, AI is understood as a technology that significantly transforms the economy on the basis of reasonable actions of machine devices capable of performing creative activities, which is exclusively human prerogative. There are several main areas of AI application: creation of natural language texts, speech recognition, creation of virtual agents, machine learning platforms, hardware, decision management, multilayer neural networks, ensuring more natural interaction between people and machines, robotic automation of processes, natural language processing, and text analysis. At the same time, the pace of distribution of AI-based developments is quite high. Key features of the process of formation of innovative economy on the basis of AI can be combined in five groups: the group of changes in factors of production (AI as a new factor and influencing others), the group of transformational impact of AI on each sector of the economy and contributing to the emergence of a synergetic effect in the form of a new quality of the economic system (Industry 4.0), the group of individualization of communicative interaction with the buyer, the group of price optimization, and the group of inventory and assortment management. Obstacles to the practical application of AI may arise both from companies and from researchers. The solution to these problems lies in the distribution of the positive experience of the leading companies in this area; conducting training courses, training sessions, master classes; and in the transition from quantitative indicators to assess the development of AI to high quality standards, which ensures the elimination of the incomparability of heterogeneous indicators. In addition, it is necessary to popularize the ideas of AI, purposeful efforts to train professional personnel in this field, and increase funding for artificial intelligence. In combination, these measures can lead to the creation of more optimal conditions for the implementation of AI achievements in both the world and the Russian economy.

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REFERENCES Averkin, A., Haase-Rapoport, M. G., & Pospelov D. A. (1992). Explanatory dictionary of artificial intelligence. Retrieved from http://www.raai.org/library/tolk/ aivoc.html Bae, J. (2017). The rising influence of artificial intelligence and algorithms in food science. Turkish Online Journal of Educational Technology, December Special Issue, 890–896. Ferreira, K. J., Lee, B. H. A., & Simchi-Levi, D. (2016). Analytics for an online retailer: Demand forecasting and price optimization. Manufacturing and Service Operations Management, 18(1), 69–88. https://doi.org/10.1287/msom.2015.0561 Grosz, B. (2016). One hundred year study on artificial intelligence (AI 100). Stanford, CA: Stanford University. https://ai100.stanford.edu Ilyin, N. I., Malinetsky, G. G., Kolin, K. K., Zatsarinny, A. A., Raikov, A. N., Lepskiy, V. E., & Slavin, B. B. (2017). Distributed situational centres system of cutting edge development. Paper presented at the Proceedings of 2017 10th International Conference Management of Large-Scale System Development. https://doi .org/10.1109/MLSD.2017.8109638 Kevorkova, Zh. A., Shinkareva, O. V., Panasenko, S. V., Nikishin, A. F., & Mayorova, E. A. (2018). Prospects for Russian vending sector development based on consumer preference analysis. International Journal of Civil Engineering and Technology, 9(10), 1169–1175. Kwon, Y. J. V. (2017). The rise of AI in economics theory. Turkish Online Journal of Educational Technology, December Special Issue, 885–889. Panasenko, S. V., & Kazantseva, S. Yu. (2018). The role and value of neurotechnology in the digital economy. Moscow, Russia: Publishing House of the Russian University of Economics. Panasenko, S. V., & Mkrtchyan, V. S. (2018). Prospects for the use of nanotechnology in various sectors of the digital economy. Russian Entrepreneurship, 19(11), 3269–3278. Press, G. (2017, January 23). Top 10 hot artificial intelligence (AI) technologies. Retrieved from https://www.forbes.com/sites/gilpress/2017/01/23/top-10-hot-artificial -intelligence-ai-technologies/#7f9e81861928 Russell, S., & Norvig, P. (2006). Artificial intelligence: Modern approach. Moscow, Russia: Williams. Seifullaeva, M. E., Shirochenskaya, I. P., Shklyar, T. L., Mkhytaryan, S. V., & Panasenko S. V. (2017). Strategy of import substitution at Russian food market. International Journal of Economic Perspectives, 11(3). Retrieved from https:// www.elibrary.ru/item.asp?id=35507256 Shamshad, A. (Ed.). (2017). Artificial intelligence and broadband divide: State of ICT connectivity in Asia and the Pacific (Analytical Report). Bangkok, Thailand: United Nations Economic and Social Commission for Asia and the Pacific. Sukrat, S., & Papasratorn, B. (2018). An architectural framework for developing a recommendation system to enhance vendors’ capability in C2C social commerce. Social Network Analysis and Mining, 8(1). https://doi.org/10.1007/ s13278-018-0500-7

CHAPTER 74

PROSPECTS AND PITFALLS OF INNOVATION DEVELOPMENT Alexey M. Kornilov Financial University, Moscow, Russia

ABSTRACT Recently the idea of innovative development has come to be perceived as a sort of universal remedy against all economic maladies scale and complexity notwithstanding. At the same time, at the conceptual level, it remains a rather controversial solution meant to ensure intensive growth in a closed autarkic economic system. Both in theory and in practice, the concept tends to degenerate into a kind of fake, imitational development capable of producing nothing but financial bubbles. Overcoming the vulnerabilities of the innovation economy would require thorough reorganization of the main resource for generating new knowledge—personnel of R&D sector—as well as fundamental changes in the goal setting of the scientific and technological complex, criteria for evaluating its economic and social efficiency.

Innovative development has recently become synonymous with a certain universal solution to any problems plaguing the world economic system as a whole as well as its national components. Moreover, this happens not only in the mass consciousness but also in a special discourse of professional

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economists and government officials. Such hopes may seem overly optimistic given the fact that the most striking achievement of innovative development—digital economy—debuted as a key factor in the global economic dynamics in the form of dotcom meltdown (March 10, 2000). Due to the latter, the American sector of high-tech goods and services alone lost between 2000 and 2002 more than 5 trillion dollars, and the long-term negative consequences of the meltdown on a global scale have yet to be fully overcome. It is highly unlikely the event was merely an accident. Tom Goodwin, vice president of innovation for Havas Media, has once observed that the flagships of innovation development have one feature in common: “Uber, the largest operator on the taxi market, does not have its own car fleet, Facebook does not create its own content on media, Alibaba, the largest retailer , there is no own inventory, Airbnb, the largest provider of housing, does not own real estate.” Indeed what kind of a growth one is to expect of something so ephemeral in the first place? METHODOLOGY In order to properly assess the benefits and risks innate in the innovative economy, it is worth understanding the origin of the latter—not so much as a special form of economic turnover but rather on a conceptual level, as a way to ensure sustainable economic growth. To this end, it seems fit to rely predominantly on thematic analysis—diachronic in perspective and nomothetic in scope—along with more general tools of research: deductive reasoning and systemic approach. RESULTS The concept of innovative economy (CIE) stands somewhat alone in economic theory. Embracing the entire spectrum of relations between the market, the state, society, and the individual, in the final analysis it—unlike other “framework” political economic doctrines—was originally created as a response to just one, albeit the most important problem of economic discourse—the fundamental limitation of economic growth. Economic growth is commonly understood as an increase in the market value (adjusted for inflation) for a certain period of time of goods and services produced in the framework of a complex economic system. In modern conditions determined primarily by the factor of globalization, the problems of economic growth mainly boil down to the question of its limits—and even more to its fundamental limitations. It is no secret that the “ideal” globalized economy, completely free from administrative and customs-tariff remnants of the era

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of national states, has all the signs of autarky, which as we know, inevitably brings economic development to a halt, gradually replacing its simple reproduction of value (von Böhm-Bawerk, 1921). The mainstream of economics could offer nothing of the kind. From the point of view of its basic propositions—even cleared of Ricardian pessimism—the growth was something irrelevant, since the Hicks–Hansen model (see Figure 74.1) applies equally well to both growing and contracting economy—not to mention the one whose internal dynamics is limited to a purely homeostatic pulsation. All growth resources—including technical progress—and even factors of production—with the exception of capital perhaps—serve no more than a source of disturbances within this model that can bring an economic system out of its ideal state—equilibrium. The so-called exogenous models (e.g., Solow–Swan) made economic growth entirely dependent on production efficiency, without, however, explaining the reasons for the increase in the latter but simply taking for granted (Solow, 1956), which would be extremely unorthodox when applied to a system with zero input—that is, without any external feed of production factors—and constantly decreasing sources of investment that could be used to increase labor efficiency.

Figure 74.1  Hicks–Hansen model. Note: IS = investment–saving curve; LM = liquidity preference–money supply; I = interest rates; Y = real GDP. Not that wherever IS moves basic assumption of the model–relation between I and Y remains true.

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The endogenous models (Romer, 1990) reduced the problem of increasing labor efficiency exclusively to the development of the R&D sector. This assumption, however, clearly contradicted the empirical evidence: in the same United States from 1945 to 1990, there was a fivefold increase in the number of research personnel yet concurrent dynamics of the economic growth show nothing of a kind (Gregersen & Johnson, 1997). Various solutions of ensuring growth for the world economy based on the hypothesis of catching-up development (Abramovitz, 1979; North, 1981; Dalum, Johnson, & Lundvall, 1991), while retaining all the shortcomings of their paternal Solow–Swan model, agreed with reality even less. For one thing, in practice, the poor countries have shown miracles of economic growth (that is, anything beyond purely statistical effects) rather than as an exception. More often, they tended to combine short-living booms of purely raw material etiology with long-lasting recessions, limiting all their “development” to the import of high-tech products. Accordingly, in order to somehow explain this blatant inconsistency between theory and practice, the catch-up development hypothesis had to be supported by extremely controversial arguments of a cultural, political, and other nature for all intents and purposes unsuitable for an objective assessment and analysis. And even if we stipulate that poverty really serves as an absolute competitive advantage in the economic race, this resource by its very nature soon will be exhausted. The accelerated growth rates (beyond the low-base effect) would inevitably smother their very raison d’être—poverty. And as we know from cases of Japan, Taiwan, South Korea, and most recently of China, this development doesn’t take long—even from the layman perspective. Schumpeter’s discourse although much predating all of these seemed to either resolve or circumvent all the limitations to growth that plagued both classical political economy and neoclassical economics. Constant creative destruction both driving and driven by unceasing innovative activity made growth pretty much conditioned upon itself (Schumpeter, 1934). Yet Schumpeter’s contemporaries reacted to the CIE rather frostily. The very idea of economic development by way of solely recombining available resources especially when promted by nothing but ideas way ahead of their time sounded almost clerical—as an assertion of the priority of the spiritual element over the material and the application of creatio ex nihile to the genesis of commodity value. An attitude of the mainstream of economics to CIE was summed up in an article by Milton Friedman (1970) in The New York Times Magazine: “There is one and only one social responsibility of business—to use its resources and engage in activities designed to increase its profits” (para. 33) and corporations that will do something else are sure to lose competitiveness—to the detriment of their owners, workers, and society as a whole. Curious to note that these words were written in 1970—three years after the publication of

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Daniel Bell’s article, “Notes on the Post-Industrial Society” (Bell, 1967), where the main parameters of an innovative economy—albeit in a slightly different terminological outline—are considered if not yet given then definitely inevitable. The scholarly community has truly appreciated the contribution made by Schumpeter to economic theory only when new phenomena and trends consonant with CIE became so visible in the world economy that economics had no choice but to change research paradigm. This happened against the background of the rapid development of various concepts “parallel” to Schumpeterian discourse, based on somewhat different premises but in terms of conclusions—vision of the future—differing mostly in terminology. One major result of this development thought was that the concept of innovation ​​ became firmly associated with technological progress thus assuming its modern look. The substantial changes that occurred in the postwar period in the developed economies of the West quite predictably manifested for the first time and, more importantly, were theoretically understood in the United States. The first to grasp them were futurologists: in 1971, a monograph by Alvin Toffler was published in which the new social phenomena and deformations caused by a change in the economic paradigm were described in general terms (Toffler, 1971). In his later work, the author has even proposed the exact date that the postindustrial era started: 1956, when the proportion of white-collar workers (or cognitive society as one would put it today) in American workforce for the first time exceeded the factory workers. In forms academically more rigorous, the transformation and deconstruction of the industrial economy and society as a whole was described by Daniel Bell (1973) in the monograph “The Coming Post-Industrial Society.” To illustrate how the concept of Bell’s postindustrial society is consistent and consonant with CIE—moreover emerges with logical inevitability from the theoretical assumptions laid out by Schumpeter—it would suffice to refer to the definition given to the phenomenon by the author himself: The “post-industrial” society [is] one in which the economy had moved from being predominantly engaged in the production of goods to being preoccupied with services, research, education and amenities; in which the professional-technical class had become the major occupational group; and-most importantly-in which innovation . . . increasingly dependent on advances in theoretical knowledge. (Bell, 1967, p. 102)

The theory of the postindustrial society caused a huge resonance in the research community—primarily among economists and sociologists—however, the reaction it met was mixed. The major objections could be boiled down to the alleged apophaticism—the description of the future state of society mainly through the negation of the present. Bella’s critics, accordingly,

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focused on the essential features of the new phase of socioeconomic development offered by him: • informatization—a substantially wider distribution in all spheres of life of information technology; • singling knowledge generation out in a particular sector of the economy, with the university as its main structural element; • replacing mass standardized industrial production with flexible small-scale; and • blurring the boundaries of traditional classes and social groups by means of universal equal access to the key element of economic turnover—knowledge—until former elites are finally replaced with the new: highly mobile with their status based solely on competence. Preference was initially given to the first one thus marking the beginning of the concept of the information society. The latter was introduced in 1972 by the Japanese researcher K. Koyama; however, the work of another Japanese scholar, Masuda Yoneji, made it truly popular—along with the works of American futurologists John Naysbit (1988, Megatrends: Ten New Directions Transforming Our Lives) and the aforementioned Alvin Toffler (1990, Powershift). Subsequently the idea of the information society organically agglomerated with the concept of the knowledge society, which basically reduced postindustrialism to the second of the Bells’ criteria to establish the change of phase in socioeconomic development, that is, rendering knowledge generation a sector of the economy in its own right (Umesao, 1969). The combination of these approaches may be defined as the second edition of CIE, fundamentally differing from the original Schumpeterian discourse only in the rigid binding of innovations to the field of R&D. In this updated version, innovative development seemed to be the ideal recipe for sustainable economic growth on a global scale. Deconstruction in 1971–1978 of Bretton–Woods system eliminated last objections to the most vulnerable cluster of Schumpeter’s ideas—financial. Now his scheme not only ceased to look exotic but more than that—ideally corresponded to the financial basis of consumerism. In the new exposition of CIE (Boulding, 1991; Pasinetti, 1981; Kuznets, 1971), creative destruction as an inexhaustible source of development was somewhat relegated to the background by a new resource, visibly unlimited as well—the creative potential of human consciousness. As for the objections raised against the endogenous growth models: the absence of a strict correlation between the volume of investment in the scientific and technological complex and macroeconomic dynamics—all of them were removed by relying on the concept of maintaining a certain (high) “density of the innovation stream,” that is, by shifting emphasis from the creation of scientific products to their commercialization (see Table 74.1).

Prospects and Pitfalls of Innovation Development    655 TABLE 74.1  Normal Efficacy of Bona-Fide Innovations, by Stage Percentage Reaching the Stage

Stage of Innovation Traversing “Valley of Death” (resulting in market tested products)

9.1

Feasibility (generating no loss)

2.7

Minor success (generating at least minuscule profit)

1.5

Major success

0.6

Source: Based on analysis of EU IPR-Helpdesk statistics by Klaus G. Saul

It is worth noting that detailed studies of the latter concept have revealed the key vulnerability of CIE 2.0. In 1997, Christensen and Leslie, in their monograph The Innovator’s Dilemma: When New Technologies Cause Great Firms to Fail drew attention to the problem of inverse creative destruction, that is, disruptive destruction. The essence of his reasoning was that truly breakthrough innovations rarely bring financial success to their creators and as a rule are commercialized by epigones (see Table 74.2). True that the tendency toward pseudo-innovative development is more visible in countries with market elements in the economy historically depressed. In this regard post-Soviet Russia could serve as a kind of “inverse role-model.” Its desire to retain Soviet relevance in international affairs while simultaneously eradicating every memory of social obligations reminiscent of the Soviet era produced a kind of “pseudo-development political economy” that deserves more detailed consideration. The nature of the latter tends to what could be summed up as “compulsive modernization,” that is, persistent attempts on the part of Russia’s government to demonstrate resounding successes in the Science and Technology (S&T) sphere. By doing so it not only tries to divert attention from its otherwise fabulous inefficiency, grotesque corruption and virtually no feedback in public life but moreover seeks to establish its own legitimacy—clocking itself in a shadow of Soviet grandeur thus providing Russian statehood a kind of positive raison d’être. It is however a tricky thing to be bursting with S&T breakthroughs when technological lag accumulated TABLE 74.2  Percentage of Pseudo-Innovations in Pharmaceutics, for the Period of 2002–2011 Type of Innovation

Percentage

Disruptive (breakthrough)

0.2

Basic (real advance)

1.3

Improving (some advantage)

6.1

Pseudo-innovation

92.4

Source: Based on data in Prescribe International, 21(126), April 2012

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during the Cold War—sizable to begin with—is aggravated by senseless deconstruction of the Soviet legacy, downward spiraling and general archaization of the economy. Without real breakthroughs one has no choice but to fake them—by exaggerating secondary roles played by Russia’s personnel in international collaborations or better still reusing results of the research carried out way back in the Soviet era (e.g., those used to be off-limits for the general scholarly community due to the state secrecy considerations). Compulsive modernization combined with peculiar logic of Russia’s rent-seeking economy is best reflected in the two major formulas of pseudodevelopment: (a) participation in a state project is all the more beneficial for a private contractor the less the stated purpose of the contract are realized; (b)projects of S&T nature are the most profitable. The latter is true not only because the probability of achieving their result (at least on the first try) is by definition set rather low. More importantly, refurbishing some part of the vast Soviet S&T legacy to make it look like “the real thing” would cost at least 10 times less than to produce truly breakthrough scientific results—even with the entire aggressive PR, kickbacks, bribes, and other questionable overheads. As a result, even the over-publicized Russia’s militaryindustrial complex tend to produce novelties which at a closer look prove to be superficially upgraded leftovers of the Cold War R&D—often dating back as much as 50 or 60 years (like recent Avangard hypersonic ICBMbased glider or Poseidon nuclear-armed unmanned underwater vehicle) and embodied more in a digital form than something truly tangible. In the civil sphere, suit is followed by endogenous iPad (produced by reattaching a new label to an outdated and commercially unsuccessful American model), iPhone-killer (consisting entirely of Chinese components), and various technology-demonstrating Russian automobile engines—with the only part made in Russia being cast-iron hulls (see Table 74.3). It would be misleading, however, to reduce the Schumpeterian trap exclusively to post-Soviet aberrations. Developed economies are by no means immune to the temptation of imitating growth by promoting fake TABLE 74.3  Typical Breakdown of Residents’ Activity in Russia’s Technoparks and “Innovative Hubs” Type of Activity R&D in natural and technical areas of science

Expenses (%) 3.5

Production (mostly concrete and wooden building products)

43.1

Wholesale and retail trade (mostly pharmaceuticals, magazines, food, and beverages)

44.1

Services (mostly car maintenance, activities real-estate, sales, and advertising agents) Source: Based on Diesperova, 2017

9.3

Prospects and Pitfalls of Innovation Development    657

Figure 74.2  Hyperloop’s “predecessor” from Yakov Perelman’s “entertaining physics” (1916).

innovations and technologies. Some estimates (Brynjolfsson, Eggers, & Gannamaneni, 2018) show that in 2017, a consumer surplus generated by Facebook’s new free (and therefore hardly of any value to speak of) services alone “increased”—or rather blew up—US GDP growth from 2.06% to 2.17% (i.e., 21.3 billion USD), while eight other major commercial digital platforms—Alibaba, Airbnb, Instagram, LinkedIn, Skype, Snapchat, Twitter, and Uber—had a similar effect to the tune of 93.1 billion USD. Even the way some of the worlds’ most reputable innovators promote the S&T hype is not necessarily more sophisticated than the most corrupt postSoviet state corporations and semi-state entities. Suffice it to say that Ilona Masks’ hyperloop is hard to tell apart from a “project” of the electromagnetic train (in fact, an intellectual experiment meant to elucidate certain aspects of physics) from the Entertaining Physics by Yakov Perelman—a science pop book for teenagers published in 1916 (see Figure 74.2). CONCLUSIONS Does all the above mentioned mean that there is no real alternative in this dilemma, and CIE is nothing more than a way to legitimate financial bubbles? The future of the innovation economy entirely depends on whether it will be possible to provide a resource capable of ensuring the required density of the innovation stream with exclusively—or at least predominantly— meaningful, substantial innovations. And here, again, we will have to return to the problems of human resources development in the R&D sector. The lack of a visible correlation between economic growth and growth in the number of research and technical personnel reflects on the one hand the subordinate role that the R&D sector still retains in the postindustrial economy while on the other—the imperfect organizational forms

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of this sector. Drawing parallels with previous epochs, the S&T complex is still somewhere halfway between the craft organization of labor and manufactory. Creating an innovative economy—providing a continuous, dense stream of meaningful innovations—will require structural changes in its form equivalent to the transition from manufacture to factory production. To what extent is it possible to be brought about in the near future? Some relevant solutions lie on the surface. The first one has to do with the fact that ceteris paribus scientific research is the most productive when it pertains to the realm of professional activity as much as to the recreational one (leisure, hobby). In modern society, there is an enormous latent resource for generating new knowledge, that is, the demographically significant layer of people (a) with special training (higher education, often supplemented with a scientific degree) but due to the confluence of unfavorable life circumstances forced to quit scholarly carrier; and (b) those demonstrating enhanced intellectual abilities and natural aptitude for mental activity but for one reason or another devoid of proper training. In Russia, this resource is especially abundant—since most of its economically active population were brought up in the framework of the Soviet economy, with its highly developed—though very much deformed—S&T complex and extreme prestige of a “scholarly” carrier. Contemporary Russian economy is, however, able to make use of their training only to a very small extent—due mostly to the steep decrease in its military-industrial capacities that used to absorb the bulk of the highly qualified personnel. Judge for yourself: from 1990 to 2008, a number of researchers in Russia dropped from about a million to 393,000 and to 2017—80,200 with more than 27,000 made redundant the last 3 years. A decrease on such a scale could hardly be reduced to natural demographic changes—even combined with brain-drain factor. The latter is indirectly evidenced by the fact that from 1995 till 2005, Russian researchers numbering on average about 600,000 published only 286,000 reviewed articles—less than 0.05 articles per scholar! Left with no means to continue their studies and a salary of rather symbolic value, the bulk of these highly trained people had no choice but to leave S&T realm. Suffering from an acute social frustration (due to the loss of an elite status), they nonetheless remain accustomed—if not addicted—to the high standard of intellectual activity which they compensate by idly browsing social digital networking. The obvious way to utilize this demography is to intellectually involve it in crowdsourcing R&D projects—perhaps through some “antechamber” arrangement stylized as an elitist network with an emphasis on intellectual competition and recreation (a kind of Mensa International—online). This general strategy is currently pursued with a fair measure of success by, for example, major pharmaceutical firms. One thing that significantly facilitates the use of crowdsourcing mechanism for networking R&D is an impressive development and wide

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distribution of social media that allows organically combining various motivation for nonprofessional participation in scientific activities: from material or status-related (driven by hope to (re)enter research community) to purely recreational or even altruistic (driven by social responsibility). Quite as obvious is the fact that relevant digital platforms can be easily adapted to serve specific needs of the scholarly community as well: to ensure maximum transparency and objectivity of expert activities and rating of both research and individual researchers; to remotely train future research and technical personnel—with virtually unlimited reach, starting from secondary—if not primary—school; to expedite (but not simplify!) peer-review process as well as conferring academic degrees; to facilitate remote interaction of research teams and personnel optimization of research projects; to better implement functions of exchange, transfer, and protection of intellectual property, and so on. By the same token, it could be attuned to serve S&T agenda of the as society as a whole: to promote scientific knowledge, to facilitate access to education at all levels, and to generally human capital in the direction of the full-scale knowledge society. Developing this kind of digital infrastructure would imbue the digital economy with something more meaningful than to inflate prices while reselling others’ services. As for the risks of providing legal cover and thus perpetuating financial bubbles through replacing substantial innovations with the fake ones, to encourage the former, and demotivate the latter is in essence a function of government. A path an innovative economy will take depends on faithful execution of the function no more and no less than any other economic strategy. REFERENCES Abramovitz, M. (1979). Rapid growth potential and its realisation: The experience of capitalist countries in the post-war period. In E. Malinvaud (Ed.), Economic growth and resources (pp. 1–51). London, England: Macmillan. Bell, D. (1967). Notes on the post-industrial society. The Public Interest, 7. Bell, D. (1973). The coming of post-industrial society: A venture in social forecasting. New York, NY: Basic Books. Boulding, K. E. (1991). What is evolutionary economics? Journal of Evolutionary Economics, 1(1), 9–17. https://doi.org/10.1007/BF01202334 Brynjolfsson, E., Eggers, F., & Gannamaneni, A. (2018). Using massive online choice experiments to measure changes in well-being. Retrieved from https://www.nber. org/papers/w24514 Christensen, C. M., & Leslie, D. (1997). The innovator’s dilemma: When new technologies cause great firms to fail. Boston, MA: Harvard Business Review Press. Dalum, B., Johnson, B., & Lundvall, B.-Å. (1992). Public policy in the learning society. In B.-Å. Lundvall (Ed.), National systems of innovation: Towards a theory of innovation and interactive learning (pp. 293–314). London, England: Pinter.

660    A. M. KORNILOV Diesperova, N. A. (2017). “Pseudoinnovation” and “pseudoinvestments” in Russian economy. RUDN Journal of Economics, 25(1), 41–53. Friedman, M. A. (1970, September 13). A Friedman doctrine: The social responsibility of business is to increase its profits. The New York Times https://www. nytimes.com/1970/09/13/archives/a-friedman-doctrine-the-social-responsibility-of-business-is-to.html Gregersen, B., & Johnson, B. (1997). Learning economies, innovation systems and european integration. Regional Studies, 31(5), 479–490. Kuznets, S. (1971). Economic growth of nations: Total output and production structure. Boston, MA: Harvard University Press. Naysbit, J. (1988). Megatrends: Ten New Directions Transforming Our Lives. New York, NY: Grand Central. North, D. C. (1981). Structure and change in economic history. New York, NY: W. W. Norton & Company. Pasinetti, L. L. (1981). Structural change and economic growth: A theoretical essay on the dynamics of the wealth of nations. Cambridge, England: Cambridge University Press. Romer, P. M. (1990). Endogenous technological change. Journal of Political Economy, 98(5). https://www.jstor.org/stable/2937632 Schumpeter, J. A. (1934). The theory of economic development. Cambridge, MA: Harvard University Press. Solow, R. (1956). A contribution to the theory of economic growth. Quarterly Journal of Economics, 70(1), 65–94. https://doi.org/10.2307/1884513 Toffler, A. (1971). Future shock. New York, NY: Bantam Books. Toffler, A. (1990). Powershift: Knowledge, wealth, and the 21st century. New York, NY: Bantam Press. Umesao, T. (1969). The art of intellectual production. Tokyo, Japan: Iwanami Shoten. von Böhm-Bawerk, E. (1921). Positive Theorie des Kapitales. Jena, Gustav Fischer.

CHAPTER 75

EVALUATION OF THE ECONOMIC EFFICIENCY OF INVESTMENT PROJECTS WITH POTENTIALLY INFINITE LIFECYCLE Yaroslav S. Potashnik Minin Nizhny Novgorod State Pedagogical University Victor P. Kuznetsov Minin Nizhny Novgorod State Pedagogical University Alexander P. Garin Minin Nizhny Novgorod State Pedagogical University Elena V. Shpilevskaya The All-Russian State University of Justice Elena S. Gailomazova K. G. Razumovsky Moscow State University of Technologies and Management

Meta-Scientific Study of Artificial Intelligence, pages 661–668 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

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ABSTRACT Evaluation of economic efficiency is one of the key tasks that needs to be solved in a process of investment planning. Currently proposed numerous methods and indicators to identify the different economic characteristics of business investment. At the same time, in our opinion, some aspects of an assessment of the economic efficiency of investment projects need additional methodological study. In particular, tools for assessing the economic efficiency of investment projects with potentially endless life cycles need to be clarified and developed. This chapter is devoted to the solution of this problem. This chapter presents the genesis of approaches to assessing the economic efficiency of investment projects, as well as the main indicators characterizing the economic attractiveness of investments. The necessity of development of methodical tools used by enterprises in estimation of economic efficiency of investment projects is proved. An approach to assessing the economic efficiency of investment projects with potentially infinite life cycles is proposed, necessary formulas and explanations are presented. A methodical example of the use of the proposed approach is given. Scientific methods were used such as a system approach, logical analysis, synthesis, and a method of pairwise comparisons. This chapter is intended for researchers and specialists in the field of finance.

The most important task that needs to be solved in the process of investment planning is to assess the economic efficiency of developed projects. Developed and used in practice with a status of official “Guidelines for Evaluating the Effectiveness of Investment Projects” (Andrashina, 2014). However, some aspects of an assessment, in our view, remain insufficiently explored. In particular, proposed valuation methods are useful in cases where investment projects generate cash flows during a certain final period of implementation. If a project has potential to remain profitable for an indefinite, theoretically infinite period of time. This may be the case, for example, for projects involving the production of goods. The use of traditional valuation approaches becomes unsuitable, as they involve modeling of cash flows for each step of the project’s life cycle. In this regard, it can be stated that there is a need to develop an approach that allows assessing the economic efficiency of investment projects with a potentially infinite life cycle with a sufficient degree of accuracy, which this chapter is devoted to. METHODOLOGY In recent decades in our country, it is possible to observe the genesis of approaches to the interpretation of the essence of a category economic efficiency of the investment project and methods of their evaluation. An approach that prevailed until the mid-90s of the last century was based on consideration

Evaluation of the Economic Efficiency of Investment Projects    663

of the economic efficiency of a project as an integral characteristic of economic feasibility. It was decided to distinguish an absolute and comparative economic efficiency. The overall efficiency of a project was calculated as a ratio of the accounting profit to a value of an investment. Formulas (Gryaznova & Fedotova, 2009) were used to assess the overall effectiveness • in the relationship of projects implemented by existing enterprises:

Eod =

∆P (75.1) K

where ∆P = profit gain (cost reduction) caused by capital investments; K = volume of capital investments. • with regard to new enterprise projects:

Een =

P (75.2) K

where P is the planned profit of an enterprise. The value of Eod was compared with a standard of efficiency En, if Eod ≥ En, then s project under consideration was recognized as cost-effective. Comparative effectiveness was evaluated when it was necessary to select a project from alternatives. As a criterion, reduced costs were used—the sum of current cost and one-time capital investments reduced to the same dimension. Formulas (Van Horne & Wachowicz, 2016) were used to calculate given costs:

Pi = Ci + EKi minimum (75.3)



Pi = Ki +TCi minimum (75.4)

where Ci is the current cost of output for each project; Ki is the capital investment for each project; E is the standard of efficiency of capital investments by setting at the level of a minimum allowable profit in relation to the investments at which the investment may be considered appropriate; T is a standard payback period investments in fixed assets due to profit—the reciprocal of E. The main drawbacks of this approach, in our view, are the use as an effect from the implementation of investments of accounting profit, which depending on the characteristics of the accounting at the enterprise can be arbitrarily changed in a significant amount. In modern conditions, the economic efficiency of an investment project is considered, mainly, as a category reflecting compliance of a project

664    Y. S. POTASHNIK et al. Main indicators of economic efficiency of investment projects Effect: net income, net discounted income, accrued net income, accrued net discounted income Yields: index of return on expenses, index of return on investments, discount index of return on expenses, discount index of return on investments, internal rate of return, modified rate of return. Payback: simple payback period, payback period taking into account discounting Risk: variance, standard deviation, coefficient of variation, cost of capital

Figure 75.1  Main indicators characterizing the economic efficiency of investment projects.

to goals and interests of participants (Dmitriev, 1991). A broad system of indicators reflecting the effect, profitability, payback, feasibility, and risk of investment projects is used to assess economic efficiency (see Figure 75.1). A more complete account of cash flows of investment projects with a potentially infinite life cycle, it is possible, in our view, by using the underlying principle of an income approach to definition of business value (Garina, Klyueva, & Sevryukova, 2015; Van Horne & Wachowicz, 2016). In accordance with it, the life cycle, in our case of investment projects, is divided into two periods, distinguished by stable growth rates of net cash flow: predicted and extended. The forecast period is characterized by the growth rate of net cash flow of an investment project. The extended period of an investment project, which is characterized by a stable long-term growth rate of net cash flow (positive, zero, or negative). We suggest estimating an economic efficiency of investment projects based on the analysis of values of an accumulated discounted net cash flow of a project and calculated using the formula: n



Vpr NCF + (75.5) m (1 + E ) (1 + E )n m =0

ADNCFpr = ∑

where ADNCFpr is the accumulated discounted net cash flow for the entire life of a project with an infinite life cycle; n is the project step, at the end of which the period is expected to start, is (extended period) with a steady rate of change in net cash flow; NCF is the net cash flow of the project in step m. E is a discount rate, expressed in fractions of a unit, equal to a minimum rate of return on invested capital required to approve an investment project. A discount rate is determined taking into account the cost of capital,

Evaluation of the Economic Efficiency of Investment Projects    665

depending on its sources, structure, available alternatives, and riskiness of a project (Dmitriev, 1991; Markova & Narkoziev, 2018; Chelnokova & Nabiyev, 2015). Vpr is the accumulated net cash flow of the investment project in an extended period, given (discounted) to the last step of a forecast period: ∞



NCFn + m (75.6) (1 + E )m m =1

Vpr = ∑

Since it is assumed that in extended period growth rates of the net cash flow of an investment project will be stable, then the Gordon model can be used to determine the Vpr (Andrashina, 2014):

NCFn + 1 (75.7) (r − g )

Vpr =

where g is the projected growth rate of the project’s net cash flow in an extended period. If the discounted net cash flow of a project is nonnegative, the project may be considered as cost-effective. A project is more effective when ADNCFpr is high. If several options are considered for the possible rates of change in the net cash flow of a project in an extended period (Potashnik, Garina, Romanovskaya, Garin, & Tsymbalov, 2018), can be presented as follows: n



Vpr NCFm + (75.8) m (1 + E ) (1 + E )n m =0

ADNCFpr = ∑

where Vpr is an average expected accumulated net cash flow of an investment project in extended periods, reduced to the last step of a forecast period, calculated by the formula: I



Vpr = ∑Vpri ⋅ Pi (75.9) i =1

where i is the number of options considered; I is the amount of options considered; Vpri is an average expected accumulated net cash flow of the investment project in the extended period, reduced to the day step of the forecast period, variant i, is calculated in general by the formula (Lapaev & Potashnik, 2014); Pi is the probability of variant i. It is possible Vpr that an option of terminating a project at a certain step of the extended period may be considered among others. This Vpri variant

666    Y. S. POTASHNIK et al.

of continuation of an investment project is not determined on a basis of the Gordon model, but on a basis of modeling cash flows of each step from the beginning of an extended period to termination of a project, bringing the corresponding net cash flow to the end of a forecast period and summing discounted values that can be represented in a form of the formula: k



NCFn + m (75.10) (1 + E )m m =1

Vpri = ∑

where k is the step of termination of an investment project. In addition to the ADNCFpr indicator to expand the range of economic evaluation of projects, a discounted ROI index (ROIi ) can be calculated according to the formula:

ROI i =

ADNCFpr + DC (75.11) DC

where DC is the value of investment, reduced to the initial step of the project implementation. If ROIi value is high, the efficiency of a project is going to be higher. If the formula (Kuznetsov, Romanovskaya, Egorova, Andryashina, & Kozlova, 2018) is used, it is assumed that investments in an extended period necessary to maintain a stable positive growth rate are made at the expense of a generated income. If calculations of efficiency indicators proposed above lead to contradictory results, in our opinion, preference should be given to alternatives with the highest ADNCFpr. RESULTS Example of calculation of ADNCFpr indicator for two mutually exclusive projects with potentially infinite life cycle are seen in Table 75.1. Both projects can be considered effective, while Project B is more effective. Methodical example of calculation Vpr for Projects A and B is presented in Table 75.2. A methodical example of calculation of the discounted ROI index for Projects A and B is presented in Table 75.3. Thus, Project A is more attractive. Above approach was tested in PJSC “PBC,” Nizhny Novgorod. The approbation showed its practical significance. Results obtained with this

Evaluation of the Economic Efficiency of Investment Projects    667 TABLE 75.1  Methodical Example of Calculation of ADNCFpr Project A

Project B

Project Step (in years)

Project Step (in years)

NCFm

ADNCFm

1

–230

NCFm

–230

1

–280

2

100

–146.7

2

110

–188.37 –84.27

ADNCFm –280

3

140

–49.54

3

150

4

168

47.73

4

180

19.95

5

190

139.31

5

198

115.38

6

200

219.71

6

211

200.21

7

208

289.39

7

222

274.57

200 = 1111.11 0.2 − 0.02

599.36

227 = 1261.11 0.2 − 0.02

626.39

Vpr ADNCFpr

Vpr ADNCFpr

599.36

626.39

TABLE 75.2  Calculation Vpr Project A i

gi

Pi

Vpri

Vpri × Pi

Vpr

1

3

0.25

1172.68

293.17

1104.25

2

2

0.60

1111.11

666.67

3

–1

0.15

962.73

144.41

Project B i

gi

Pi

Vpri

Vpri × Pi

Vpr

1

3

0.25

1335.29

333.82

1252.63

2

2

0.60

1261.11

756.67

3

–1

0.15

1080.95

162.14

TABLE 75.3  Calculation of ROIi Project A

Project B

Indicator

Calculation

Value

ROIi

599.36 + 230 230

3.6

Indicator

Calculation

Value

ROIi

626.39 + 280 280

3.2

approach allowed management of an enterprise to obtain additional information on the economic efficiency of a number of developed projects, increasing the validity of assessment and implementation.

668    Y. S. POTASHNIK et al.

CONCLUSIONS An approach that allows estimating the economic efficiency of investment projects with potentially infinite life cycles with a sufficient degree of reliability is proposed. Tests showed that the proposed approach had practical significance. Its application in relation to investment projects with an indefinite life cycle makes it possible to increase the reliability of an assessment of their economic efficiency and validity of selection for implementation. REFERENCES Andrashina, N. S. (2014). Analysis of best practices of development of domestic machine-building enterprises. Bulletin of Saratov State Socio-Economic University. 1(50), 24–27. Chelnokova, E. A., & Nabiyev, R. B. (2015). Tutor activity of the teacher to ensure successful adaptation of university students. Bulletin of Mininsky University, 3(11), 23. https://vestnik.mininuniver.ru/jour/searc h/search Dmitriev, M. N. (1991). Efficiency of capital investments: Textbook. Nizhny Novgorod, Russia: MIPK NISI. Garina, E. P., Klyueva, Yu. S., & Sevryukova A. A. (2015). Approaches to formation of competitive strategies of organizations by branches. Kazan Science, 10, 120–122. Gryaznova, A. G., & Fedotova, M. A. (2009). Business evaluation. Moscow, Russia: Finance and Statistics. Kuznetsov, V. P., Romanovskaya, E. V., Egorova, A. O., Andryashina, N. S., & Kozlova, E. P. (2018). Approaches to developing a new product in the car building industry. Advances in Intelligent Systems and Computing, 622, 494–501. https:// doi.org/10.1007/978-3-319-75383-6_63 Lapaev, D. N., & Potashnik, Y. S. (2014). Determination of the cost of capital investment projects in industry. Audit and financial analysis, 5, 199–202. Markova, S. M., & Narkoziev, A. K. (2018). Production training as a component of professional training of future workers. Bulletin of Mininsky University, 6(1). https://doi.org/10.26795/2307-1281-2018-6-1-4 Potashnik, Y. S., Garina, E. P., Romanovskaya, E. V., Garin, A. P., & Tsymbalov, S. D. (2018). Determining the value of own investment capital of industrial enterprises. Advances in Intelligent Systems and Computing, 622, 170–178. https://doi. org/10.1007/978-3-319-75383-6_22 Van Horne, D., & Wachowicz, J. M., Jr. (2016). Fundamentals of financial management. Translated from English. Moscow, Russia: I. D. Williams.

CHAPTER 76

THE ROLE OF FOREIGN ECONOMIC RELATIONS IN DEVELOPMENT OF DIGITAL TECHNOLOGIES IN ONCOLOGICAL SERVICE The Experience of Modern Russia Yuri V. Przhedetsky Rostov Cancer Research Institute of the Ministry of Health Natalia V. Przhedetskaya Rostov State University of Economics Ksenia V. Borzenko Rostov State University of Economics

Meta-Scientific Study of Artificial Intelligence, pages 669–675 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

669

670    Y. V. PRZHEDETSKY, N. V. PRZHEDETSKAYA, and K. V. BORZENKO

ABSTRACT The purpose of the work is to determine the role of foreign economic relations in development of digital technologies in the oncological service by the example of experience of modern Russia and to develop recommendations for highly effective usage of foreign economic relations for quick and successful digital modernization of activities of the Russian oncological services. The methodology of the works includes comparative analysis, which is used for comparing the traditional and digital technologies in the oncological service; modeling of socioeconomic processes and systems and formalization, which are used for showing the modern and prospective future Russian experience in development of digital technologies in the oncological service. The authors substantiate the necessity for using foreign economic relations for development of digital technologies in the oncological service in modern Russia. It is determined that limitation of foreign economic relations in the interests of import substitution leads to deficit of infrastructural provision and absence of natural market stimuli for digital modernization of the oncological services.

Oncological diseases are one of the most widespread and dangerous diseases in the world, and the number of people of oncological diseases grows annually. Thus, active R&D are conducted for improvement of the existing practice of functioning of the oncological services. Most of the created innovations envisage using the digital technologies in the activities of the oncological service. The priority for the national economy and nonprofit direction of the activities of the oncological services predetermine large state regulation of their activities which often supposes state property and management of these services. That’s why digital modernization of activities of the oncological services is organized and controlled by the state. In Russia, this tendency is manifested in adoption of the Decree of the Government of the Russian Federation Dated May 5, 2018, No. 555, “Regarding the state information system in the sphere of healthcare,” which envisages creation of the digital contour of healthcare, which is confirmed by the materials of the Ministry of Healthcare of the Russian Federation (2019). Though this government program of digital modernization of healthcare in Russia is oriented at the sphere on the whole, attention is paid to implementation of digital technologies into the activities of the oncological services for increasing its effectiveness. It is set for the period 2019–2024, with RUB 5 billion allocated for its implementation. The working hypothesis of the research is that foreign economic relations are very important for the development of digital technologies in the oncological service. The purpose of the chapter is to determine the role of foreign economic relations in development of digital technologies in the oncological service by the example of experience of modern Russia and to

Role of Foreign Economic Relations in Development of Digital Technologies    671

develop recommendations for highly effective usage of foreign economic relations for quick and successful digital modernization of activities of the Russian oncological services. METHODOLOGY The performed overview of the existing scientific literature on the selected topic showed that the importance of application of digital technologies in the activities of the oncological service is emphasized in most modern specialized studies, which include Adam, de Bruin, Burton, Giatsi Clausen, and Murchie (2018); Boyes, Turon, Hall, Proietto, and Sanson-Fisher (2018); Harris, Cheevers, and Armes (2018); Popkova, Ragulina, and Bogoviz (2019); Przhedetsky, Przhedetskaya, Przhedetskaya, and Bondarenko (2018); Przhedetsky, Przhedetskaya, Przhedetskaya, and Borzenko (2019); Varma and Sawant (2018); and Zhang, Liu, Yan, Fang, and Chen (2019). According to the given publications, we performed a comparative analysis of the traditional and digital technologies in the oncological service (see Table 76.1). As is seen from Table 76.1, digital technologies allow increasing the effectiveness of the oncological service’s activities. They make deep preventive examinations, which stimulate the prevention of oncological diseases, accessible. Digital technologies allow achieving high precision of diagnostics TABLE 76.1  Comparative Analysis of the Traditional and Digital Technologies in the Oncological Service Criterion of Comparison

Traditional Technologies

Digital Technologies

Prevention of oncological diseases

surface prevention examinations

deep preventive examinations

Diagnostics of oncological diseases

low precision, high probability of mistakes

high precision, low probability of errors

Treatment of oncological diseases

domination of surgical method

foundation on innovational conservative methods

Fixing an appointment with the oncological service

in person, via telephone

in the personal electronic account via the Internet

Receipt of the results of functional diagnostics and laboratory tests

in person, in paper form during appointment with the specialist

remotely in the digital form in the electronic personal account via the Internet

Receipt of sick leave certificates and prescriptions for medicine for treatment of oncological diseases

paper sick leave certificates, received and used during personal presence of the patients

digital sick leave certificates that are received and used remotely

Social adaptation of patients with oncological diseases

mass marketing, individual psychological support

individual marketing in social networks

672    Y. V. PRZHEDETSKY, N. V. PRZHEDETSKAYA, and K. V. BORZENKO

limitation of foreign economic relations, import substitution

Infrastructural provision: • deficit of digital equipment • deficit of pharmaceutical drugs, medicine, and materials • deficit of digital personnel • deficit of digital technologies that are ready for practical application

State regulation: • low requirements on development of digital technologies • insufficient support for modern modernization

Oncological service: • foundation on traditional technologies • slow rate of development of digital technologies

Consumption: • low volume of effective demand for innovational services that are provided on the paid basis • necessity for provision of services by the terms of mandatory medical insurance

Result: • low share of diagnosing oncological diseases at early stages • high death rate from oncological diseases • low quality of life of patients with oncological diseases

Figure 76.1  The modern experience of Russia on Development of Digital Technologies in the Oncological Service.

of oncological diseases and reducing the probability of errors and potential risks to patients’ health during examination (negative consequences of medical interference). The determined experience of modern Russia in development of digital technologies in the oncological service through the prism of foreign economic relations is shown in Figure 76.1. As is seen from Figure 76.1, state regulation of activities of the oncological services envisages low requirements on application of digital technologies (absence of clear norms and standards of application of these technologies) and insufficient support for their digital modernization (insufficient legal provision, deficit of state financing, and absence of tax and other preferences). This hinders the formation of the necessary infrastructural provision for digital modernization of activities of the oncological services in Russia and leads to the following: • deficit of digital equipment due to low production capacities of domestic manufacturers;

Role of Foreign Economic Relations in Development of Digital Technologies    673

• deficit of pharmaceutical drugs, materials, and medicine due to absence or insufficient quality of domestic analogs of imported pharmaceutical drugs and medicines; • deficit of digital personnel due to closeness of the domestic system of education; and • deficit of digital technologies, which are ready for application, due to deficit of financing of R&D and high complexity of independence implementation of the whole innovative process. RESULTS The following future Russian experience in development of digital technologies in the oncological service on the basis of foreign economic relations is offered (see Figure 76.2). As is seen from Figure 76.2, it is recommended to correct state regulation for provision of the institutional provision of development of digital

stimulating the top-priority foreign economic relations

State management: • institutional provision of development of digital technologies • support for digital modernization on the basis of public–private partnership

Infrastructural provision: • leading digital equipment, medicines provided by transnational clusters • digital personnel and leading digital technologies which are ready for practical application due to exchange of experience

Oncological service: • quick rate of development of digital technologies and foundation on them during provision of services

Consumption: • domestic demand for innovational services that are provided on the paid basis • foreign demand for innovtional services that are provided on the basis of medical tourism • provision of services by the conditions of medical insurance

Result: • increase of the share of detection of oncological diseases at early stages • reduction of death rate from oncological diseases • increase of quality of life of patients with oncological diseases

Figure 76.2  The perspective future Russian experience in Development of Digital Technologies in the Oncological Service on the basis of foreign economic relations.

674    Y. V. PRZHEDETSKY, N. V. PRZHEDETSKAYA, and K. V. BORZENKO

technologies, support for digital modernization on the basis of public–private partnership, and stimulation of top-priority foreign economic relations: • transnational clusters of manufacturers of digital equipment, medicines, and means of diagnosis and treatment of oncological diseases; • international exchange of experience; • direct foreign investments into development of digital technologies and their implementation into activities of the oncological services; and • medical tourism, aimed at diagnosis and treatment of oncological diseases. Implementation of the offered measures of state regulation will allow overcoming the deficit of infrastructural provision and forming high demand for innovative services that are provided on the paid basis. This will ensure a quick rate of development of digital technologies, the oncological services, and the foundation on them during provision of services for treatment of oncological diseases. CONCLUSION Thus, the offered hypothesis was confirmed; it was substantiated that it is necessary to use foreign economic relations for development of digital technologies in the oncological service in modern Russia. Limitation of foreign economic relations in the interests of import substitution leads to a deficit of infrastructural provision and absence of natural market stimuli for digital modernization of the oncological services. The perspective directions of usage of foreign economic relations for development of digital technologies in the oncological service in modern Russia are transnational clusters, international exchange of experience, attraction of direct foreign investments, and medical tourism. Together with the state financial, institutional, and infrastructural support, foreign economic relations will allow implementing quick and successful digital modernization of the oncological services in Russia and thus increasing the effectiveness of prevention, detection, and treatment of oncological diseases. REFERENCES Adam, R., de Bruin, M., Burton, C. D., Giatsi Clausen, M., & Murchie, P. (2018). What are the current challenges of managing cancer pain and could digital technologies help? BMJ Supportive & Palliative Care, 8(2), 204–212.

Role of Foreign Economic Relations in Development of Digital Technologies    675 Boyes, A., Turon, H., Hall, A., Proietto, A., & Sanson-Fisher, R. (2018). Preferences for models of peer support in the digital era: A cross-sectional survey of people with cancer. Psycho-Oncology, 27(9), 2148–2154. Harris, J., Cheevers, K., & Armes, J. (2018). The emerging role of digital health in monitoring and supporting people living with cancer and the consequences of its treatments. Current Opinion in Supportive and Palliative Care, 12(3), 268–275. Ministry of Healthcare of the Russian Federation. (2019). Digital contour of healthcare according to the Decree of the Government of the RF Dated May 5, 2018, No. 555, “Regarding the state information system in the sphere of healthcare.” https://www.rosminzdrav.ru/news/2018/05/08/7856-podpisano-postanovlenie-pravitelstva-rossiyskoy-federatsii-o-edinoy-gosudarstvennoyinformatsionnoy-sisteme-v-sfere-zdravoohraneniya Popkova, E. G., Ragulina, Y. V., & Bogoviz, A. V. (2019). Fundamental differences of transition to industry 4.0 from previous industrial revolutions. Studies in Systems, Decision and Control, 169, 21–29. Przhedetsky, Y. V., Przhedetskaya, N. V., Przhedetskaya, V. Y., Bondarenko, V. A., & Borzenko, K. V. (2018). The role of social-ethical marketing and information and communication technologies in response to challenges of oncology. European Research Studies Journal, 21(Special Issue 1), 377–386. Przhedetsky, Y. V., Przhedetskaya, N. V., Przhedetskaya, V. Y.,& Borzenko, K. V. (2019). Social networks as a tool of early detection of cancer. Advances in Intelligent Systems and Computing, 726, 887–894. Varma, C., & Sawant, O. (2018). An alternative approach to detect breast cancer using digital image processing techniques. In Proceedings of the 2018 IEEE International Conference on Communication and Signal Processing, 8524576, 134–137. Zhang, H., Liu, R., Yan, C., Fang, W., & Chen, Z. (2019). Advantage of next-generation sequencing in dynamic monitoring of circulating tumor DNA over droplet digital PCR in cetuximab treated colorectal cancer patients. Translational Oncology, 12(3), 426–431.

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ABOUT THE EDITORS

Elena G. Popkova, Doctor of Science (economics), is the founder and president of the Institute of Scientific Communications (Russia) and leading researcher of the Center for Applied Research and the chair for Economic Policy and Public–Private Partnership of Moscow State Institute of International Relations (MGIMO; Moscow, Russia). Her scientific interests include the theory of economic growth, sustainable development, globalization, humanization of economic growth, emerging markets, social entrepreneurship, and the digital economy and Industry 4.0. Elena G. Popkova organizes all-Russian and international scientific and practical conferences and is the editor and author of collective monographs, and serves as a guest editor of international scientific journals. She has published more than 300 works in Russian and foreign peer-reviewed scientific journals and books. Victoria N. Ostrovskaya, Doctor of Science (economics), professor, head of the Center for Marketing Initiatives (Stavropol, Russia). Her research interests include marketing support for foreign economic activity, digital Industry 4.0, digitalization of marketing, benchmarking in retail, structuring of the quality function, social marketing, territory marketing, economic development of emerging markets, and project management. Victoria N. Ostrovskaya gives lectures in Southern Federal University (Rostov-on-Don, Russia), organizes scientific and practical conferences at the regional, national, and international levels; acts as a moderator and speaker of business and training events (forums, seminars, and work meetings) for business entities; and conducts active research work. She is the author of more than Meta-Scientific Study of Artificial Intelligence, pages 677–678 Copyright © 2021 by Information Age Publishing All rights of reproduction in any form reserved.

677

678   About the Editors

170 scientific publications, including more than 130 scientific papers, and also carries out activities to promote the results of scientific research for their practical use.