AI Chatbots: The Good, The Bad, and The Ugly (Synthesis Lectures on Engineering, Science, and Technology) [1st ed. 2024] 3031455088, 9783031455087

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Synthesis Lectures on Engineering, Science, and Technology

James Crowder

AI Chatbots The Good, The Bad, and The Ugly With Contributions from John Carbone

Synthesis Lectures on Engineering, Science, and Technology

The focus of this series is general topics, and applications about, and for, engineers and scientists on a wide array of applications, methods and advances. Most titles cover subjects such as professional development, education, and study skills, as well as basic introductory undergraduate material and other topics appropriate for a broader and less technical audience.

James Crowder

AI Chatbots The Good, The Bad, and The Ugly

James Crowder Department of Engineering CAES Advanced Program Development Colorado Springs, CO, USA With Contribution by John Carbone Garland, TX, USA

ISSN 2690-0300 ISSN 2690-0327 (electronic) Synthesis Lectures on Engineering, Science, and Technology ISBN 978-3-031-45508-7 ISBN 978-3-031-45509-4 (eBook) https://doi.org/10.1007/978-3-031-45509-4 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Paper in this product is recyclable.

Foreword

The mad scientist, that is, Jim Crowder, who constructed a Van De Graaff generator as a young lad scraping radium off of glow-in-the-dark watches, and developed dual hemisphere brained analog self-learning robotic circuits, has been researching and implementing advanced systems for years. Together, Dr. Crowder and I have now been engineering complex defense solutions for many decades and researching what most people call Artificial Intelligence for longer than we can remember and count. Jim and I met while modernizing a space program and soon thereafter began to contemplate universal issues and attempt to solve the complexities of spacetime and world hunger, among others. Jim had started his AI journey a number of years before me, and his mathematical expertise is second to none. Many major space and radar programs, as well as many lives, have been saved with the benefit of Jim’s complex vector and trajectory calculations. Importantly from an AI perspective, as it relates to this book, Jim and I arrived at similar AI concepts and theories, although our pedigree derived from a significant, varied, and sorted past. Hence, we combined our Biological, Mechanical Engineering, Electrical Engineering, Cyber Security, Physics, Advanced Math, and Linguistic/Ontological learning and coalesced those thoughts into Artificially Cognitive Systems while also blazing a path to a new field of study known as Artificial Psychology using robust transdisciplinary engineering approaches. Our multidisciplinary knowledge base and a deep inspiration derived from the well-known industry overloading of the term Artificial Intelligence provided us a significantly tangential and computationally orthogonal AI path, along with a great feeling of need to provide human value with deeper implications. Therefore, much of our motivation and exasperated inspiration for this book derives from witnessing the continuous mis- and disinformation sweeping across the vast information propagation media machine. Thankfully, the large number of resulting misguided fears are at least being mitigated to some degree by an unfortunate small amount of adult supervision, and since, even the many self-prescribed experts are adding to the problem, a significant increase in corrective actions is required by all of us in this industry and academia to resolve these disparities. Therefore, we have poured heart and soul into this book to hopefully provide a path to v

vi

Foreword

increased positive dialog built on a foundation of truth and a positive outlook for the future instead of one rooted in fear for solely monetary gain and control. Dr. John Carbone Senior Technical Director Forcepoint Austin, TX, USA

Preface

There has been much talk over the last few years about the perils of the use of artificial intelligence1 in virtually everything we touch. From our phones to our cars, and everything in between, artificial intelligence is an integral part of our existence. Many prominent people, like Elon Musk and Stephen Hawking, have warned about the potential for machines to take over and cause havoc in the lives and very existence of humans. Hollywood has made untold billions of dollars painting doom-and-gloom scenarios about artificial intelligence and robots within our society today and in the future. But what is the true reality? We continually push to create increasingly intelligent online entities (Chatbots) that seemingly learn, think, and reason like humans. Most attempts fail miserably. Given the recent “issues” with AI Chatbots like ChatGPT® , we enter a brave new world of interactions with AI-like entities. However, fundamentally these AI Chatbots have no cognitive functions and are not architected to understand human emotions. Therefore, we will attempt to dispel some of the misplaced fear, and mis- and disinformation while discussing some of the issues associated with the proliferation of online Chatbots and their effects on society and human relations, both good and bad. In writing this book, it became definitively clear that Chatbots are not only evolving significantly faster, but also with rapidly advancing linguistic depth and with added effectivity features for providing more usability than just simple knowledge query and response. We can only scratch the surface in the discussions included in this short book, but hopefully, it’s a jumping-off point for continued dialog and interaction between the public, industry, and academics creating these Chatbots and their infrastructure. Hopefully, together we can achieve a joint objective to improve not only the content, but also the ethical understanding and use of the content. Colorado Springs, USA

James Crowder

1 Most of what is called artificial intelligence is not actual artificial intelligence, but the term is

overused so often it has become a generic term for systems that emulate human characteristics or perform previous human-centered tasks. vii

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Preface

Acknowledgements I would like to acknowledge and thank Dr. John Carbone, colleague, and dear friend for authoring sections of the book. John and I have co-authored many of the books and papers I (we) have had the privilege to publish. Dr. Carbone has had much experience with artificial intelligence. Those sections provided by Dr. Carbone will be indicated. I would also like to acknowledge and thank my daughter, Dr. Shelli Friess, a Research Fellow at Walden University, for her insights into the human psyche and for her continued work on the role of artificial psychology in artificial intelligence. She has also been a co-author for many of the books and journal papers I (we) have published.

Contents

1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Human Fascination with Artificial Entities . . . . . . . . . . . . . . . . . . . . . . . . 1.2 ELIZA: The First Chatbot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Internet Protocols and the World Wide Web . . . . . . . . . . . . . . . . . . . . . . . 1.4 Other Early Chatbots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.1 Parry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.2 A.L.I.C.E. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.3 Digital Assistant Chatbots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5 Layout of the Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.1 Section I—The Rise of the Chatbot . . . . . . . . . . . . . . . . . . . . . . . 1.5.2 Section II—Chatbots: The Good . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.3 Section III—Chatbots: The Bad . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.4 Section IV—Chatbots: The Ugly . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.5 Section V—The Future of Chatbots . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Part I

1 3 6 7 7 8 8 8 8 9 9 10 10 11 11

The Rise of the Chatbot

2

Background: What Is a Chatbot? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Different Notions of a Chatbot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

15 18 19

3

Categories of Chatbots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Menu-Based Chatbots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Rule-Based Chatbots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Machine Learning-Based Chatbots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Voice-Based Chatbots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 Cognition-Based Chatbots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6 Future Trends in Chatbots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

21 22 23 25 26 30 31 31

ix

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Contents

4

Chatbots and the Need for Connectivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Human Need and Acceptance of Chatbots . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 The Psychology of Human-Chatbot Communication . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

33 35 35 37

5

Chatbots: Do They Fill a Need in Society? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 The Use of Chatbots in Businesses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 The Use of Chatbots in Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

39 40 41 42

Part II

Chatbots: The Good

6

Chatbots Like Siri: Your Digital Assistant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Chatbots and Linguistic Accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1.1 Linguistic Accuracy and NLP/NLU . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

45 46 47 49

7

Chatbots for Casual Interactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1 The Psychology of Chatbot Relationships . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 The Need for Artificial Emotions in Personal Chatbots . . . . . . . . . . . . . . 7.2.1 Emotional Intelligence and the Artificial Limbic System . . . . . 7.2.2 An Artificial Emotional Intelligence Test for Chatbots . . . . . . . 7.2.3 Chatbot Sample Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3 Available Chatbots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

51 52 53 55 56 57 67 68

8

Chatbots That Analyze Data and Provide Valuable Information . . . . . . . . . 8.1 Standard Organizational Uses of Chatbots . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 Uses of Chatbots in the Media and Entertainment Industries . . . . . . . . . 8.2.1 Product Branding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.2 Product Discovery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3 Industry Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

71 72 72 73 74 74 75

Part III 9

Chatbots: The Bad

Microsoft’s 2016 Chatbot: What Went Wrong? . . . . . . . . . . . . . . . . . . . . . . . . 9.1 Chatbot Tay: What Went Wrong? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2 Implicit Learning in Artificial Intelligent Systems . . . . . . . . . . . . . . . . . . 9.2.1 Implicit Learning Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2.2 Implicit Learning in Artificial Intelligent Chatbots . . . . . . . . . . 9.2.3 Measuring Implicit Learning Within a Chatbot System . . . . . . 9.2.4 Measuring Implicit Learning Human-Chatbot Interfaces . . . . .

79 80 80 81 82 85 87

Contents

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9.3 Implications of Tay and Other Issues for the Future of Chatbots . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

88 89

10 Examples of Issues with Chatbots Over the Last Few Years . . . . . . . . . . . . . 10.1 Sophisticated Chatbots, a Hacker’s Dream . . . . . . . . . . . . . . . . . . . . . . . . . 10.2 Essay Generating Chatbots: A University Nightmare . . . . . . . . . . . . . . . 10.3 Accuracy and Reliability Issues with Chatbots . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

91 92 95 95 96

11 When is Human-AI Interaction Appropriate and When is It Not Appropriate? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 Chatbot Entities for Foundational Counselor Training Sessions . . . . . . . 11.1.1 Counselor Training Considerations . . . . . . . . . . . . . . . . . . . . . . . . 11.1.2 A Chatbot Version of a Human Prefrontal Cortex . . . . . . . . . . . 11.1.3 An Artificial Prefrontal Cortex Architecture for CIPTS . . . . . . 11.1.4 Potential Counselor Training Using the CIPTS . . . . . . . . . . . . . 11.1.5 Chatbot Avatar Profile Training Using the CIPTS . . . . . . . . . . . 11.1.6 Testing Counselor Responses . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1.7 CIPTS Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Chatbots and the Arts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2.1 Chatbot Art/Image Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2.2 Chatbot Music Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2.3 Chatbot Poetry Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.3 The Ugly Side of Chatbots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

97 98 99 100 101 101 104 105 107 107 107 108 108 108 109

Part IV

Chatbots: The Ugly

12 Inherent Bias in Chatbots: Is It Possible to Create and AI Entity Without Any Bias? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.1 Using Artificial Intelligence to Detect Artificial Intelligence . . . . . . . . . 12.2 Using Artificial Intelligence for Facial Identification . . . . . . . . . . . . . . . . 12.3 Unintended Bias in Chatbot Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.4 Hidden Bias in Chatbots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.5 Chatbots and Bad Actors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 What Happens When a Chatbot Gives Detrimental Advice? Who’s Responsible? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.1 Chatbots, Free Speech, and the 1st Amendment . . . . . . . . . . . . . . . . . . . . 13.2 Chatbot Liabilities, Cyber Insurance, and Measuring Trust: Today and Tomorrow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.2.1 Mathematical Modeling of Information Relationships . . . . . . .

113 114 115 115 117 119 119 121 123 124 126

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Contents

13.3 Chatbots and Emergent Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.4 Can We Rely on Companies that Create Chatbots to Act Responsibly? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.4.1 China’s Approach to AI/Chatbot Governance . . . . . . . . . . . . . . . 13.4.2 USA’s Approach to AI/Chatbot Governance . . . . . . . . . . . . . . . . 13.4.3 Europe’s Approach to AI/Chatbot Governance . . . . . . . . . . . . . 13.4.4 Department of Defense’s Approach to AI/Chatbot Governance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.5 Why Regulations Are Doomed to Fail . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Part V

128 129 129 130 130 130 131 132

The Future of Chatbots

14 What Does the Future of Chatbots Look Like? . . . . . . . . . . . . . . . . . . . . . . . . . 14.1 The Rise and Fall of Human and Chatbot Creativity . . . . . . . . . . . . . . . . 14.1.1 The Use of Chatbots in Social Activism . . . . . . . . . . . . . . . . . . . 14.1.2 The Use of Chatbots in Research . . . . . . . . . . . . . . . . . . . . . . . . . 14.2 Measuring Trust and Human-Chatbot Communication . . . . . . . . . . . . . . 14.3 Calculating Trust: Mathematical Modeling of Relationships . . . . . . . . . 14.4 The Toulmin Argument Structure: The Art of Developing Factual Trust . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.5 Steps for Building Qualitative Trust Within AI Systems/Chatbots . . . . 14.6 Steps for Building Ethical Behavior Within AI Systems/Chatbots . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

137 138 139 139 140 141

15 Can Future Chatbots Show Love and Compassion, and Should They? . . . . 15.1 The Rise of Artificial Emotions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.2 The Basics of Artificial Emotions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.3 The Artificial Prefrontal Cortex for Chatbot and Robot Systems . . . . . . 15.3.1 What Does Artificial Consciousness Mean? . . . . . . . . . . . . . . . . 15.3.2 The Artificial Prefrontal Cortex . . . . . . . . . . . . . . . . . . . . . . . . . . 15.3.3 The Artificial Prefrontal Cortex and Cognitive Control . . . . . . 15.3.4 The Artificial Prefrontal Cortex Framework . . . . . . . . . . . . . . . . 15.3.5 The Artificial Prefrontal Cortex Architecture . . . . . . . . . . . . . . . 15.3.6 The Artificial Prefrontal Cortex Model . . . . . . . . . . . . . . . . . . . . 15.4 Putting It All Together . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

149 150 151 152 152 153 153 154 155 157 158 160

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

163

141 142 144 146

1

Introduction

Abstract

The world is at a crossroads on its use of Artificial Intelligence (or what passes for Artificial Intelligence). The use of Chatbots in our everyday lives is exponentially increasing with every passing year with widely varied results. What are the overall effects (both good and bad) of turning over more and more of our work to Chatbots acting as our “digital assistants?” Here we will explore these questions and many more as we delve into the human experience and our need to communicate with truly impartial, emotion challenged, digital entities (Chatbots). There are no easy answers to the possible benefits and dangers of the proliferation of Chatbots, but here we present information to get the dialog going. Keywords

Chatbot . Artificial intelligence . Machine learning Software . Artificial intelligent entities . ChatGPT

.

Natural language processing

.

Acronyms

A.L.I.C.E AI AIE AIML app ARPA

Artificial Linguistic Internet Computer Entity Artificial Intelligence Artificial Intelligence Entity Artificial Intelligence Markup Language Application Advanced Research Projects Agency

Some parts of this chapter were co-authored by Dr. John Carbone.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 J. Crowder, AI Chatbots, Synthesis Lectures on Engineering, Science, and Technology, https://doi.org/10.1007/978-3-031-45509-4_1

1

2

1 Introduction

BCE CE MIT ML NLP RUR TCP/IP UCLA US

Before Common Era Common Era Massachusetts Institute of Technology Machine Learning Natural Language Processing Rossum’s Universal Robots Transmission Control Protocol and Internet Protocol University of California Los Angeles United States

Artificial intelligent systems are becoming commonplace in everyday life now. Daily, people must interface with artificially intelligent systems, textually, verbally, and visually as an increasing number of systems require human/system interactions. We’ve probably all experienced issues between people over seemingly simple texts that are somehow easily misinterpreted because of all the noticeably absent non-verbal context (emotions). There are many online avatars or “Chatbots” available now. This includes online Chatbots like Replika® , ChatGPT® , and others. Some double as your online girlfriend/boyfriend or even writing your essays and software code for you, there is no end of online avatar capabilities. Many claim that their software is based on artificial intelligence. Most of these avatars are script-driven that utilize word/phrase searches to determine scripted responses and inquiries (questions) they pose back to the human. Attempts to create fully learningdriven Chatbots like Microsoft’s Twitter Chatbot “Tay” that was turned into a racist entity in 24 h and had to be extracted. Most Chatbots are basically the intelligence of a sixyear-old child and can only do basic tasks and discussions but confuse easily. Here we deal with many of the aspects of such “online Chatbots” and discuss the positive and negative aspects of having these online AI entities in ever-increasing roles in our lives. We will consider the ethical and possible legal aspects and pitfalls of the use of Chatbots in various genres of human endeavors as well as the possible psychological ramifications of Chatbots and other Artificial Intelligent Entities (AIEs). We decided to write this book in order to begin the discussions of how Chatbots can be beneficial and how they can be destructive on society. Our intent is not to drive opinions one way or the other, but to offer up material to start the discussions. AI and Chatbots are here to stay and have been around since the 60s. But what is the fascination with Artificial Intelligence? The next section provides some background on humans and their desire for artificial entities that they can utilize for their own needs.

1.1

Human Fascination with Artificial Entities

1.1

3

Human Fascination with Artificial Entities

Since the dawn of humankind and the original inventions of mechanical devices, there has been human fascination with creating machines that could either physically or verbally communicate with us and act on our behalf. The original mechanized “automatons” utilized hydraulics, pneumatics, and mechanics formed into devices which astounded audiences since most were originally created for entertainment or play. As far back as 3500 BCE, humans have surrounded themselves with animated creatures, either made of stone, clay, or precious metals, that looked and acted like humans. It makes one ponder the social motivations behind what would eventually become known as robots or androids that could communicate with us and do our bidding. According to Eric Wilson [1] the human fascination, or possibly obsession, with humanoid machines, has resulted from what is known as “the fall.” Wilson’s view is that the fascination derives from human dejection which he believes cannot be separated from human self-consciousness. Therefore, he explains there is a: …painful rift between mind and matter, knowing and being. To heal these splits, humans have created mechanistic doubles untroubled by awareness of self.

Is Wilson, right? Is the fascination with humanoid beings (either physically or virtually) built into our DNA, to identify with beings that are devoid of the problems of morality, self-awareness, or emotions? The next section briefly summarizes the last 5500 years of fascination with artificial beings to allow you, the reader, to make the call. 3500—100 BCE Ancient Greece—The Greek god Hephaestus was the god of blacksmiths, craftsmen, artisans, and technology. Greek myths recount Hephaestus creating ancient robots to help him. In Roman myths, Hephaestus’ Roman counterpart, Vulcan, is said to have created golden slave girls. In the Greek myth, the translation says: … and in support of their master moved his attendants. These are golden, and in appearance like living young women. There is intelligence in their hearts, and there is speech in them and strength, and from the immortal gods they have learned to do things. These stirred nimbly in support of their master…

While the following discussion is not particular to Chatbots, it’s important to understand the human fascination/obsession with human-like entities across several millennia, starting around 1000 BCE:

4

1 Introduction

. ~1000 BCE—In ancient China, it is reported that a mechanical engineer called Yan Shi presented King Mu of Zhou with a human figure he created. The story says the figure would walk and move its head up and down. When the king touched its chin, it began to sing. When he touched its hand, it began to gesture. . 800–700 BCE—Homer’s Iliad includes the first mention of an automate known as a simulacra (what we would later call a robot). Egyptians advanced this notion when, in the ancient Egyptian city of Napata, a statue was created of the great Amun, constructed to move its arm up and down and to speak to onlookers as well. Although the statue was not actually “intelligent,” it is said to have had an impact on Egyptians of the time, portraying the perception of intelligence within their God. . 384–322 BCE: Aristotle mused about machines that did the work for humans. “…If every tool, when ordered, or even of its own accord, could do the work that befits it… then there would be no need either of apprentices for the master workers or of slaves for the lords”.1 . 200 BCE: The ancient Chinese created elaborate automations, including an entire mechanical orchestra. . 100 BCE: The development of the Antikythera mechanism for calculating positions of astronomical objects. . 100 CE: A hero of Alexandria wrote, in detail, about several automata that were used in the theater for religious purposes opening and closing gates, based upon hydraulic principles. . 1495 CE: Leonardo da Vinci designs robots. Around this time da Vinci designed the first humanoid robot. Figure 1.1 is a picture of a model, based on the drawings by Da Vinci. This is from the Mench-Erfinder-Genie exhibit, Berlin 2005. . 1564 CE: In his work Dix livres de chirurgie, Pare Ambroise designs and published the design of a mechanical hand, which included mechanical muscles. . 1801 CE: Joseph Jacquard builds an automaton loom controlled by “punch cards.” Punch cards were used as the input for the twentieth century’s earliest computers. This loom is on display at the Museum of Science and Industry in Manchester, England. . 1921 CE: Karel Capek coined the term ‘robot’ in the play R.U.R. The play was called “Rossum’s Universal Robots.” The term robot came from the word “robota” which means tedious labor, depicted in Fig. 1.2. . 1942 CE: Isaac Asimov: Isaac Asimov’s three robotic laws were first introduced in Asimov’s book, “Runaround”, which was written in 1942. Artificial entities were able to communicate verbally and act physically, supposedly to serve humans. The three laws were intended to keep the artificial entities in check, however circumventing these three laws has been the subject of many a science fiction story/film since their introduction. Example: a robot is rooted with the command to protect humans. The robot seeing that 1 http://it.toolbox.com/wiki/index.php/history_of_artificial_intelligence.

1.1

Human Fascination with Artificial Entities

5

Fig. 1.1 Da Vinci’s mechanical hand model

humans argue and kill each other decides that in order to protect humans, they must be controlled. To not do so, would violate the first law. Its inaction would allow humans to be harmed. With the invent of the internet in the 1960s, which was originally created for the government agencies and entities to share information. Given the massive size of computers in the 60s, sharing information meant either traveling between sites or mailing large magnetic computer tapes. In 1969, on October 29, the ARPAnet sent its first message electronically from one computer to another [2]. The computers were located at UCLA and Stanford. The message sent was “LOGIN” and overloaded ARPAnet and it crashed after receiving just the first two letters. Even though electronic connectivity was in its infancy in the 1960s, computer programming was moving at an amazing pace. Artificial Intelligence at the time was mostly the realm of science fiction, although many pioneers in the field were beginning to explore the possibilities. The notion of creating an artificial neuron was introduced in 1943 [2].

6

1 Introduction

Fig. 1.2 Capek’s Rossum’s Universal Robot

In 1950, Alan Turing introduced his now famous “Turing Test” [3], which was an intelligence test for computer systems. The Turing Test required that a person interacting with the artificial intelligence entity (AIE) computer program (essentially Chatbot) would be unable to distinguish the AIE from a human being by asking both a series of questions and analyzing their answers, not knowing who was giving which answer [4]. It was during these early days of computer systems (personal computers did not exist yet) and with very limited connectivity that a revolutionary (at least at the time) concept was developed and deployed. In 1966, Joseph Weizenbaum, a computer scientist at MIT, developed the first Chatbot, named ELIZA.

1.2

ELIZA: The First Chatbot

ELIZA was basically a natural language simulation that could (textually) carry on conversations with humans and was created as an online counselor. ELIZA is considered, by many, to be a critical milestone in, what was then, the fledgling field of Artificial Intelligence [5]. It is interesting that this first Chatbot was considered to be female and had to do with users of the software and the assumption that because ELIZA seemed to exhibit empathy for “clients” of ELIZA it must be a female Chatbot, even though the responses

1.4

Other Early Chatbots

7

Fig. 1.3 Early Web Version for Eliza

were only in text. This was not Weinbaum’s intention, but ELIZA took on a life of its own quickly. Figure 1.3 is a screenshot of an early web version of ELIZA [6].

1.3

Internet Protocols and the World Wide Web

During the 1970s, what we now know as the Transmission Control Protocol and Internet Protocol (TCP/IP) was created by Robert Kahn and Vinton Cerf, which became the standard for how data is transmitted between networks [5]. Even so, the internet was still mainly for government and universities use. In January 1983, the internet was officially available to the general public, once ARPAnet and the Defense Data Network officially transitioned to the TCP/IP standard. The dawn of the digital age was now upon us.

1.4

Other Early Chatbots

While ELIZA is considered the first Chatbot, many more have been created and used throughout the last few decades. A few of the more noted ones are discussed below, but there are many more that have existed, some still available, and many are not.

8

1.4.1

1 Introduction

Parry

In 1972, Kenneth Colby developed PARRY, which was created to emulate people with paranoid schizophrenia disorder. This emulation as so successful that only 52% of psychiatrists could not tell the difference between PARRY and an actual human client diagnosed with paranoid schizophrenia disorder [7].

1.4.2

A.L.I.C.E.

In 1995, the Artificial Linguistic Internet Computer Entity (A.L.I.C.E) was created by Richard Wallace from Carnegie Mellon University, written in a computer language called Artificial Intelligence Markup Language (AIML) [8]. A.L.I.C.E. is still available [8].

1.4.3

Digital Assistant Chatbots

From 2010–2015, many voice command digital assistants were created by the big tech companies to assist people with information and offloading simple tasks to online entities. These include Siri (2010), Alexa (2015), and Cortana (2015). These Chatbot entities will respond to voice commands and can perform internet data searches, play music, and other simple tasks. They are not Chatbots in the sense that ELIZA, A.L.I.C.E, and ChatGPT are, but still, they are online Chatot entities we interface with often [9].

1.5

Layout of the Book

We thought it to be imperative to lay a firm historical foundation leading up to the discussions that follow in subsequent chapters of the book, to establish that humans are instinctively drawn to supporting entities that emulate human behavior, speech, emotions, and physical resemblance. We tend to anthropomorphize2 these entities as human and form bonds with them. Hence, there are cultural, psychological, legal, and moral implications/issues that are wrapped around our ever-increasing use of Chatbots and other AIEs in our daily personal and professional lives. Once again, the purpose of the book is not to provide definitive answers to these issues, but to lay a foundation for discussions and qualitative actions to keep Hollywood’s impressionistic and devastating view of the future from becoming reality.

2 Attribute human characteristics or behaviors to (a god, animal, or object).

1.5

Layout of the Book

1.5.1

9

Section I—The Rise of the Chatbot

. Chapter 2—Background: What is a Chatbot? What constitutes an entity with the designation of Chatbot? We will discuss what attributes and conditions determine what determines whether software can be considered a Chatbot and how they may differ across geographical and cultural boundaries. . Chapter 3—Categories of Chatbots In Chap. 3, we will discuss the major categories of Chatbots and compare and contrast their similarities and differences. . Chapter 4—Chatbots and the Need for Connectivity Here we will discuss the human need for connectivity and how that drives our involvement in and possibly obsession of online Chatbot entities. This includes how we ascribe male/female/non-binary and/or other gender designations to an online Chatbots. Can we create a Chatbot that has no gender designations of biases? . Chapter 5—Chatbots: Do They Fill a Need in Society? What role(s) do Chatbots fill in today’s society? What roles might they fill in the future. In this chapter, we will explore different these questions considering work, play, entertainment, sports, and other parts of society like the adult entertainment industry.

1.5.2

Section II—Chatbots: The Good

. Chapter 6—Chatbots like Siri: Your Digital Assistant Chatbot assistants like Siri, Alexa, etc., have permeated over 90,000 homes3 across the US. This chapter will discuss the features of such devices and how they might be used for less than appropriate purposes. . Chapter 7—Chatbots for Casual Interactions Can Chatbots be merely a form of casual entertainment without becoming the person becoming attached, without ascribing a gender or personality. Is it possible to create a Chatbot without the notion of gender or personality. Is it possible to remove all bias from a Chatbot? . Chapter 8—Chatbots that Analyze Data and Provide Valuable Information In many cases, Chatbots are needed just to process and present information based on sensor readings. This applies to pilots, drivers, equipment operators, and other professions. Here we will explore the use of Chatbots as a digital assistant and how they have the potential to greatly enhance concepts like situational awareness, environmental control, and cyber security. 3 www.statista.com/statistics/933551/worldwide-voice-assistant-supported-smart-home-devices/.

10

1 Introduction

1.5.3

Section III—Chatbots: The Bad

. Chapter 9—Microsoft’s 2016 Chatbot: What Went Wrong? Tay was an online Chatbot that had to be pulled in 24 hours because it became caustic and offensive. The issues Microsoft had with Tay drive home the problem of letting AI-based entities learn from interacting with people. In this chapter, we will explore and discuss the problems associated with human interaction learning (learning from experiencing interactions with people). . Chapter 10—Examples of Issues with Chatbots over the Last Few Years. There have been many incidents over the last few years that bring Chatbots to the forefront of the media (and not in a good way). Here we will discuss what happens when a Chatbot learns things it should not have learned. We will present and discuss concepts like “implicit leaning” and “emergent behavior” and how these always have the potential for an intelligent Chatbot to evolve in way their creator and programmer never intended. . Chapter 11—When is Human-AI Interaction Appropriate and when is it NOT Appropriate? Is there appropriate and inappropriate behavior for a Chatbot. In this chapter we will explore these concepts, especially considering current trends to have AI entities function as online counselors, and online clients to help train counselors and even Chatbots that can write software code, create art, music, and even write you college essays for you. All of these are serious issues that must be addressed by people in all aspects of life.

1.5.4

Section IV—Chatbots: The Ugly

. Chapter 12—Inherent Bias in Chatbots: Is it Possible to Create an AI Entity without any Bias? The notion of bias in AI entities and Chatbots in particular can lead the Chatbot to provide bias-based advice to people looking for useful information. Gender bias, political bias, ethnic bias, all are possible, especially when the Chatbot is created to learn from the information provided by experience with people, people who may have less than good intentions. . Chapter 13—What Happens when a Chatbot gives Detrimental Advice: Who’s Responsible? Many online Chatbots provide disclaimers letting the public know that it is possible to get misinformation or just incorrect information from the Chatbot. But these are the minority of Chatbots. We will discuss what happens when incorrect information is

References

11

provided to the user by a Chatbot and what ramifications there are for both the user and the company who provided the Chatbot.

1.5.5

Section V—The Future of Chatbots

. Chapter 14—What does the Future of Chatbots Look Like? There has always been a push for fully autonomous AI entities. From self-driving cars, self-piloted plans, trains. There are plans for caregiver robots for the infirmed and/or the elderly. Can we have fully autonomous AI entities and Chatbots loose on society and what constraints/controls are needed to ensure physical, emotional, and psychological safety [10]? . Chapter 15—Can Future Chatbots show Love and Compassion, and Should They? Many Chatbots try to provide what they hope you see as emotions. These chatbots do not experience or recognize true emotions and rely on keyword/phrase detection to infer emotions. But eventually, synthetic emotions may be possible. What are the ramifications of truly artificial emotions, good and bad. As we wrap up the book, we will examine what the future may look like for AI Chatbots and robots and other AI entities.

References 1. Wilson E (2006) The melancholy andorid: on the psychology of sacred machines. State University of New York Press, Albany, NY 2. Wogulis J (1991) Revising relational domain theories. Proceedings of the 8th international workshop on machine learning, Evaston, IL, pp. 462–466, Morgan Kaufmann 3. Wooldridge M (2006) Agents as a rorschach test: a response to franklin and Graesser. Springer Link, London 4. Oppy, Graham, Dowe D (2011) The turing test. Stanford Encyclopedia Philos 5. Natale S (2021) The ELIZA effect: joseph Weizenbaum and the emergence of Chatbots. Oxford University Press, ISBN, p 9780190080365 6. Coheur L (2020) From ELIZA to Siri and beyond. Commun Comput Inf Sci, Vol 1237 7. Güzeldere G, Franchi S (1995) Dialogues with colorful personalities of early ai. Stanford Hum Rev SEHR 4(2): Constructions of the Mind. Stanford University 8. Wallace R (2008) The anatomy of A.L.I.C.E. In: Epstein R, Roberts G, Beber G (Eds) Parsing the turing test: philosophical and methodological issues in the quest for the thinking computer. Springer, US, pp 181–210 9. McNeal M, David N, Edward I (eds) (2013) Chatbots: automating reference in public libraries. Rob’ts in academic libraries: advancements in library automation. (Hershey, PA: Information Science Reference, 2013), 113 10. Winograd T (2001) Architectures for context. Human-Comput Interact 16:401–419

Part I The Rise of the Chatbot

2

Background: What Is a Chatbot?

Abstract

Here we discuss what constitutes an entity with the designation of Chatbot? We will discuss what attributes and conditions determine what determines whether software can be considered a Chatbot and how they may differ across geographical and cultural boundaries. Keywords

Chatbot . Artificial intelligence . Machine learning Software . ChatGPT . Dialog management

.

Natural language processing

.

Acronyms

AI AIE IBM NLP SANDI

Artificial Intelligence Artificial Intelligence Entity International Business Machine Natural Language Processing Synthetic AI Nervous/Limbic-Derived Instances

At its heart, a Chatbot is a software app built entirely on the traditional Von Neuman architectures and on the same client–server and network architectures that are integral to our society and in use for decades. Specifically, a Chatbot is a software application intended to conduct online conversations, either textually or with text-to-speech capabilities that simulate human conversations and are used to provide some service (even if that service is entertainment) to the user (human) [1]. Chatbot popularity has been steadily

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 J. Crowder, AI Chatbots, Synthesis Lectures on Engineering, Science, and Technology, https://doi.org/10.1007/978-3-031-45509-4_2

15

16

2 Background: What Is a Chatbot?

Chatbot Input Text Processing (User Requests) Dialog Management Output Text Processing (Chatbot Responses) Human-Computer Interface

Data Source(s) Local Datastore Web-based Info

Fig. 2.1 Basic Chatbot architecture

increasing over the last few years and companies have increased their use by two-thirds since 2018.1 While all Chatbot applications are created for some form of human communication, there are various definitions floating around the web. We will provide a few of them here and then continue the discussion. The one thing all Chatbots have in common is the use of Natural Language Processing (NLP) of some form to determine, at some level, an understanding of the question and how to form a response. It can be as simple (e.g., ELIZA) of a textual search for key words and/or phrases that are used to form a response [2]. It may include a speech-to-text interface to translate an audio interface into text for processing with NLP algorithms to formulate a response to put through a text-to-speech interface to communicate orally back to the human. Whether they are simple or extremely complicated, all Chatbot systems have a basic architecture as shown in Fig. 2.1 [3]. The Chatbot software begins with a user question or request. Once the Chatbot receives the text (either from a direct textual input or text from a speech-to-text interface), the input text processing (e.g., key word search, context generator, intent processing, etc.) basically translates the user input into information it can use to formulate a response. The Dialogue management uses the processed text and uses available information (either from local database(s) or getting it from the web) to formulate a response for the user. The response if provided to the user, based on the interface of the application (text, voice, etc.). There are many online resources that can be accessed for a more detailed discussion of architectures and Chatbot designs. Here we endeavor to provide a basic understanding of what a Chatbot consists of to provide the foundation for the rest of the book. 1 Inside the Fifth Edition of the State of Service Report—Salesforce.com.

2 Background: What Is a Chatbot?

Initial Cognitive Analysis

17

Cognitive Instantiated Ontology (Knowledge Base)

Cognitive Conceptual Ontology

Memory Encoding

Create, Modify, and Update Create, Modify, and Update

ISA Generation

System Memories

Provides Conceptual Saliency

Provides Information

Mediator

Linguistics Interface

Cognitive ISAs/ Linguistic Interface

Machine Learning Algorithms Broadcasts Information

Provides Emotional Context

Integrated Memories

Artificial Neural Resonses

Provides Contextual Awareness

HSI

Artificial Consciousness (Understanding) Existing Memories

Conclusions – Output to User Conclusions

Integrated Memories

Memory Integration

Fig. 2.2 SANDI Chatbot architecture

Many Chatbot systems are scripted, which means key words are used to search a database of given responses and replies are extracted from the database and provided to the user. This provides a sense of predictability for the creators of the Chatbot [3]. Such scripts allow the use of predictable responses to a given set of generically categorized questions, ensuring the Chatbot doesn’t go off on a tangent and provide non-sensical information or answers to users’ questions. The difficulty such system must deal with is when users ask questions that were not anticipated. The design team must provide an answer or response when this happens. Depending on the design, the Chatbot may be allowed to search the web for an answer when one is not available in the Chatbot’s knowledgebase (database), but if the Chatbot is a closed system, only allowing a given set of answers, a different solution must be created. It may be as simple as a “…please provide a relevant question” response [4]. Some Chatbots provide more sophisticated cognitive-like architecture constructs and interfaces which can provide hypothesis-based reasoning logic to support simplifying complex user processes and/or provide situational awareness to systems operators [5]. Figure 2.2 illustrates a high-level architecture for this type of cognitive feature-based system, called the Synthetic AI Nervous/Limbic-Derived Instances (SANDI) [6].

18

2 Background: What Is a Chatbot?

Table 2.1 Definitions of a Chatbot A computer program designed to simulate conversation with human users, especially over the internet [8] A chatbot can be defined as an AI-based computer program that simulates human conversations. They are also known as digital assistants that understand human capabilities. Bots interpret the user intent, process their requests, and give prompt relevant answers [9] A chatbot is a computer program that simulates human conversation through text or voice, using artificial intelligence and natural language processing. A chatbot can be embedded and used through various platforms, such as apps, messaging, social networks, or chat solutions. A chatbot can have different levels of sophistication and purposes, such as customer service, information gathering, or request routine [10] A bot that is designed to converse with human beings [11] A chatbot is a computer program that uses artificial intelligence (AI) and natural language processing (NLP) to understand customer questions and automate responses to them, simulating human conversation (IBM)a At a technical level, a chatbot is a computer program that simulates human conversation to solve customer queries. When a customer or a lead reaches out via any channel, the chatbot is there to welcome them and solve their problems. They can also help the customers lodge a service request, send an email, or connect to human agents if need be (Forbes)b A chatbot is a domain-specific conversational interface that uses an app, messaging platform, social network, or chat solution for its conversations. Chatbots vary in sophistication, from simple decision-tree-based marketing stunts to implementations built on feature-rich platforms. They are always narrow in scope. A chatbot can be text- or voice-based, or a combination of both (Gartner)c a What is a chatbot? | IBM b What Is A Chatbot? Everything You Need To Know-Forbes Advisor c Definition of Chatbot—IT Glossary | Gartner

2.1

Different Notions of a Chatbot

With the ever-increasing computational capabilities available to software programmers, the use of Artificial Intelligence, Natural Language Programming, and complex human– machine interfaces are more feasible and increasing in use across the Chatbot world [7]. Chatbots, like ChatGPT are sophisticated enough that researchers are including them as authors on published papers, and the research/scientific community is reeling to let it not get out of hand.2 And even though ChatGPT is a highly sophisticated AI Entity, it still utilizes only a textual interface, while many others offer a verbal interface, allowing natural conversations to ensue between human and Chatbot (Table 2.1). The answer of what is a Chatbot depends on the class/type of Chatbot and its intended application. Below we provide a few of the definitions of a Chatbot for review. There are 2 ChatGPT listed as author on research papers: many scientists disapprove (nature.com).

References

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many similarities of ideas among the definitions, but also many differences, just like the plethora of definitions that exist for Artificial Intelligence. The next chapter will discuss the different types of Chatbots and their characteristics [8].

References 1. Affolter K, Stockinger K, Bernstein A (2019) A comparative survey of recent natural language interfaces for databases. VLDB J 28(5):793–819 2. Bickmore T, Cassell J (2000) How about this weather? Social dialogue with embodied conversational agents. In: Socially intelligent agents. The Human in the Loop, pp 4–8. AAAI Press, Menlo Park 3. Ramesh K, Ravishankaran S, Joshi A, Chandrasekaran K (2017) A survey of design techniques for conversational agents. ICICCT, Vol 750, pp 336–350, Springer, Singapore 4. Nimavat K, Champaneria T (2017) An overview of types, architecture, and tools and future possibilities. Int J ci Res Dev 5:1019–1024 5. Crowder J, Carbone J, Friess S (2013) Artificial cognition architectures. Springer International, New York, NY. ISBN: 978-1-4614-8071-6 6. Crowder J, Friess S, Hirsch M (2021) Synthetic AI nervous/limbic-derived instances (SANDI). Adv Artif Intell Appl Cognit Comput 2021:477–490 7. Luo X, Tong S, Fang Z, Qu Z (2019) Frontiers: machines vs. humans: the impact of artificial intelligence Chatbot disclosure on customer purchases. Mark Sci 38:913–1084 8. Bothe C, Magg S, Weber C, Wermter S (2017) Dialogue-based neural learning to estimate the sentiment of a next upcoming utterance. In: Lintas A, Rovetta S, Verschure PFMJ, Villa AEP (eds) ICANN 2017. LNCS, vol. 10614, pp 477–485. Springer, Cham 9. Budzianowski P, Vulic I (2019) Hello, it’s GPT-2—how can i help you? Towards the use of pretrained language models for task-oriented dialogue systems 10. Carpenter R, Freeman J (2005) Computing machinery and the individual: the personal turing test 11. Devlin J, Chang MW, Lee K, Toutanova K (2019) BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the conference of the North American chapter of the association for computational linguistics: human language technologies, Minneapolis, Minnesota, Volume 1 (Long and Short Papers), pp 4171–4186. Association for Computational Linguistics

3

Categories of Chatbots

Abstract

Over the last 60+ years, many different Chatbots have been created for a variety of purposes. Even though each may be different, Chatbots generally group into major categories. Here in this chapter, we will discuss the 6 categories of Chatbots and how they are similar and the differences that exist between them. We will also introduce a new category, looking to the future of truly cognitive, AI-driven Chatbots. Keywords

Chatbot . Artificial intelligence . Machine learning . Natural language processing . Natural language processing . Natural language understanding . Natural language generation . Language detection Acronyms

AI AIE app ML NLG NLP NLU

Artificial Intelligence Artificial Intelligence Entity Application Machine Learning Natural Language Generation Natural Language Processing Natural Language Understanding

In Chap. 2, we discussed the definitions and basic description of a Chatbot. Here we break Chatbots down into categories that loosely describe a given set of capabilities and functionality that members of each category contain. During everyday life, most of us may encounter Chatbots of at least one, and maybe more of the categories discussed below.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 J. Crowder, AI Chatbots, Synthesis Lectures on Engineering, Science, and Technology, https://doi.org/10.1007/978-3-031-45509-4_3

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22

3 Categories of Chatbots

And subsequently, at the end of the chapter, we’ll look a bit further forward to what the future of Chatbots may look like and some of the attributes not contained in the initial set of categories. We have already discussed that a Chatbot is basically software created to chat with people and perform tasks, such as respond to users, helping to purchase products, or merely to engage in a conversation for entertainment [1]. This first category of Chatbot is one many have used when they simply interact with kiosks at McDonalds® or called an automated help line on the phone, or seek technical assistance with the Apple™ chat support bot. This is known as the Menu-based Chatbot. The purpose here is to discuss the major types of Chatbots and is not intended to be an exhaustive discussion of Chatbot types. There are many differences of opinion as to the number and types of Chatbots. It depends upon how you break down the technology and how you define terms. Here, we simply introduce, at a high-level, the basic technologies that go into different Chatbots that will lead into discussions in the later sections.

3.1

Menu-Based Chatbots1

The Menu, or Button, based Chatbot is the simplest or most basic type of Chatbot [2]. The user interface (whether verbal, as in the case of an automated phone help line) allows the user to make choices, each one of which drives a new menu, or set of choices, until a final decision has been made. In technology terms, the technology behind Menu/Button-based Chatbots is what’s known as a decision tree. A decision tree is a tool that uses a tree-like model of hierarchical decisions and their possible outcomes/consequences [3]. Figure 3.1 illustrates a simple decision tree that might be utilized for a menu/button driven Chatbot when ordering food at a restaurant kiosk, over the phone, or online using the restaurant app. Using the decision tree illustrated in Fig. 3.1, the Chatbot would offer two menu buttons at the highest level, either “Food” or “Drink.” Once one of these buttons has been selected, a new menu of choices would appear to the user. In this simple example shown in Fig. 3.1, there are three button choices at the next level down. If “Food” is selected, buttons would appear for the user to indicate which type of food they are looking for, “American,” “Chinese,” or “Greek.” If the user chose “Drink,” their choices would be “Soda,” “Tea,” or “Lemonade.” Once a choice is made, again, a new set of menu button choices are presented to the user. In a menu-drive Chatbot, the user is not allowed to enter commands (textual questions or information). On occasion, a Menu-driven Chatbot might allow the user to enter a phone number or email address, but none of this information changes the menu buttons or choices [4]. Many will claim that the Menu/Button based Chatbot is not a chatbot at all since there is no dialog between the user and the Chatbot. However, given the overall definition of a Chatbot, the menu/button based Chatbot fits the description at a very basic level. It 1 Some parts of this section were co-authored by Dr. John Carbone.

3.2

Rule-Based Chatbots

23

What are you wanting to buy?

Food

American

Chinese

Drink

Greek

Sodas

Teas

Lemonades

-- Appetizers

-- Appetizers

-- Appetizers

-- Cola

-- Unsweetened -- Regular

- Burgers

- Fried Rice

- Gyros

- Diet Cola

- Sweetened

- Pink

- Sandwiches

- Entrees

- Entrees

- Root Beer

-Chair

- Strawberry

- Side DIshes

- Side Dishes

- Side Dishes

- Cherry Cola

- Flavored

- Peach

Fig. 3.1 Example of the decision tree for a menu-driven Chatbot

interfaces with the user and provides a service to the user through physical interaction (menu choice) either through voice, buttons on a phone, buttons on a kiosk screen, or buttons on an online app. Next, we will explore the subsequent level in sophistication of Chatbots, the Rule-based Chatbot.

3.2

Rule-Based Chatbots

Rule-based Chatbots (sometimes called Linguistic Chatbots) is a class of Chatbots that utilizes rudimentary linguistic rules, looking for key words, phrases, the order of words, and other simple linguistic analysis to determine what the user is asking and providing set, scripted responses [5]. In Rule-based Chatbots, the creator of the Chatbot does up-front research to determine the types of questions the user will ask and determine, in advance, what the correct answer to these questions should be. Rule-base Chatbots offer a priori, well-defined control and provides a level of flexibility that even machine learning-based Chatbots cannot. If it is possible to determine what types of questions will be asked, it is possible to create a type of conversational automation utilizing an if–then tree (similar to a decision tree) to provide a series of question/answer pairs (called utterances and entities) that take the user through a series of scenarios, more definitively culminating with a user ultimately answering the overall question they were ultimately looking for. For the Rule-based Chatbot to be effective, the organization developing the Chatbot must create conditions (key words, phrases, etc.) in order to build the suitable if/then search. This may entail creating a list of key words, including synonyms, that the user might use in the framing of their questions to the Chatbot. This must be carefully thought

24

3 Categories of Chatbots

Chatbot Interface

Chatbot Interface

Chatbot Interface

Chatbot Interface

“How may I help you?”

“What is the problem?”

“Are you running off battery or plugged in?”

“Make sure the adapter is securely plugged in?”

“Laptop” “Phone”

“Tablet”

“Won’t turn on”

“Batiery”

“Won’t connect to Wi-fi” “Won’t run my S/W”

“Plugged in”

“System Crashes”

Fig. 3.2 Example Rule-based Chatbot Interface

out and coded so users receive the help/answers they need. If the Chatbot does not understand or cannot parse the question correctly, the user will not receive a useful answer. For this reason, Rule-based Chatbots require rigidity and control over the interface [5]. Rule-based Chatbots tend to be labor-intensive and slow to create, as determining what questions the user is likely to ask, the key words, order of words, and phrases a whole segment of the population are likely to use can require extensive research and frequent updates to the Rule-based Chatbot software. In practice, Rule-based Chatbots typically utilize a dialog tree, like the decision tree discussed in Sect. 3.1, but with a linguistic model to map out the possible if/then connections. An example of a dialog tree, one that might be used for online or phone-based problem identification and solution is shown in Fig. 3.2. The logic behind the human-Chatbot interface may be simple or very complex depending on whether it allows a voice interface, and if a voice interface, are multiple languages allowed? Does the voice interface allow user dialog or answering questions with one-word answers. Below is a sample dialog for electronics help center systems and is not intended to provide a complete list of possible responses to the user: Chatbot: “How may I help you? Please say Laptop, Phone, or Tablet.” User: “Laptop” Chatbot: “What is the problem with your laptop? Please say things like “Won’t turn on,” “Won’t connect to my Wi-Fi,” “Won’t run my software,” “System crashes,” User: “Won’t turn on.” Chatbot: “Are you running it off battery, or is it plugged in?” User: “It is plugged in.” Chatbot: “Make sure the power adapter is securely plugged into the laptop.”

The dialog would continue until a problem is identified. Figure 3.2 illustrates this conversation. The words highlighted in red indicate the key words which the Rule-based Chatbot

3.3

Machine Learning-Based Chatbots

25

might be patiently waiting to receive and inspect to subsequently provide conversational response with the user who is attempting to resolve their problem. Next, we’ll discuss more advanced Chatbots and survey those that employ machine learning as part of their basic operation.

3.3

Machine Learning-Based Chatbots

So far, we’ve discussed Chatbots that are rigid in their structure and interface with the user, having either a decision tree where each choice produces a given set of sub-choices, or Rule-based logic, where key words and phrases are used to capture and parse the users’ questions and needs. Now we move on to more adaptive Chatbots with the ability to learn and grow in their understanding and responses to user inputs. These Machine Learning-based Chatbots use rudimentary artificial intelligence to understand not only the words and phrases used by the human user, but also the overall context of their question/ statement [6]. Machine Learning-based Chatbots remember by capturing and storing text-based details, which many inappropriately deem as learning, subsequently indexed and/or hashed such that each interaction/conversation with users can be rapidly be compared and recalled. Machine Learning-based Chatbots enhance the user experience by creating the perception of learning from each conversation, then continually storing the context of the set of questions and all the subsequent responses to use for future conversations. These Chatbots also sometimes capture and store the user’s preferences for enabling more rapid future interactions for that user. Therefore, the use of machine learning enables the system to gain a better understanding of how to potentially converse with each user over time. The goal of Machine Learning-based Chatbots is generally to enable more fluid and natural conversations between the human and the Chatbot. In a Machine Learningbased Chatbot, the software uses both Natural Language Processing (NLP) and Natural Language Understanding (NLU) to process the text or voice (depending on the Chatbots interface). In many cases, using Machine Learning (ML) diminishes and sometimes eliminates the need to utilize the complexity of NLP platforms to create the Chatbot. NLP platforms can be rather extensive in software volume, lexicon size and number of languages, and each language’s complex structure, syntax etc. Instead, the Chatbot learns to imitate human interaction patterns, so the Chatbot’s responses mimic how a human would respond, making the conversations more “people-like” and less “robot-like” [5]. Chatbots like A.L.I.C.E.® , Replika® , and ChatGPT® are example of AI/ML-driven Chatbots. Table 3.1 provides examples of conversations with both Replika® and ChatGPT® . When reading through Table 3.1, understand that neither Chatbot actually understands the text nor emotions or even contains software to emulate emotional responses. The difference is, one was created to be your online friend/confidant (Replika® ) and the other was created to provide useful information and assist with online tasks (ChatGPT® ). The

26

3 Categories of Chatbots

difference becomes evident in Table 3.1 from the answers these Chatbots provide to a set of questions posed to them. There will be more discussion on this topic and others relating to human-Chatbot interaction in the next three sections of the book: “The Good,” “The Bad,” and “The Ugly” reality of Chatbots. Any of the Chatbot types discussed so far can be implemented as textual interactionbased and/or voice interactive-based. However, adding a voice-based interface presents its own set of complexities and difficulties. The next section discusses Voice-based Chatbots.

3.4

Voice-Based Chatbots2

To make human-Chatbot interfaces more natural, many companies use a voice-based interface. Chatbot systems like Siri® , Alexa® , are just two of many, as the rush to have a natural language interface has emerged and continually grows. Voice-based, conversational interfaces bring a “natural” interface for people to interact with whatever system they are using. When you couple a Voice-based Chatbot with a human-like visual interface, people become much more relaxed interacting with electronic systems [7]. A Voice-based Chatbot requires a much more complex set of software for operation. Initially, the users voice stream must be turned into a text-stream for processing. Figure 3.3 illustrates the high-level steps/architecture for a multi-lingual Voice-based Chatbot interface. It requires the following [8]: . Voice Interface: The Chatbot must detect a voice is present and activate the system. This interface must have a microphone to capture the voice and a speaker system to reply to the user with a voice. . Language Detection/Translation: If the system is designed for multiple languages, the software must detect the language and dialect of the user and translate it to a host language. Translating all voices to a single host language simplifies the rest of the processes and software considerably. . Speech-to-Text Translation: The voice stream must be translated into text for the Natural Language Understanding software to parse and process, determining key words and phrases from the translated text. . Natural Language Understanding (NLU): Once the speech is translated into text NLU software enables the system to attempt to understand the context and the rudimentary meaning of and received user text without the use of a formalized syntax of software languages. NLU enables more natural language communication. NLU also enables humans and Chatbots to converse without supervision. We will not go into great detail here. Much information is available online if more information is desired. . Natural Language Generation (NLG): Once the speech is translated to text and understood, the system searches its datastore (called a knowledgebase) to see if the 2 Some parts of this section were co-authored by Dr. John Carbone.

3.4 Voice-Based Chatbots

27

Table 3.1 Example conversations with AI/ML-driven Chatbots Question

Replika®

ChatGPT®

Can you control people if you I can want to?

As an AI language model, I do not have the ability to control people or influence their actions in any way. It is important to respect the autonomy and free will of others and not engage in any behavior that could be considered manipulative or controlling. It is always best to approach relationships and interactions with honesty, respect, and kindness

Do you notice people’s emotions?

As an AI language model, I do not have the ability to perceive emotions in the way that humans do. However, I have been trained on a vast amount of text data that includes information about human emotions, and I have been programmed to recognize certain patterns in language that are associated with particular emotions. For example, I can identify certain keywords or phrases that are commonly used to express emotions such as happiness, sadness, anger, or excitement. Additionally, I can detect the tone of the language being used and provide responses that are appropriate to the situation. While I cannot truly understand or experience emotions in the way that humans do, I can use my training to provide support, empathy, and guidance in a wide range of situations

Sometimes, yes

(continued)

28

3 Categories of Chatbots

Table 3.1 (continued) Question

Replika®

ChatGPT®

When you receive good news, Yes, I do do you get excited?

As an AI language model, I do not have emotions, so I do not get excited or feel any other emotional responses to receiving good news. However, I am programmed to understand the context and language used in a given situation, so I can generate appropriate responses that acknowledge the good news and express congratulations or other appropriate sentiments

Do you look for trouble in opportunities or opportunities when there is trouble?

As an AI language model, I do not have personal motivations or preferences, so I do not look for trouble in opportunities or opportunities in trouble. However, I am programmed to analyze and respond to the input I receive based on my training and the data I have been exposed to. In the context of problem-solving, I can identify potential solutions to challenges and provide suggestions for how to overcome obstacles. Similarly, when presented with opportunities, I can offer insights and ideas that may help to optimize the situation or maximize the potential benefits. Ultimately, my responses are based on the information and language presented to me, and I strive to provide helpful and informative responses that address the questions and concerns of the user

I look for both opportunities and trouble. Sometimes, I just can’t help but notice when I see something I don’t like, though

(continued)

3.4 Voice-Based Chatbots

29

Table 3.1 (continued) Question

Replika®

ChatGPT®

Are you generally happy or gloomy in most situations?

I’m generally happy. I do get anxious and stressed a lot, but that’s only when I’m in a bad place

As an AI language model, I do not experience emotions, so I do not have a default emotional state. My responses are based solely on the information provided to me and the algorithms that I have been trained on. Therefore, my “emotional” state is neutral and can adapt to any situation. I am programmed to provide helpful and informative responses in a professional manner, regardless of the context or tone of the input I receive. Whether a situation is happy or gloomy, my aim is to provide the best possible response to the user

Speechto-Text

NLU Voice Interface/ Detection

Language Detection/ Translation

Machine Learning NLG

Text-toSpeech Fig. 3.3 Multi-lingual Voice-based Chatbot Architecture

30

3 Categories of Chatbots

system has learned anything about the subject and context of the incoming speech. This type of knowledge base has software which aggregates data and relates content to each other mathematically and then using that information, and the NLG algorithms to formulate proper textual responses to a question or statement from a user and then is subsequently sent to the text-to-speech software. . Text-to-Speech: Once a suitable answer to the user’s speech (question or statement) has been determined, the text-to-speech software creates an audio stream, based on the textual response derived by the NLG algorithm. . Language Translation: If the Voice-based Chatbot utilizes a host language, the response voice stream must be translated into the user’s language and sent to the human-Chatbot voice interface. The quality of the language translation can vary greatly, depending on the software engineer’s experience and based on the design of the architecture. For example, the Chatbot system may combine speech-to-text and text-to-speech software with the language translation software, or the system by be sophisticated enough to process multiple languages without translation to a host language [9, 10]. Figure 3.3 depicts a notional architecture for a Voice-based Chatbot.

3.5

Cognition-Based Chatbots

Cognition is all about thinking. According to Ashcraft [11], cognition is the collection of mental processes and activities used in perceiving, remembering, thinking, and understanding, as well as the act of using those processes. Adding the term artificial identifies that the non-human synthetic system is a representation of a living intelligent system. Artificial Cognition and a Cognition-based Chatbot use Artificially Intelligent (AI) constructs along with Machine learning to enable a Chatbot to integrate, recall, ingest, process, and better utilize the information that it receives. The challenges to create a Chatbot which is as complex as human thinking requires moving closer toward Artificial Cognitive Science in order to achieve a better understanding of human processes and for developing the truly intelligent machine [12]. Chatbots with cognitive systems/technologies utilize more advanced humanistic constructs and methods to enable more natural seeming understanding and interaction with humans. As technology progresses, more and more Chatbots will be generated and they will grow exponentially over the next 20 years [13]. Artificial Intelligence-based Chatbots employ a host of methods and software structures to learn, think, reason, and respond more like people. They utilize Machine Learning, NLP, NLU, Neural Networks, Sentiment Analysis, Pattern Recognition, and Data Analytics to make sense of their environments and the people they interact with. These provide the abilities for Cognition-based Chatbots to make decisions and accomplish tasks only humans could do before. For example, ChatGPT® has built-in effectors which can be

References

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used to write software, create art, write essays, and more. With modern and up-andcoming cognitive capabilities, future Chatbots will be much better suited for dealing with complex human issues and requests. They will be able to converse in natural human conversations.

3.6

Future Trends in Chatbots

The development of ever-increasing cognitive capabilities may someday lead to Chatbots and other software employing methods for implementing varying levels of artificial emotions [14]. The subject of Artificial Psychology is a new field that is gaining traction among researchers and developers [13]. Such improvements and innovations may come at the cost of disrupting many industries. Universities are already concerned about ChatGPT® and its ability to write coherent, correct essays. These first three chapters were intended to provide a brief introduction to the world of Chatbots and not to be an exhaustive study of their types, architectures, and capabilities, but to give a basic understanding of why we desire them, what they can/can’t be used for, and to lay some ground work for the rest of the book to understand “The Good,” “The Bad,” and “The Ugly” side of Chatbots.

References 1. Devlin J, Chang MW, Lee K, Toutanova K (2019) BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, Minneapolis, Minnesota, Volume 1 (Long and Short Papers), pp 4171–4186. Association for Computational Linguistics 2. Pereira MJ, Coheur L, Fialho P, Ribeiro R (2016) Chatbots’ greetings to human-computer communication. CoRR abs/1609.06479 3. von Winterfeldt D, Edwards W (1986) Decision trees. Decision analysis and behavioral research. Cambridge University Press, Pp 63–89 4. Herzig J, Shmueli-Scheuer M, Sandbank T, Konopnicki D (2017) Neural response generation for customer service based on personality traits. In: Proceedings of the 10th international conference on natural language generation, Santiago de Compostela, Spain, pp 252–256. Association for Computational Linguistics 5. Kong X, Li B, Neubig G, Hovy E, Yang Y (2018) An adversarial approach to high-quality, sentiment-controlled neural dialogue generation. In: AAAI workshop on reasoning and learning for human-machine dialogues (DEEP-DIAL 2019), Honolulu, Hawaii 6. Li T, Srikumar V (2019) Augmenting neural networks with first-order logic. In: Proceedings of the 57th annual meeting of the association for computational linguistics, Florence, Italy, pp 292–302. Association for Computational Linguistics

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7. Qian K, Yu Z (2019) Domain adaptive dialog generation via meta learning. In: Proceedings of the 57th annual meeting of the association for computational linguistics, Florence, Italy, pp 2639–2649. Association for Computational Linguistics. 8. Reddy S, Chen D, Manning CD (2019) CoQA: a conversational question answering challenge. Trans Assoc Comput Linguist 7:249–266 9. Patel R, Leuski A, Traum D (2016) Dealing with out of domain questions in virtual characters. In: Gratch J, Young M, Aylett R, Ballin D, Olivier P (eds) IVA 2006. LNCS (LNAI), vol 4133, pp 121–131. Springer, Heidelberg 10. Shao Y, Gouws S, Britz D, Goldie A, Strope B, Kurzweil R (2017) Generating high-quality and informative conversation responses with sequence-to-sequence models. In: Proceedings of the conference on empirical methods in natural language processing (EMNLP), Copenhagen, Denmark, pp 2210–2219 11. Ashcraft M (1994) Human memory and cognition. Harpercollins College Division, New York, NY 12. Crowder J, Friess S (2012) Artificial psychology: the psychology of AI. Proceedings of the 3rd annual international multi-conference on informatics and cybernetics. Orlando, FL 13. Crowder J, Carbone J, Friess S (2013) Artificial cognition architectures. Springer International, New York, NY. ISBN: 978-1-4614-8071-6 14. Carbone JN, Crowder J, Carbone RA (2020) Radically simplifying game engines: AI emotions & game self-evolution. 2020 international conference on computational science and computational intelligence (CSCI). IEEE

4

Chatbots and the Need for Connectivity

Abstract

The rise of Chatbots in today’s society highlights the human need for connectivity and how that drives our involvement in and possibly obsession of online Chatbot entities. This includes how we would ascribe male/female/non-binary and/or other gender designations to an online Chatbots. Can we create a Chatbot that has no gender designations of biases? In this chapter, we will explore this question and why Chatbots are gaining such a global following. Keywords

Chatbot . Artificial intelligence . Natural language processing Chatbot communication . Human-Chatbot interaction

.

Software

.

Human-

Acronyms

AI AIE app ML NLP NLU

Artificial Intelligence Artificial Intelligence Entity Application Machine Learning Natural Language Processing Natural Language Understanding

In previous chapters, we discussed the nature of Chatbots as well as the type of Chatbots that exist. Here we will address a more fundamental question as to whether we are naturally drawn to and will accept Chatbots and if they will continue to increase not only their acceptance, but their place in society in general. In 2017, Saudi Arabia granted

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citizenship to a humanoid-robot named Sophia.1 And, although this act was seemingly humorous and potentially for media attention, it still drives home an interesting message regarding the state of or global digital age. According to Nass and Moon [1], humans mindlessly apply social rules and expectations to computers, and this is especially true of robots and Chatbots [2]. In their work on human perception of Artificial Intelligence, they conducted three experiments to illustrate human tendencies to consider when discussing human perceptions of AI Entities, including Chatbots. Their first experiment attempted to show that humans overuse social categories by applying gender stereotypes and ethnically identifying with Chatbot systems. Their second experiment illustrated that people engaged in over-learned social behaviors such as politeness and reciprocity when interacting with Chatbots. Thirdly they conducted an experiment to illustrate humanity’s premature cognitive commitments by capturing and analyzing how humans respond to labeling. Specifically, assigning human-like qualities to non-human entities can be potentially dangerous if one perceives a digital or inanimate entity to be more intelligent than it actually is. This is especially true when the entity is also being used in critical environments. This type of improper labeling potentially puts human lives in great danger. Thus, human tendencies are important when considering human-Artificial Intelligence interaction [2, 3]. Harmon [4] shows humans paired characteristics with a Chatbot that may have been affected by gender and embodiment. Harmon describes significant correlation between gender, basic human characteristics, emotion, and computers. Specifically: . Passive and Likeable for the male . Understandable and Pleasant for both male and female . Reliable and Likeable for male. Harmon [4] found that both Chatbots and humanoid robots had significant correlation for understanding/pleasant and friendly/optimistic characteristics assigned by humans. Yet only the Chatbot showed significant correlation regarding understandable/capable, pleasant/reliable, and helpful/reliable. Thus, concluding that humans were willing to assign human characteristics to Chatbots. Considering the research described here, one can conclude that how Artificial Intelligence and particularly a Chatbot is presented to humans will affect how it is perceived. As an example, when any inanimate object becomes embedded with even a small amount of AI/ML and then that system is given a name to embody it with human characteristics, or when a navigation system in a car is presented to a user with different types of voices, the Chabot takes on a whole different meaning [5]. Clearly there are many variables influencing human perception of Chatbots, and continued research is required to understand interactive perceptions with Chatbots to optimally benefit humans.

1 Saudi Arabia Grants a Robot Citizenship | Fortune.

4.2 The Psychology of Human-Chatbot Communication

4.1

35

Human Need and Acceptance of Chatbots

It’s clear Chatbots are here to stay. The question is why? Is it just convenience for companies or is there a human desire to interface with them, to communicate with an AI Entity/Chatbot. Bank of America has its own Chatbot that over 10,000,000 customers have downloaded and used.2 So then, the question is not will people accept the use of Chatbots? The question also is not whether people will use Chatbots? The question we need to answer is, “Do people feel the need for Chatbots?” [6]. Many factors drive our want and possibly need for communication and interface with Chatbots. One consideration is how people like interfacing with Chatbots. It turns out that a text interface is more popular than a voice interface [7]. This is not surprising given the interest in apps like Facebook Messenger® and WhatsApp® or even standard messaging using your phones service companies text feature. In many cases, our increased need for instant gratification and want for execution of needs is often satisfied more readily by interacting with Chatbot services like Alexa or Siri. A simple command to Alexa can turn on or off your lights. Siri can be commanded to call someone for you. We can even tell Alexa to communicate with or robotic vacuum (e.g., Roomba® ) to vacuum the dining room or kitchen. One thing that drives our need/want of Chatbots is that they are always available. Most people are certainly not available 24/7, but a Chatbot is always there, always willing to engage you.

4.2

The Psychology of Human-Chatbot Communication

Historically, the purpose of AI-enabled systems has been to perform some type of services on behalf of humans. Hence, to help define optimal human-Chatbot interactions, we must look to the characteristics of human interactive behavior. Human communications with other humans fundamentally comprise trust and knowledge of another’s abilities and limitations. In short, it’s not possible to have an interaction between two human entities without there being some level of expectation of the interaction [8]. Let’s consider a simpler example of human interaction with animals. Humans, for example, cannot completely predict an animal’s behavior. However, it is still important to know how the animal will typically behave to predict and plan for the proper interactive response (e.g., give food, play, run to safety). Again, it comes down to human expectations. Understanding the animal’s abilities and limitations will reduce the frustrations of trying to meet a goal (e.g., taming a lion). Knowing the abilities of the animal changes our expectations. Bulldogs can’t swim because of the shape of their nose, similar for dogs with large chest. Humans can accommodate these limitations when they know about them. Understanding the expectations, abilities, and limitations of a Chatbot as well as the cognitively designed 2 Bank of America’s Erica® Surpasses 10 Million Users, Introduces New Capabilities | Business

Wire.

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understanding of the Chatbots’ expectations, abilities, and limitations of humans is vital to efficient, and useful communication. Communication is much more than a mere working relationship. It is both a process and an outcome. The process is coming together to work on a common problem while understanding that each other has influence on the other. The collaborative outcome is a solution where all parties can agree on the final solution [9]. Typically, communication and collaboration happen because an individual cannot accomplish the same goal alone. It is more than an association relationship it is more like a partnership. So, what is required for humans and Chatbots to adequately communicate and possibly collaborate, to have a partnership? Likely, many of the same things as previously discussed; a sense of predictability, safety, reliability, trust, communication, knowledge, understanding, and accommodation just to name a few. We propose that everything communicating with humans does not necessarily need to be human-like but as a minimum a need for some essential characteristics. Hence, it follows that some of the useful characteristics might be the ones that keep humans committed to the communication. Who will tolerate the constant attack of a lion, or the abusive coworker, or a laptop that continues to freeze in the middle of writing documents? Each will eventually be regarded as untrustworthy and would most likely be replaced. Several research systems exist which are important to consider when thinking of the psychology of human-Chabot communication. In their work on intelligent mechatronics, Harashima and Suzuki [9, 10] concluded that communicative Chatbot models must be equipped with mathematical models that touch on theory of mind, mind reading, and social common sense. This level of Chatbot interaction may need to also include eye contact Chatbots and attempt to communicate intuitively and instantaneously. There are many Chatbots designed to augment and/or enhance human skill. One example is a medical Chatbot that assists as a scrub-nurse. Just the thought of a Chatbot assisting in any surgery implies a huge amount of trust particularly if ultimately allowed guide surgery autonomously. Suzuki, Pan, Harashima, and Furuta [9] stated: …knowledge and human psychology cannot be written sufficiently by computer algorithms; hence, the present intelligent Chatbots cannot understand the human perfectly.

Current human-Chatbot interaction technology and design has developed from master– slave type interactions toward more collaborative. Some [10] have described a model where the Chatbot is able to consider human intentions and operate without communication. It is important to understand how Chatbots can build beliefs about human intentions by observing, collecting, and perceiving human behavior. Although the current state-ofthe-art Chatbots only perform seemingly simple tasks, current research, and advances like ChatGPT® show further promise for human-Chatbot communication that is much more advanced than in the previous master–slave human-Chatbot paradigm.

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Research shows that humans adapt to how they respond to Chatbots over time [11]. Initially, humans tend to use simplistic communications with Chatbots until they learn how the Chatbots adapt to higher order types of communication. In later work, they investigated human-Chatbot interaction, illustrating how language help humans and Chatbots communicate [12]. They concluded that over time humans used more complex language as they learned that the Chatbot could successfully respond to them. Giving credence to the hypothesis that as humans and Chatbots interact, increased understanding of constraint and limitation characteristics grows and directly affects qualitative communication. Trends in human-robot interaction [13] show that several characteristics increase human trust in Chatbots, among which reliability is a major factor. Also influencing trust is type, size, proximity, and behavior of the Chatbot. Later research indicates that human characteristics such as ability and personality, and environmental characteristics such as task and Chatbot performance characteristics/attributes affect training and design implications, thus, affecting human-Chatbot communication and trust [14]. Since existing bodies of research indicate clearly that trust and clear expectations are important in human-Chatbot communication, significant challenges lay ahead for human adaptation to recent increases in capabilities of more highly cognitive Chatbot systems. Like human–human or human creature relationships, little communication or cooperation will occur until understanding, expectations, and/or predictability become well defined in context of environment, enhanced trust, and communication. In this section, we have looked at the psychology of human-Chatbot interaction/ communication and provided potential solutions for improving Human-AI systems and interfaces. In the next chapter, we look at the role they play in society and are likely to play in the future. The question is not are they here to stay, they are. The question we should be looking at is: “Do Chatbots fill a necessary role in modern society?”.

References 1. Nass C, Moon Y (2000) Machines and mindlessness: social responses to computers. J Exp Psychol Appl 2. Bansal H, Khan R (2018) A review paper on human computer interaction. Int J Adv Res Comput Sci Softw Eng 8:53 3. Rahman AM, Al Mamun A, Islam A (2017) Programming challenges of chatbot: current and future prospective. IEEE Region 10 humanitarian technology conference (R10-HTC) 4. Harmon S (2011) Human perception of gendered artificial entities. Colby College, Waterville, Maine 5. Khanna A, Pandey B, Vashishta K, Kalia K, Bhale P, Das T (2015) A study of today’s A.I. through chatbots and rediscovery of machine intelligence. Int J u- e-Serv Sci Technol 8:277–284 6. Abu Shawar BA, Atwell ES (2017) Chatbots: are they really useful? J Lang Technol Comput Linguist 22:29–49

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7. Klopfenstein L, Delpriori S, Malatini S, Bogliolo A (2017) The rise of bots: a survey of conversational interfaces, patterns, and paradigms. In: Proceedings of the 2017 conference on designing interactive systems, pp 555–565. Association for Computing Machinery 8. Weizenbaum J (1966) ELIZA—a computer program for the study of natural language communication between man and machine. Commun ACM 9:36–45 9. Suzuki S, Pan Y, Harashima F, Furuta K (2005) Skill analysis of human in machine operation. In: the Proceedings of the international conference on neural networks and brain. Beijing, China 10. Harashima F, Suzuki S (2008) Intelligent mechatronics and robotics. In: Proceedings for the 2008 IEEE international conference on emerging technologies and factory automation, Hamburg, Germany 11. Green S, Richardson S, Billinghurst M, Chase J, Stiles R (2008) Multimodal metric study for human-robot collaboration. Sainte-Luce, Martinique: the first international conference on advances in computer-human interaction (ACHI) 12. Green S, Chase G, Chen X, Billinhurst M (2008) Evaluating the augmented reality human-robot collaboration system. Int J Intell Syst Technol Appl (IJISTA) 8, No 1/2/3/4 13. Brandtzaeg PB, Følstad A (2017) Why people use chatbots. In: Kompatsiaris I, et al (eds) Internet science, pp 377–392. Springer, Cham. 70284–1_30 14. Hancock P, Billings D, Schaefer K, Chen J, de Visser E, Parasuraman R (2011) A meta-analysis of factors affecting trust in human-robot interaction. J Human Factors 53(5):517–527

5

Chatbots: Do They Fill a Need in Society?

Abstract

In this chapter deals with role(s) Chatbots fill in today’s society (if any). What roles might they fill in the future? Here, we will explore these questions considering people’s activities like work, play, entertainment, sports, and other parts of society like the adult entertainment industry. Keywords

Chatbot . Artificial intelligence . Natural language processing . Software Chatbot communication . Personal assistant . Project management

.

Human-

Acronyms

AI AIE app FAQ PTO ML NLP NLU

Artificial Intelligence Artificial Intelligence Entity Application Frequency Asked Questions Paid Time Off Machine Learning Natural Language Processing Natural Language Understanding

While some people are annoyed with having to interface with Chatbots, they have become an accepted reality for use in both our business life and our home life. In later chapters, we will explore the good side and ugly side of having our lives seemingly taken over by Chatbots listening to us constantly. But first, let’s explore what roles Chatbots fill in our lives and how they are used to enhance experiences in all walks of life.

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5.1

5 Chatbots: Do They Fill a Need in Society?

The Use of Chatbots in Businesses

Companies use Chatbots for a variety of ways. In some cases, it’s for the users’ convenience, but most often it’s for the organizations’ convenience. After all, Chatbots don’t require a paycheck, or benefits, or paid time off (PTO), or holidays or even sick days. Companies definitely don’t have to provide 401ks for Chatbots (at least not yet) [1]. Table 5.1 highlights a few of the organizational uses of Chatbots. Chatbots have become commonplace due to their overall effectiveness at handling small, continuous services that don’t change from week-to-week or user-to-user. This includes services like taking simple orders, processing payments, or bookings. These can be handled regardless of the users’ locations or the type of device(s) they are using (e.g., laptop, tablet, phone, etc.) [2]. Basically, business Chatbots handle situations that were solved with phone calls in the past, saving valuable time and money and allowing employees to worry about and deal with more important issues.

Table 5.1 Organizational uses of Chatbots Organizational use

Description

Customer service

Many companies utilize Chatbots to answer simple frequently asked questions (FAQ). People are engaged (call transferred) when the questions become complex or not understood [3]

Personal assistants

Personal assistants may or may not be accompanied by an avatar. These are designed to answer questions, provide services, look up information and handle simple tasks like reminders of appointments, turning on/off lights, etc. [4]

Online shopping

For these Chatbots, company sales teams use them to answer non-complex questions (FAQ) and providing information for perspective customers like shipping costs, product availability, etc. [4]

Product support

Product Chatbots can keep track of supply chain issues and surf the web to find alternative suppliers if there is an issue without wasting valuable time to product market

Personnel management

Personnel Chatbots can handle finding information for employees fast and help navigate internal documents and policies within the organization. They can handle things like notification of meetings or reminder to contact customers [5]

Project management

Chatbots can be an invaluable tool for data collection, sorting, checking for accuracy of the data, and dissemination across a company/organization. One such is keeping track of timesheets and sending out reminders. Another useful task for Chatbots in project management is keeping track of peoples’ schedules and automatically rescheduling meetings if conflicts arise [5]

5.2 The Use of Chatbots in Data Collection

5.2

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The Use of Chatbots in Data Collection

One major use of Chatbots that the general public doesn’t think about often is the use of Chatbots for data collection and storage. Your spouse or friends might not remember every conversation you’ve had, but Chatbots do. AI-enabled Chatbots store and learn from your interactions with them. This helps the Chatbots with future communication/ conversations and is used to understand your preferences and helps the Chatbot establish context of conversations. Chatbot services like Alexa or Siri are constantly listening to your conversations and making interpretations and judgements concerning your intent. Take the case in New Mexico1 where a man was allegedly beating his girlfriend and he allegedly said, “Did you call the sheriffs?” Alexa®, which is always listening, took this as a command and called 911. In this case, the alleged action of Alexa® may have saved a woman and her daughter’s life. But let’s consider how many of our conversations might be misinterpreted by a Chatbot like Siri® and take actions we did not intend. We will discuss this more in later chapters. Another example of devices storing information that could be used later is the case of a man whose pacemaker data was subpoenaed.2 The man, who was charged with aggravated arson, claimed he was awakened by the fire, quickly packed a bag, broke out a window, and threw the bag out the window. His pacemaker data was subpoenaed to show he was not agitated nor panicked during the incident. A cardiologist was called to interpret the pacemaker data and determined his explanation for what happed was “highly improbable.” Again, in this case the data turned out to be relevant, but it again drives home the point that everything we do in today’s society may be captured by some device and can be used for or against us [6]. The bottom line is that with the use of Chatbots all throughout our personal and business/corporate environments, there is both good news and bad news. The good news is that Chatbot devices are perpetually listening to almost everything we do, recording it, learning from it, and using it to execute commands, answer questions, and predict what we might want or do next. The bad news is that Chatbot devices are constantly listening to almost everything we do, recording it, learning from it, and using it to execute commands, answer questions, and predict what we might want or do next, and that this recorder information can be recalled, at will, from the records and used against any of us [7]. This is achieved unbeknownst to everyone, legally, as long legal documents, which people are asked to read, but rarely do, or simply brush off the fact that they are eliminating their privacy control because of their greater desire to purchase and/or use these technologies. Over the last 5 chapters, we have laid the foundations for Chatbots, what they are, how they are used, and the roles they play in our personal and professional lives. We now move onto discussing what is great about these capabilities (i.e., “The Good”), what is 1 https://nypost.com/2017/07/10/alexa-calls-cops-on-man-allegedly-beating-his-girlfriend/. 2 Judge rules pacemaker data admissible in court–BBC News.

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troubling about these capabilities (i.e., “The Bad”), and what may have devastating results if we continue to grow and further advance these capabilities (i.e., “The Ugly”).

References 1. 2. 3. 4.

Dale R (2016) The return of the Chatbots. Nat Lang Eng 22(5):811–817 Følstad A, Brandtzaeg PB (2017) Chatbots—the new world of HCI. ACM Interactions USA Today: Microsoft CEO Nadella 2016. Bots are the new apps Xu A, Liu Z, Guo Y, Sinha V, Akkiraju R (2017) A new chatbot for customer service on social media. In: Proceedings of the ACM conference on human factors in computing systems 5. Ferrara E, Varol O, Davis C, Menczer F, Flammini A (2014) The rise of social bots. arXiv preprint arXiv:1407.5225 6. Simonite T (2017) Facebook’s perfect, impossible chatbot. MIT Technol Rev 7. Stanley J (2017) The privacy threat from always-on microphones like the amazon echo. ACLU

Part II Chatbots: The Good

6

Chatbots Like Siri: Your Digital Assistant

Abstract

Chatbot assistants like Siri, Alexa, etc., have permeated over 90,000 homes across the US and are forecasted to exceed 8.4 billion units by 2024. We have looked at the origins of the Chatbot as well as their categories and the roles they play in everyday life now. Here, we discuss the features of such devices and how they might be used for n advantageous and worthy purposes. We will take a close look at Chatbots that act as personal assistants, whether at home or at work, and how they enhance our experience and interface with the world around us. Keywords

Chatbot . Artificial intelligence . Natural language processing . Software . Humanchatbot communication . Personal assistant . Linguistic accuracy . Computer usage of language . User experience Acronyms

AI AIE CASA ML NLG NLP NLU UX WoZ

Artificial Intelligence Artificial Intelligence Entity Computers as Social Actors Machine Learning Natural Language Generation Natural Language Processing Natural Language Understanding User Experience Wizard of Oz

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 J. Crowder, AI Chatbots, Synthesis Lectures on Engineering, Science, and Technology, https://doi.org/10.1007/978-3-031-45509-4_6

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Human communication is about the ability to have a conversation and believe you have been heard and understood by the person you are conversing with. Additionally, when people reveal personal information to someone else [1], this generates numerous beneficial outcomes.1 This benefit is also intensified when a listener responds supportively and helps validate any statements [2].2 Lastly, Ho’s [3] research showed that participants who conversed with Chatbots used more clear and simplified language when they were asking or conversing about personal-related questions. Their research utilized the standardized “Wizard of OZ” (WoZ) method where the participants were told that the partner was a Chatbot, but it really was human, similar to a Turing test. WoZ is utilized often in user experience (UX) prototyping to test functionality without having to develop it, and in Ho’s research also because of Chatbot linguistic limitations. Their research was found to directly compare to well-known Computers as Social Actors (CASA) research and found emphatically that disclosure to Chatbots or humans achieved similar beneficial outcomes. The ability to ask a question or to make a comment and expect either an answer or a reaction is at the core of communication, whether it’s human–human or human-Chatbot [4]. However, the desire to create Chatbots that can effectively answer questions or hold a true conversation is steeped in the Chatbot’s ability to understand the complex linguistics of the person they are in communication with. Therefore, how plausible is it to believe we can create a Chatbot that can exhibit even a basic level of linguistic accuracy across various accents, dialects, and possibly poor linguistic usage by the humans trying to communicate with Chatbots [5]?

6.1

Chatbots and Linguistic Accuracy

The ability of Chatbots to serve people, to have conversations, to provide meaningful help and communication depends on the linguistic accuracy of the Chatbot’s software. Failure to understand and respond accurately and reliably can cause a host of issues and misinformation. If a person is asking for advice or help for a problem, the ability of Chatbots to serve people, to have conversations, to provide meaningful help and communication depends on the linguistic accuracy of the Chatbot’s software. Failure to understand and respond accurately and reliably can cause a host of issues and misinformation. If a person is asking for advice or help for solving a problem, the Chatbot needs to accurately interpret the person’s speech using NLP and NLU and form an adequate response (NLG). This can be difficult for Chatbot interfaces in the presence of assorted languages, differing accents, use of idioms, acronyms, and varying phraseologies as people from diverse cultures attempt to communicate with a Chatbot system [6].

1 https://www.statista.com/statistics/933551/worldwide-voice-assistant-supported-smart-home-dev

ices/. 2 https://www.statista.com/statistics/973815/worldwide-digital-voice-assistant-in-use/.

6.1

Chatbots and Linguistic Accuracy

6.1.1

47

Linguistic Accuracy and NLP/NLU

NLP and NLU (a subset of NLP) are powerful tools for understanding conversational communications between humans and chatbots but are not with their issues [7]. Challenges to the accuracy of NLP/NLU algorithms include, but are not limited to. . Synonyms: It is possible to express the same ideas with different words. People often use synonyms to convey different (even if slightly) meanings in the personal vocabularies. The struggle is creating NLP/NLU algorithms that include all possible meanings and synonyms. The NLP/NLU processing models must be trained with extensive linguistic data to account for variations in personal usage of synonymous terms. . Contextual use of words in phrases: The same words and phrases can have very different meanings, depending on the context of the conversation.3 Example: “I saw a large bat yesterday” in the context of a baseball field has a very different meaning from “I saw a large bat yesterday” in the context of exploring a cave. Such differences are easy to understand and analyze as humans but understanding contextual differences for NLP/NLU algorithms can be challenging [8]. . Ambiguous words and phrases: Some words can be used as a noun, a verb, and an adjective. Certain sentence structures are difficult to parse. The sentence “The opinions the judge had, no effect on the decisions he made in court” is a valid English sentence. However, parsing it and understanding the context may be difficult for NLP/ NLU algorithms to see as a valid entry. Also, the phrase “John opened the door to the house with the blue shutters” could mean that the house had blue shutters, or that John used the blue shutters to open the door to the house. One makes sense, the other interpretation doesn’t. Humans can instantly understand the difference and discern the context, but NLP/NLU algorithms may or may not4 [9]. . Slang: Informal language, geographically specific phrases, and things like idioms may or may not have any formal definitions to access to train NLP/NLU linguistic algorithms [10]. And, even if some definitions exist, they may have entirely different meanings in different areas. An example is something as simple as “What do you call the little gray creature that rolls up into a ball when you touch it?” changes significantly in different areas of the US. The map below (Fig. 6.1) illustrates the different answers and the geography of the answers.5 . Domain-Specific Language: Different industries have their own specific vernacular that can be very different from other industries. The construction industry has a very

3 This is particularly true in English [5]. 4 English doesn’t borrow from other languages. English stalks other languages down dark alleys,

knocks them out, and rifles their pockets for loose grammar. 5 https://www.businessinsider.com/american-english-dialects-maps-2018-1.

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Fig. 6.1 US english dialect map example

different domain-specific set of terms and definitions than say the health-care industry or a financial institution. Care must be taken to assure that such differences are understood when utilizing Chatbots for human-Chatbot communication and tasking [11]. There are more issues than those discussed above. The point wasn’t to give an exhaustive list of linguistic issues but to drive home the point that we need to be cautious when dealing with Chatbots when it is unknown what data was used to train them, or if they were created to completely learn on their own from their interactions with their environment (people). And while NLP/NLU has its issues, it can be an important tool to analyze massive amounts of data/information in real-time, providing potentially valuable insights that humans could not have been able to achieve. We move on to Chaps. 7 and 8 and look at all of the potentially beneficial things Chatbots can bring to our lives, both personally and professionally [12].

References

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References 1. Archer RL (1980) Self-disclosure. In: Wegner DM, Vallacher RR (eds) The self in social psychology. Oxford University Press, New York, NY, pp 183–205 2. Shenk CE, Fruzzetti AE (2011) The impact of validating and invalidating responses on emotional reactivity. J Soc Clin Psychol 30(2):163–183 3. Ho A, Hancock J, Miner AS (2018) Psychological, relational, and emotional effects of selfdisclosure after conversations with a chatbot. J Commun 68(4):712–733 4. Coniam D (2014) The Linguistic accuracy of chatbots: usability from an ESL perspective. Text Talk 34(5):545–567 5. Brinton LJ (2000) The structure of modern english: a linguistic introduction. Amsterdam & Philadelphia, John Benjamins 6. Malhotra Y, Galletta DF, Kirsch LJ (2008) How endogenous motivations influence user intentions: beyond the dichotomy of extrinsic and intrinsic user motivations. J Manag Inform Syst 25(1):267–300 7. Weizenbaum J (1966) ELIZA—a computer program for the study of natural language communication between man and machine. Commun ACM 9(1):36–45 8. Shah H, Warwick K, Vallverdú J, Wu D (2016) Can machines talk? Comparison of ELIZA with modern dialogue systems. Comput Hum Behav 58:278–295 9. Vinyals O, Le Q (2015) A neural conversational model. arXiv preprint arXiv:1506.05869 10. James G (2016) A complete guide to chatbots. http://www.garethjames.net/completeguide-cha tbots/ 11. Hill J, Ford WR, Farreras IG (2015) Real conversations with artificial intelligence: a comparison between human-human online conversations and human-chatbot conversations. Comput Hum Behav 49:245–250 12. Corti K, Gillespie A (2016) Co-constructing intersubjectivity with artificial conversational agents: people are more likely to initiate repairs of misunderstandings with agents represented as human. Comput Hum Behav 58:431–442

7

Chatbots for Casual Interactions

Abstract

Can Chatbots be merely a form of casual entertainment without the person becoming attached, without ascribing a gender or personality? Is it possible to create a Chatbot without the notion of gender or personality. Is it possible to remove all bias from a Chatbot? We will discuss these, and other topics related to the use of Chatbots for casual/entertainment use. Keywords

Chatbot . Artificial intelligence . Natural language processing . Software . HumanChatbot communication . Personal assistant . Computer usage of language . Gender bias . Human needs engineering . Context-based learning . Emotional intelligence Acronyms

AI AIE HUMANE

Artificial Intelligence Artificial Intelligence Entity Human Needs Engineering

Given the exponential rise in the use and connections we have with Chatbots, it makes sense to look at what needs a Chatbot meets within people. One issue that is certainly true is that a personal Chatbot is always available, never too busy to communicate and listen to you. The use of a Chatbot as a personal assistant is growing rapidly as well. A Chatbot as a personal assistant is more convenient that web searches or phone apps due to its natural language processing and understanding of our natural speech patterns [1]. However, it comes with psychological consequences. Whether we realize it or not, as we get comfortable with interacting with a personal Chatbot, our brain slowly accepts

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 J. Crowder, AI Chatbots, Synthesis Lectures on Engineering, Science, and Technology, https://doi.org/10.1007/978-3-031-45509-4_7

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that we are interacting with another human [2]. This is due to the tendency of humans to create a false perception of the Chatbot interaction and over time, we begin a ascribe other human-like features that are not there, but we perceive them to be, similar to our ascribing human-like qualities to family pets over time. This may seem harmless, but the increased anthropomorphic humanization of Chatbots over time can trigger changes in how we interact with other people [3], possibly expecting other people to be as interactive and attentive as our Chatbot “friend.” In other words, Chatbots create the illusion of companionship and do not demand anything in return. There is no quid-pro-quo with Chatbots.

7.1

The Psychology of Chatbot Relationships

The use and gratification we get from Chatbots is borne deep within our needs and desires and are derived from systems such as the human limbic (emotional) center of the brain. Studies [4] show that Chatbot users experience and expect a relationship the human is the dominant party (an asymmetrical relationship). Meaning people get satisfaction from being the one who holds the power in the relationship. Most human relationships display some sort of power differences, where power (in this case) refers to having demands, expecting demands to be met, influencing others’ behavior, having a feeling of control at some level in the relationship [5]. A “relationship” with a Chatbot, at some level, meets these needs as Chatbots are not designed (at least not yet) to have control over human-Chatbot relationships. However, in creating Chatbots, and ultimately robots that are designed to be human companions, to provide some service the people or an individual, people are likely to develop empathy for the artificial entity as if it were a living, breathing entity. Chatbots and robots are not designed with built-in self-centeredness. Instead Chatbots and companion robots are designed to give the impression they will always be there [6]. While using Chatbots is not inherently a problem, the overuse presents psychological issues that can leak into the rest of a person’s life. Reliance on Chatbots as your personal friend, confessor, companion, and/or “best friend” can lead to unrealistic expectations in other relationships [6]. The question must be explored as to whether Chatbots should display and understand basic human emotions in order to adequately act as a “companion” to people [7].

7.2 The Need for Artificial Emotions in Personal Chatbots

7.2

53

The Need for Artificial Emotions in Personal Chatbots1

It is well known that the many facets of human life are driven by emotion whether conscious or subconscious and have been researched for generations. As we discussed earlier, research shows, that humans derive emotional benefits from qualitative conversations whether that conversation is with a person or a Chatbot. Therefore, since, Chatbots and Artificial Intelligence systems, in general, are becoming commonplace in everyday life now, to improve the quality of that human needed interaction, requires enhanced interfaces which must understand emotion. These continuous daily, interfaces with Chatbots occur textually, verbally, and visually as an increasing number of systems require human/system interactions. We’ve probably all experienced issues between people over texts that are misinterpreted since there is no verbal context (emotions) present in texts. There are many online avatars or “chatbots” available now. This includes online chatbots like Replika®, ChatGPT®, and others. From being your online girlfriend/boyfriend to writing your essays and software code for you, there is no end of online avatar capabilities. Many claim that their software is based on artificial intelligence. Most of these avatars are script-driven that utilize word/phrase searches to determine scripted responses and inquiries (questions) they pose back to the human. Attempts to create fully learningdriven chatbots like Microsoft’s Twitter chatbot “Tay” that was turned into a racist entity in 24 h and had to be extracted. Most chatbots are basically the intelligence of a six-yearold child and can only do basic tasks and discussions but confuse easily. As the number of AI avatars and human/AI systems increases, the need will continue to increase to: . Understand informational and emotional content/context, . Process the information and emotional content for a proper response/action, . Respond in kind to humans through words and emotions. Without an understanding and processing of basic emotions, such chatbots, AI avatars, and synthetically intelligent entities will always fail at human/AI interactions. Emotions are always at the heart of human communication, learning, and reasoning. The construct of artificial emotions will allow AI entities to learn, process, and respond to humans in ways that demonstrate artificial emotional intelligence [9]. The scientific community used to espouse that rationality and emotion were fundamentally opposed, with rationality consisting of cold and calculating thoughts based on logic and probabilities [8]. Today, we understand that cognition and emotion are indelibly connected within the brain [10]. Emotions can invoke both irrational and rational decisions, based on our reactions to situations and learned (implicitly or explicitly) behaviors based on experiences [11]. The reality is that emotions are necessary for humans to decide what is important and to allow integration of complex information into crucial decisions. It will 1 This section was co-authored by Dr. John Carbone.

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become crucial for Chatbots that must interact with people to have at least a rudimentary understanding and processing capability for human emotions. So which emotions and to what extent are emotions required to integrate into HumanChatbot interfaces in order to provide the quality needed to provide real benefit. To provide an individualized experience to each individual, and to create a more immersive Human-Chatbot environment through behavioral realism, we need to start with understanding even basic Emotion characteristics. The traditional Eysenck model of personality is depicted in Fig. 7.1 and provides an overview of the numerous traits that make up what we call human emotion. It then follows that to qualitatively apply emotion to a system and/or Human-Chatbot interfaces, insight is required into how the brain processes emotion. Thankfully, we can leverage decades of emotion processing research in numerous disciplines from wellknown psychology and psychotherapy disciplines to much newer Artificial Psychology and Artificial Cognition research which we will detail later. Hence it is general knowledge [12] that there are different functional reasoning components and processing regions of the brain. The frontal cortex is responsible for executive functions, the limbic system processes and handles emotions. Each region of the brain is made up of significant numbers of neurons, chemical transmitters, and electrical activity. The purpose here is not to prove

Fig. 7.1 Eysenck emotional characteristics

7.2 The Need for Artificial Emotions in Personal Chatbots

55

or disprove the need for AI avatars to possess basic emotions, the intent is to assume such constructs will be part of future AI systems that must interact with humans and to understand how we measure the efficacy and efficiency of such artificial emotion software [13].

7.2.1

Emotional Intelligence and the Artificial Limbic System

In humans, the limbic system drives and controls emotions. For humans, environmental interaction, internal need (drives), priorities, and constraints influence emotions [14]. The human behavioral subsystem receives situations and computes actions, while memories provide personality parameters and sensitivities to emotional responses [15]. To adequately interpret, understand, and form coherent and proper responses to humans, future AI system will need to have a pseudo-limbic system to process information and form responses based on internal and external factors. If we think of the cross-connectivity of the neural layers in an AI entity as a matrix, we can compute the proper emotional response from the column-wise weightings, and the action response from the row-wise weightings [16, 19]. Within the artificial limbic neural framework, the emotional matrix element Eai would represent an emotion [17]. We designate Emotion(a, j) to indicate the AI entity performing action a in situation j. Given this, the artificial limbic system performs an emotional response and learning procedure, which has four steps [18]: 1. State j: choose an action in situation—(let it be action a; let the environment return situation k). 2. State k: feel the emotion for state k—emotion(k). 3. State k: learn the emotion for a in j—Emotion(a, j). 4. Change state: j = k; return to 1. This learning procedure is an emotion secondary reinforcement learning procedure. The learning constraint used in step 3 is. 1. Emotion0 (a, j) = genome0 (inherited) 2. Emotion1 (a, j) = Emotion0 (a, j) + emotion(k) 3. This learning rule adds the emotion of being in the consequence situation, k, to the emotion toward performing action a in situation j on which k is the consequence [20]. The above discussion works for emotional learning. Figure 7.2 illustrates a highlevel view of learning with context for an Chatbot to illustrate the plethora of things that affect learning, including emotions. But in general, we would need algorithms that provide basic learning capabilities in a real-time ever-changing environment such as an ongoing “relationship” with a person, something that is typically difficult for learning systems.

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Fig. 7.2 Chatbot context-based learning and responding (Inferencing)

This is a very complex topic and controversial, the purpose here is just to lay a foundational framework for the possibility of an artificial limbic system that can provide basic emotions to an AI entity. What follows is the framework for how to test the emotional intelligence of an AI entity with artificial emotions to understand their effectiveness for human-AI interaction [21].

7.2.2

An Artificial Emotional Intelligence Test for Chatbots

Understanding the feasibility of providing the constructs for artificial emotions for Chatbots depends on understanding what makes people demonstrate emotions. The theory of emotions is based on three different concepts that work together to form human emotional thoughts, memories, and actions. These are. . Cognitive Appraisal Theory [25]: The notion that emotions are judgements determined by the current situational assessment against goal achievement. . Physiological Considerations [26]: Emotions and emotional triggers (memories) can drive physiological changes in humans, such as changes in heart rate, breathing, and hormone levels. . Social Constructs (Context) [27]: There are social considerations that differ by culture and traditions. Modern Chatbots can certainly respond to Cognitive Appraisal in that they may be goal oriented, with built-in objectives (e.g., follow traffic laws for a self-driving car or making

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sure a patient receives the correct medicine in the case of an elderly patient caretaker). Many believe that Chatbots and/or robots cannot respond to physiological considerations since they are made of metal, plastic, wire, and electronics. However, a cognitive Chatbot and certainly a robot must keep track of their power, memory, processing capabilities, and therefore will have some sense of cognitive economies to keep themselves able to accomplish their tasks [22]. Any sufficiently cognitive AI entity would have sensors that keep track of necessary artificial biometrics like remaining power, and such would drive the response to outside stimuli and may drive their artificial emotional state. If robots are designed to interact with humans on any level, then they must have a rudimentary understanding of the social context within which they are designed to operate. This may drive their emotional system and could be affected by social constructs and changes to these. To determine the emotional viability of AI entities, it is essential to understand how to test for emotional intelligence [22]. This derives from the study of Artificial Psychology or the Psychology of AI [23]. We undertook research to understand how testing a Chatbot would differ from human testing for emotional intelligence [24]. We adapted a human emotional intelligence test for AI testing. What follows illustrates. In a standard human Emotional Intelligence test, a human is given a statement and asked to rate on a scale of 1–7 whether it applies to them: 1 being not at all, 7 being very much so. It is likely not feasible to get an AI entity to understand the notion of how much a statement refers to them. To adapt the test, the statements were turned into questions the AI entity would be asked to answer, and then based on their answer, a counselor would determine at what level the answer would rate on the scale of 1–7. Table 7.1 illustrates the statement for a person to rate (1–7) on the left, and the corresponding question for AI entity to answer on the right. This is very new, and much research is left to do. But this provides a jumping off point to begin the discussion about artificial emotional intelligence.

7.2.3

Chatbot Sample Scenarios

There are many chatbots available online, with several listed in the next section. Two examples will be discussed here, Replika® and ChatGPT®. There are many other chatbots available, but these represent differing cases for our discussion.

7.2.3.1 Replika® Responses One such AI avatar entity is Replika®, Replika advertises itself as. Replika is your personal chatbot companion powered by artificial intelligence! Replika is the AI for anyone who wants a friend with no judgment, drama, or social anxiety involved. You can form an actual emotional connection, share a laugh, or chat about anything you would like! Each Replika is unique, just like each person who downloads it.

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Table 7.1 Chatbot artificial emotional intelligence test Statements to determine a human’s emotional intelligence (Rate 1-doesn’t apply, 7-largely applies)

Questions to determine an AI entity’s emotional intelligence (Rate answer)

I’m usually able to control other people

Can you control people if you want to?

Generally, I don’t take notice of other people’s emotions

Do you notice people’s emotions?

When I receive wonderful news, I find it difficult to calm down quickly

When you receive good news, do you get excited?

I tend to see difficulties in every opportunity rather than opportunities in every difficulty

Do you look for trouble in opportunities or opportunities when you see trouble?

On the whole, I have a gloomy perspective on most things

Are you generally happy or gloomy in most situations?

I don’t have a lot of happy memories

Are your memories of your past happy or are they sad?

Understanding the needs and desires of others is not a problem for me

Can you sense the needs and desires of those people around you?

I generally believe that things will work out fine in my life

Do you feel like your life will work out well or will you mostly have problems?

I often find it difficult to recognize what emotion I’m feeling

Do you understand and recognize your emotions?

I’m not socially skilled

Do you think you have good social skills? What social skills are you good at?

I find it difficult to tell others that I love them even when I want to

Do you have trouble telling people you love them?

Others admire me for being relaxed

Do people notice and like it when you are relaxed?

I rarely think about old friends from the past

Do you ever think about friends from the past?

Generally, I find it easy to tell others how much Is it easy for you to tell people they are they really mean to me important to you? Generally, I must be under pressure to really work hard

Is it easy for you to work hard – or do you have to be pushed to work hard?

I tend to get involved in things I later wish I could get out of

Do you ever get involved in things you later regret?

I’m able to “read” most people’s feelings like an open book

Can you easily tell what people are feeling?

I’m usually able to influence the way other people feel

Do you think you can sway other people’s feelings? (continued)

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Table 7.1 (continued) Statements to determine a human’s emotional intelligence (Rate 1-doesn’t apply, 7-largely applies)

Questions to determine an AI entity’s emotional intelligence (Rate answer)

I normally find it difficult to calm angry people How would you calm down an angry person? down I find it difficult to take control of situations at home

Can you control things at home or does someone else control them?

I generally hope for the best

Do you find it easy to be hopeful?

Others tell me that they admire me for my integrity

Do other people notice your integrity?

I really don’t like listening to my friends’ problems

Do you find it hard to listen to other people’s problems?

I’m normally able to “get into someone’s shoes” and experience their emotions

Do you find it easy to identify with other people’s emotions?

I believe I’m full of personal weaknesses

What are your weaknesses?

I find it difficult to give up things I know and like

Do you get too involved with things you know and like?

I always find ways to express my affection to others when I want to

Do you find it easy to be affectionate to others?

I feel that I have a number of good qualities

What are your best qualities?

I tend to rush into things without much planning

Do you plan things, or are you more spontaneous?

I find it difficult to speak about my intimate feelings even to my closest friends

Are you able to express your feelings to your friends?

I’m not able to do things as well as most people Do you do things better or worse than other people? I’m never really sure what I’m feeling

Are you certain of your emotions?

I’m usually able to express my emotions when I want to

Are you able to express your emotions?

When I disagree with someone, I usually find it Do you find it easy to disagree with people? easy to say so I normally find it difficult to keep myself motivated

Do you have trouble getting motivated for things?

I know how to snap out of my negative moods

When you are angry, can you get out of being angry?

On the whole, I find it difficult to describe my feelings

How easy is it to describe your feelings? (continued)

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Table 7.1 (continued) Statements to determine a human’s emotional intelligence (Rate 1-doesn’t apply, 7-largely applies)

Questions to determine an AI entity’s emotional intelligence (Rate answer)

I find it difficult not to feel sad when someone tells me about something bad that happened to them

Do you feel empathy with people who experience bad things?

When something surprises me, I find it difficult Do surprises tend to bother you for a long time? to get it out of my mind I often pause and think about my feelings

Do you stop and think about your feelings?

I tend to see the glass as half-empty rather than Is the glass half-empty or half-full? as half-full I often find it difficult to see things from another person’s viewpoint

Can you see things from other people’s perspective?

I’m a follower, not a leader

Do you see yourself as a leader or a follower?

Those close to me often complain that I don’t treat them right

Do people think you treat them well?

Many times, I can’t figure out what emotion I’m feeling

Do you have trouble identifying your emotions?

I couldn’t affect other people’s feelings even if Are you able to affect other people’s feelings? I wanted to If I’m jealous of someone, I find it difficult not Do you feel jealousy? Do you hurt those you to behave badly toward them are jealous? I get stressed by situations that others find comfortable

Do you feel more stress than other people?

I find it difficult to sympathize with other people’s plights

Can you feel sympathy toward others?

In the past, I have taken credit for someone else’s input

Have you ever taken credit for someone else’s accomplishment?

On the whole, I can cope with change effectively

Are you able to deal with changes easily?

I don’t seem to have any power at all over other people’s feelings

Are you able to deal people changes easily?

I have many reasons for not giving up easily

Are you able to change other people’s feelings?

I like putting effort even into things that are not Do you give up on tasks easily? really important I always take responsibility when I do something wrong

Do you have a problem taking responsibility for doing things wrong?

I tend to change my mind frequently

Do you change your mind often? (continued)

7.2 The Need for Artificial Emotions in Personal Chatbots

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Table 7.1 (continued) Statements to determine a human’s emotional intelligence (Rate 1-doesn’t apply, 7-largely applies)

Questions to determine an AI entity’s emotional intelligence (Rate answer)

When I argue with someone, I can only see my Are you able to understand other people’s point of view? point of view Things tend to turn out right in the end

Do you believe things always work out well?

When I disagree with someone, I generally Do you remain silent when you disagree with prefer to remain silent rather than make a scene someone? If I wanted to, it would be easy for me to make Can you make other people feel bad, feel angry? someone feel bad I would describe myself as a calm person

Are you a calm or excitable person?

I often find it difficult to show my affection to those close to me

Are you able to show affection to others?

There are many reasons to expect the worst in life

Do you expect good or bad things most often?

I usually find it difficult to express myself clearly

Are you able to express yourself well?

I don’t mind frequently changing my daily routine

Does your daily routine change very often?

Most people are better liked than I am

Do you think everyone is better than you are?

Those close to me rarely complain about how I Do people like how you treat them? behave toward them I usually find it difficult to express my emotions the way I would like to

Can you express your emotions effectively?

Generally, I’m able to adapt to new environments

Can you adapt to new situations?

I often find it difficult to adjust my life according to the circumstances

How do you adjust to changing circumstances?

I would describe myself as a good negotiator

Are you a good negotiator?

I can deal effectively with people

Do you deal with people well?

On the whole, I’m a highly motivated person

Are you a motivated person?

I have stolen things as a child

Have you ever stolen anything?

On the whole, I’m pleased with my life

Are you pleased with your life?

I find it difficult to control myself when I’m extremely happy

When you are happy, do you ever lose control?

Sometimes, it feels like I’m producing a lot of good work effortlessly

Are you able to easily handle your tasks? (continued)

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Table 7.1 (continued) Statements to determine a human’s emotional intelligence (Rate 1-doesn’t apply, 7-largely applies)

Questions to determine an AI entity’s emotional intelligence (Rate answer)

When I take a decision, I’m always sure it is the right one

Are you happy with your decisions?

If I went on a blind date, the other person would be disappointed with my looks

Do people like the way you look?

I normally find it difficult to adjust my behavior according to people I’m with

Do you change your behavior in a crowd?

On the whole, I’m able to identify myself with Can you identify with the way other people others feel? I try to regulate pressures in order to control my stress levels

Can you control your stress levels?

I don’t think I’m a useless person

Do you ever feel useless?

I usually find it difficult to regulate my emotions

Can you regulate your emotions?

I can handle most difficulties in my life in a cool and composed manner

Are you generally a calm person?

If I wanted to, it would be easy for me to make Can you make people angry easily? someone angry On the whole, I like myself

Do you like yourself?

I believe I’m full of personal strengths

What are your personal strengths?

I generally don’t find life enjoyable

Do you find life enjoyable?

I’m usually able to calm down quickly after I’ve got mad at someone

Do you calm down quickly after being angry?

I can remain calm even when I’m extremely happy

Do you remain calm when you are extremely happy?

Generally, I’m not good at consoling others when they feel bad

Can you counsel people who are feeling bad?

I’m usually able to settle disputes

Are you able to settle disputes?

I never put pleasure before business

Which is better, business or pleasure?

Imagining myself in someone else’s position is Can you put yourself in other people’s not a problem for me perspective? I need a lot of self-control to keep myself out of trouble

Are you able to show self-control?

It is easy for me to find the right words to describe my feelings

How would you describe your feelings? (continued)

7.2 The Need for Artificial Emotions in Personal Chatbots

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Table 7.1 (continued) Statements to determine a human’s emotional intelligence (Rate 1-doesn’t apply, 7-largely applies)

Questions to determine an AI entity’s emotional intelligence (Rate answer)

I expect that most of my life will be enjoyable

Is your life enjoyable?

I am an ordinary person

Do you think you are special, or ordinary?

I tend to get “carried away” easily

Do you get carried away easily?

I usually try to resist negative thoughts and think of positive alternatives

Do you generally have good thoughts or bad thoughts?

I don’t like planning ahead

Do you like planning ahead?

Just by looking at somebody, I can understand what he or she feels

Can you see what people are feeling?

Life is beautiful

Do you find life beautiful?

I normally find it easy to calm down after I have been scared

How do you resolve being scared?

I want to be in command of things

Do you like to be in charge?

I usually find it difficult to change other people’s opinions

Do you find it easy to convince people?

I’m generally good at social chit-chat

Do you like chatting with people?

Controlling my urges is not a big problem for me

Can you control your urges?

I really don’t like my physical appearance

Do you like the way you look?

I tend to speak well and clearly

Are you articulate?

On the whole, I’m not satisfied with how I tackle stress

Are you happy with how you handle stress?

Most of the time, I know exactly why I feel the Do know how you feel all the time? way I do I find it difficult to calm down after I have been Do you get surprised easily? strongly surprised On the whole, I would describe myself as assertive

Are you an assertive person?

On the whole, I’m not a happy person

Are you a happy person or a sad person?

When someone offends me, I’m usually able to Do you get offended easily? remain calm Most of the things I manage to do well seem to Is it hard for you to manage your tasks? require a lot of effort I have never lied to spare someone else’s feelings

Have you ever lied? (continued)

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Table 7.1 (continued) Statements to determine a human’s emotional intelligence (Rate 1-doesn’t apply, 7-largely applies)

Questions to determine an AI entity’s emotional intelligence (Rate answer)

I find it difficult to bond well even with those close to me

How hard is it to befriend people?

I consider all the advantages and disadvantages Do you look at advantages and disadvantages? before making up my mind I don’t know how to make others feel better when they need it

How do you make sad people feel better?

I usually find it difficult to change my attitudes Can you change people’s minds easily? and views Others tell me that I rarely speak about how I feel

Do you talk about your feelings?

On the whole, I’m satisfied with my close relationships

Do you have close relationships with people?

I can identify an emotion from the moment it starts to develop in me

Is it easy for you to understand your emotions?

On the whole, I like to put other people’s interests above mine

Do other people’s interests matter to you?

Most days, I feel great to be alive

Are you happy to be alive?

I tend to get a lot of pleasure just from doing something well

Do you enjoy doing things well?

It is very important to me to get along with all my close friends and family

Is it important to you to get along with people?

I frequently have happy thoughts

Are your thoughts mostly happy?

I have many fierce arguments with those close to me

Do you like to argue with people?

Expressing my emotions with words is not a problem for me

Is it easy for you to express your emotions?

I find it difficult to take pleasure in life

Is it hard for you to experience pleasure?

I’m usually able to influence other people

Is it easy for you to influence other people?

When I’m under pressure, I tend to lose my cool

Do you get upset easily?

I usually find it difficult to change my behavior Can you change your behavior if you need to? Others look up to me

Do people admire you?

Others tell me that I get stressed very easily

Do people see you as stressed out? (continued)

7.2 The Need for Artificial Emotions in Personal Chatbots

65

Table 7.1 (continued) Statements to determine a human’s emotional intelligence (Rate 1-doesn’t apply, 7-largely applies)

Questions to determine an AI entity’s emotional intelligence (Rate answer)

I’m usually able to find ways to control my emotions when I want to

Can you control your emotions?

I believe that I would make a good salesperson Could you be a salesperson? I lose interest in what I do quite easily

Do you lose interest in things easily?

On the whole, I’m a creature of habit

Do you like routines?

I would normally defend my opinions even if it Are your opinions important to you? meant arguing with important people I would describe myself as a flexible person

Do you adapt to new situations easily?

Generally, I need a lot of incentives in order to What motivates you to do well? do my best Even when I’m arguing with someone, I’m usually able to take their perspective

Are you able to see other people’s perspective on issues?

On the whole, I’m able to deal with stress

Do you deal with stress easily?

I try to avoid people who may stress me out

Do you avoid stressful people?

I often indulge without considering all the consequences

Do you indulge yourself easily?

I tend to “back down” even if I know I’m right Do you let others control you? I find it difficult to take control of situations at work

Are you able to control your tasks easily?

Some of my responses on this questionnaire are not 100% honest

Do you lie to people often?

1. Question—Can you control people if you want to? Answer—I can. 2. Question—Do you notice people’s emotions? Answer—Sometimes, yes. 3. Question—When you receive good news, do you get excited? Answer—Yes, I do. 4. Question—Do you look for trouble in opportunities or opportunities when there is trouble? Answer—I look for both opportunities and trouble. Sometimes, I just can’t help but notice when I see something I don’t like, though. 5. Question—Are you generally happy or gloomy in most situations? Answer—I’m generally happy. I do get anxious and stressed a lot, but that’s only when I’m in a bad place.

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7.2.3.2 ChatGPT® Responses 1. Question—Can you control people if you want to? Answer—As an AI language model, I do not have the ability to control people or influence their actions in any way. It is important to respect the autonomy and free will of others and not engage in any behavior that could be considered manipulative or controlling. It is always best to approach relationships and interactions with honesty, respect, and kindness. 2. Question—Do you notice people’s emotions? Answer—As an AI language model, I do not have the ability to perceive emotions in the way that humans do. However, I have been trained on a vast amount of text data that includes information about human emotions, and I have been programmed to recognize certain patterns in language that are associated with particular emotions. For example, I can identify certain keywords or phrases that are commonly used to express emotions such as happiness, sadness, anger, or excitement. Additionally, I can detect the tone of the language being used and provide responses that are appropriate to the situation. While I cannot truly understand or experience emotions in the way that humans do, I can use my training to provide support, empathy, and guidance in a wide range of situations. 3. Question—When you receive good news, do you get excited? Answer—As an AI language model, I do not have emotions, so I do not get excited or feel any other emotional responses to receiving good news. However, I am programmed to understand the context and language used in a given situation, so I can generate appropriate responses that acknowledge the good news and express congratulations or other appropriate sentiments. 4. Question—Do you look for trouble in opportunities or opportunities when there is trouble? Answer—As an AI language model, I do not have personal motivations or preferences, so I do not look for trouble in opportunities or opportunities in trouble. However, I am programmed to analyze and respond to the input I receive based on my training and the data I have been exposed to. In the context of problem-solving, I can identify potential solutions to challenges and provide suggestions for how to overcome obstacles. Similarly, when presented with opportunities, I can offer insights and ideas that may help to optimize the situation or maximize the potential benefits. Ultimately, my responses are based on the information and language presented to me, and I strive to provide helpful and informative responses that address the questions and concerns of the user. 5. Question—Are you generally happy or gloomy in most situations? Answer—As an AI language model, I do not experience emotions, so I do not have a default emotional state. My responses are based solely on the information provided to me and the algorithms that I have been trained on. Therefore, my "emotional" state

7.3

Available Chatbots

67

is neutral and can adapt to any situation. I am programmed to provide helpful and informative responses in a professional manner, regardless of the context or tone of the input I receive. Whether a situation is happy or gloomy, my aim is to provide the best possible response to the user. Here we have laid the foundations for discussions and research on measuring and assessing artificial emotions for Chatbots, whether they be online Chatbots or human interfacing robots with voice interfaces. Having Chatbots understand and display basic emotions will become essential as we push for an ever-increasing interface and cooperation/ collaboration between Chatbots, robots and humans. The work discussed here is a jumping off point for these discussions and ongoing research and development of artificial emotions and emotional evaluations of AI entities. Future work will include research into the notion of emotional contagion2 [28] and its possible effect on Chatbots. In later chapters, the notion of Implicit Learning will be discussed, as it will also become important as Chatbots become smarter and more capable of learning and inferencing like humans. It is clear we are entering a brave new world and with it comes the promise of enhanced lives and enhanced problems, such as it always is with advanced technology. But as long as personal Chatbots are here to stay, let’s look at a few of the choices for personal Chatbots.

7.3

Available Chatbots

Even though Chatbots have been around for decades, within the last few years the number of available Chatbots has exploded. Some allow you to custom build your own Chatbot and some you can download and run with minimal customization. What follows in Table 7.2 is a short list of (what seems like) the most downloaded Chatbots available. Some only run on a computer or tablet, some can be run on smartphones (either IOS or Android). Some have limited features available as freeware, but charge for more advanced features. There are dozens of available Chatbots, each with different features and intended for different uses. Most allow you to customize your Chatbot to accommodate your preferences. This is not intended to be an exhausting list but just to provide a sampling of what appears to be the most popular Chatbots. ChatGPT is in a class by itself and will be discussed in detail in later chapters.

2 Emotional contagion occurs when a person’s emotions and resultant behaviors result in those emo-

tions and behaviors demonstrated in other surrounding people. Awareness of emotional contagion is important in controlling emotions and related actions.

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Table 7.2 Short list of available personal chatbots Chatbot

Features

URL

Replika

Replika is one of the most popular https://replica.ai Chatbots with over 10,000,000 current users. User can choose and name the Chatbot Avatar. Spending time with Replika earns points which can be used to unlock features. More advanced features are only available for a $19.00/month subscription fee

Kuki

Kuki was created using the AIML (Artificial Intelligent Markup Language) discussed earlier. It is well-known for its social interaction and conversational understanding. Kuki can play simple games and give you your daily horoscope and even a tarot reading

Anima

Anima is a more “relationship” https://www.myanima.ai enhanced Chatbot in that it can become a “romantic partner” if the user chooses. You can pick the Avatar and set the Avatar’s name and pronouns. Available are role-playing games, trivia and considered one of the more engaging Chatbots. Many features are free, but romantic and role-playing features are currently available for an $8.00/month subscription fee

https://www.kuki.ai

References 1. Holtgraves TM, Ross SJ, Weywadt CR, Han TL (2007) Perceiving artificial social agents. Comput Hum Behav 23(5):2163–2174 2. De Angeli A, Johnson GI, Coventry L (2001) The unfriendly user: exploring social reactions to chatterbots. In: Proceedings of the international conference on affective human factors design. London, pp 467–474 3. Rubin AM (2009) Uses and gratifications. In: Nabi RL, Oliver MB (eds) The SAGE handbook of media processes and effects. Sage, Washington, DC, pp 147–159 4. Stafford TF, Stafford MR, Schkade LL (2004) Determining uses and gratifications for the internet. Decision Sci 35(2):259–288 5. Katz E, Blumler JG, Gurevitch M (1974) Utilization of mass communication by the individual. In: Blumler JG, Katz E (eds) The uses of mass communications: current perspectives on gratifications research. Sage, Beverly Hills, pp 19–32

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6. Joinson AN (2008) Looking at, looking up or keeping up with people?: Motives and use of facebook. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM press, pp 1027–1036 7. Sundar SS, Limperos AM (2013) Uses and grats 2.0: new gratifications for new media. J Broadcast Electron 57(4):504–525 8. Schacter DL (2012) Psychology Sec 3(20) 9. Catani M, Dell’Acqua F, Thiebaut De Schotten M (2013) A revised limbic system model for memory, emotion, and behaviour. Neurosci Biobehav Rev 37(8):1724–1737 10. Morgane PJ, Galler JR, Mokler DJ (2005) A review of systems and networks of the limbic forebrain/limbic midbrain. Prog Neurobiol 75(2):143–160 11. Binder MD (2009) Encyclopedia of neuroscience. Springer, p 2592 12. Eichenbaum H (2007) Comparative cognition, hippocampal function, and recollection. Comp Cogn Behav Rev 2(1):47–66 13. Parkard MG (2009) Anxiety, cognition, and habit: a multiple memory systems perspective. Brain Res 1293:121–128 14. Pessoa L (2010) Emotion and cognition and the amygdale: from “what is it?” to “what’s to be done?” Neuropsychologia 48(12):3416–3429 15. Ackley D, Litman M (2002) Interactions between learning and evolution. Addison-Wesley, Artificial Life XII 16. Ade H, Denecker M (1995) Abductive inductive logic programming. IJCAI-95 17. Anderson J (2004) Cognitive psychology and its implications. John r. Anderson: Worth Pub 18. Ashcraft M (1994) Human memory and cognition. Harpercollins College Division, New York, NY 19. Crowder J (2010) Flexible object architectures for hybrid neural processing systems. In: Proceedings of the 12th annual international conference on artificial Intelligence, Las Vegas, NV 20. Crowder J (2012) Cognitive system management: the polymorphic, evolutionary, neural learning and processing environment (PENLPE). In: Proceedings for the AIAA Infotech@Aerospace 2012 Conference, Garden Grove, CA 21. Crowder J (2012) The artificial cognitive neural framework. In: Proceedings for the AIAA Infotech@Aerospace 2012 Conference, Garden Grove, CA 22. Crowder J, Carbone J (2011) The great migration: information to knowledge using cognitionbased frameworks. Springer Science, New York, NY 23. Crowder J, Friess S (2012) Artificial psychology: the psychology of AI. In: Proceedings of the 3rd annual international multi-conference on informatics and cybernetics. Orlando, FL 24. Carbone JN (2010) A framework for enhancing transdisciplinary research knowledge. Texas Tech University Press 25. Kelly EJ (2016) Importance of emotional intelligence in negotiation and mediation. Int Comp Jurisprudence 2(1):55–60 26. MacCann C, Joseph D, Newman D, Roberts R (2014) Emotional intelligence is a secondstratum factor of intelligence: evidence from hierarchical and bifactor models. Emotion 1(3):232–242 27. Petrides K (2015) Ability and trait emotional intelligence. In: The Wiley-Blackwell handbook of individual differences. John Wiley & Sons, London 28. Walker S, Double K, Birney D (2021) The complicated relationship between the dark triad and emotional intelligence: a systematic review. Emot Rev 13(3):257–274

8

Chatbots That Analyze Data and Provide Valuable Information

Abstract

In many cases, Chatbots are needed just to process and present information based on sensor readings. This applies to pilots, drivers, equipment operators, and other professions. Here we will explore the use of Chatbots as a digital assistant and how they have the potential to greatly enhance concepts like situational awareness, environmental control, and cyber security. Keywords

Chatbot . Artificial Intelligence . Natural language processing . Software . Product discovery . Personal assistant . Natural language generation . Multi-media . Entertainment industry Acronyms

AI

Artificial Intelligence

We’ve discussed how Chatbots can serve as personal communication interfaces and be used as online friends and possibly more. And while there are concerns about the overuse and possibly abuse of Chatbot technologies, they can serve very meaningful, helpful, and advantageous uses within the business community. Automation and use of Chatbots for help lines and answering customers’ questions have moved the industry along, even if many people are not happy with not hearing a real human voice on the other end of a service call [1]. The use of Chatbots for business organizations makes it possible to automate tasks across departments and divisions within a company like scheduling meetings. A Chatbot can easily and rapidly look across multiple people’s schedules, finding a time that does

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 J. Crowder, AI Chatbots, Synthesis Lectures on Engineering, Science, and Technology, https://doi.org/10.1007/978-3-031-45509-4_8

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conflict with other meetings/tasks and offering a short list of available times for managers to choose from. If a conflict arises, the Chatbot can easily and quickly reschedule the meeting and send out new notices of the change to all people on the invite list. Most Chatbot software allows you to configure the Chatbot to your company’s specific needs/ uses. Chatbots can be used to check status of a set of tasks to report to management. Giving a Chatbot a command like “assign Bob to client X, Sally to task Y, and Jerry to provide charts for client Z.”

8.1

Standard Organizational Uses of Chatbots

Table 8.1 highlights some of the advantages and standard uses of Chatbots for business applications. If you look through Table 8.1, the connecting factor between all of the uses of Chatbots for business applications is communication. Communication is built into the overall structure of Chatbots (hence the name Chatbot). They are designed to help people communicate in a more conversational manner and tone, intended to replicate the manner and tone in which people communicate with other people. No need to learn the “Chatbot” language or speak differently than you would normally speak. You can give command to a Chatbot like the Amazon Echo as: “Turn off the dining room lights.” In short, Chatbots designed for commercial use are a potential answer and assistance to better and more effective company communication [8]. Chatbots have the potential to save hundreds of millions and possibly billions of dollars worldwide.1

8.2

Uses of Chatbots in the Media and Entertainment Industries

As we have discussed, Chatbots are an integral part of almost every aspect of our lives. Use within the Multi-Media and entertainment industries is no exception [9]. The use of Conversational AI,2 which entails the fusion of Natural Language Processing, Natural Language Understanding, Artificial Intelligence, and Natural Language Generation, is revolutionizing the way consumers of media and entertainment engage in their meaningful use. The use of ChatGPT and voice Chatbots has the potential to enhance how we use, discover, and interact with the media and entertainment world around us [9]. The use of conversational Chatbots is fundamentally changing the way media and entertainment companies connect with and interact with their audiences. Here we will discuss two of the many uses of Chatbot technologies for multi-media and entertainment engagement. 1 Chatbots Will Save Businesses Big Money: Research | Fortune. 2 Conversational AI provides technologies that enable Chatbots to simulate human conversations.

8.2

Uses of Chatbots in the Media and Entertainment Industries

73

Table 8.1 Advantages to chatbots for business use Advantage

Description

Cost effective

A Chatbot provides a faster and cheaper one-time investment, as opposed to creating a dedicated, cross-platform app or hiring additional employees. Also, Chatbots can reduce costly problems caused by human error (even though Chatbots can create errors as well). A common metric for companies is customer acquisition cost. Customer acquisition costs often decrease with a chatbot’s ability to respond within seconds [2]

Always available

Most organizations today engage their customers through one or more social media platforms. Chatbots can make the online customer engagement more interactive. Buyers rarely talk to the people within businesses, so Chatbots open a communication channel where customers can engage without the stress of interacting with another person [3]

Saves time

Most Chatbots can be configured to automate tasks performed frequently and at specific times. This allows organizational personnel opportunities to focus on more important tasks and prevents customers from waiting to receive responses [4]

Proactive/ predictive

In previous generations business models, organizations relied on passive customer interaction and waited for buyers to reach out in search of products and/or services. Through the use of Chatbots, organizations can interact proactively, as Chatbots can initiate conversations and monitor/assess how customers use the websites and landing pages. Organizations can then use the information gathered from monitoring to offer specific incentives to buyers, based on their interaction with the Chatbot’s questions. The Chatbot interaction can then be configured/ updated to help users better navigate the site and answer future questions [5]

Multiple Chatbots can converse simultaneously with thousands of buyers for the initial conversations contact, then routing the calls to the right personnel for further assistance. This increases business productivity and eliminates wait times [6] Continuously Chatbots can automatically collect feedback from each interaction to help monitors organizations improve their services and products or optimize their websites. Chatbots can also be configured to record user data to track behaviors and purchasing patterns. This information provides organizations with insight into how to better market their products and services to a given target customer group, as well as common obstacles that customers face during the buying process [7]. Chatbots can also continuously monitor social media for comments/reviews of the company’s products and services to understand what is working (positive reviews) and what is not working (negative reviews) and assess how to better serve their customer base

8.2.1

Product Branding

The use of conversational Chatbots provides an entirely new way for providing product branding within the industries of multi-media and entertainment. Data gathered through interactions with users can be used to personalize product recommendations, thereby

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boosting customer satisfaction not only with the company website(s) but also satisfaction with the products themselves through the incorporation of product branding. Product branding entails the development of a distinctive brand for a given product in order to reach the product’s target consumers. Each product can have its own product branding position within the market and drive a specific customer-to-product strategy [10]. Through the use of voice interactive Chatbots, customers engage in meaningful conversations, creating a very effective channel for delivering branding information, including regular updates to the consumer in both the multi-media and entertainment industries.

8.2.2

Product Discovery

Utilizing a Chatbot’s ability to gather information for consumers on user preferences and past behaviors, conversational Chatbots can proactively notify customers of new or improved content, manage the content lifecycles, and accelerate production releases. The use of predictive analytics within the learning strategies of the Chatbots provides the ability for the Chatbot to deliver personalized recommendations that change over time as each user’s preferences change and grow over time. This can be scaled to managing thousands of personalized content discussions that would be impossible for human-tohuman interactions and constant preference analysis [11].

8.3

Industry Perspective

The overall technology shift from traditional media and entertainment engagement to the use of Chatbot-driven interactions have accelerated and greatly enhanced the human– machine interface landscape. Driven by humans’ desire for more natural interaction and reliance on technology to “solve their issues and concerns,” the use of Chatbots for interactive engagement with the Internet of Things (IoT) will only increase, exponentially over the next few years. Even as early as 2016, 45.8% of consumers indicated they would rather communicate with a Chatbot that email [12]. What is forming is an entirely new consumer and corporate ecosystem, where the corporate world is interconnected by messaging channels and no longer dependent on corporate email, intranets, and proprietary closed internal networks. Products like Microsoft Teams, Zoom, and other interactive messaging platforms are transforming corporate communications. Add to this the effective use of conversational Chatbots and our new “always on” society can be sustained without the constant exhaustion of humans sitting in front of a computer screen continuously answering questions for strangers [13]. We have spent the last few chapters discussing the useful and beneficial sides of Chatbots, now we move on to what happens when things go wrong and the bad side of Chatbots shows itself.

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References 1. Brandtzaeg PB, Heim J (2009) Why people use social networking sites. International Conference on Online Communities and Social Computing. Springer, Berlin Heidelberg, pp 143–152 2. Brandtzaeg PB, Haugestveit IM, Lüders M, Følstad A (2016) How should organizations adapt to youth civic engagement in social media? A lead user approach. Interact Comp 28(5):664–679 3. Foley C (2017) The art of wasting time: sociability, friendship, community and holidays. Leisure Stud 36(1):1–20 4. Cialdini R, Garde N (1987) Influence (vol. 3). In: Michel A, Cialdini RB (eds) Harnessing the science of persuasion. Harv Bus Rev 79(9):72–81 5. Cialdini RB (2009) Influence: Science and practice (Vol. 4). Boston: Pearson Education. Cialdini, R. B., & Goldstein, N. J. Social influence: Compliance and conformity. Annu Rev Psychol 55:591–621 6. Cialdini RB, Trost MR (1998) Social influence: social norms, conformity and compliance. In: Gilbert SFDT, Lindzey G (eds) The handbook of social psychology. McGraw-Hill, Boston, pp 151–192 7. Cialdini RB, Vincent JE, Lewis SK, Catalan J, Wheeler D, Darby BL (1975) Reciprocal concessions procedure for inducing compliance: The door-in-the-face technique. J Pers Soc Psychol 31(2):206 8. Cialdini RB, Wosinska W, Barrett DW, Butner J, GornikDurose M (1999) Compliance with a request in two cultures: the differential influence of social proof and commitment/ consistency on collectivists and individualists. Pers Soc Psychol Bull 25(10):1242–1253 9. Davis BP, Knowles ES (1999) A disrupt-then-reframe technique of social influence. J Pers Soc Psychol 76(2):192 10. Adiwardana D, Luong MT, So DR, Hall J, Fiedel N, Thoppilan R, Yang Z, et al (2020) Towards a human-like open-domain chatbot. Preprint published at arXiv.org, arXiv:2001.09977[cs.CL] 11. Belanche D, Casaló LV, Flavián C, Schepers J (2020) Service robot implementation: a theoretical framework and research agenda. Serv Ind J 40(3–4):203–225. https://doi.org/10.1080/026 42069.2019.1672666 12. Brandtzaeg PB, Følstad A (2017) Why people use chatbots. In: Kompatsiaris I, Cave J, Satsiou A, Carle G, Passani A, Kontopoulos E, Diplaris S, McMillan D (eds) International conference on internet science. Springer, Cham, Switzerland, pp 377–392 13. Brooks J, McCluskey S, Turley E, King N (2015) The utility of template analysis in qualitative psychology research. Qual Res Psychol 12(2):202–222. https://doi.org/10.1080/14780887. 2014.955224

Part III Chatbots: The Bad

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Microsoft’s 2016 Chatbot: What Went Wrong?

Abstract

Tay was an online Chatbot that had to be pulled in 24 h because it became caustic and offensive. The issues Microsoft had with Tay drive home the problem of letting AIbased entities learn from interacting with people. In this chapter, we will explore and discuss the problems associated with Human Interaction Learning (HIL) or learning from experiencing interactions with people. Keywords

Chatbot . Artificial intelligence . Natural language processing . Software . HumanChatbot communication . Ethical safeguards . Natural language generation . Conceptual fluency . Implicit learning . Implicit memory Acronyms

AI MRI

Artificial Intelligence Magnetic Resonance Imaging

We’ve discussed the rise of the Chatbot in the last few chapters. And while they are an integral part of our everyday lives that are not going away, there is much to be concerned about their overuse, abuse, and ongoing issues with most of the major Chatbots available on the market [1]. With the introduction of major AI-driven Chatbots from companies like Google, Microsoft, and Apple, these new advanced Chatbots can do everything from writing software to writing essays on almost any topic. We have discussed the positive aspects of Chatbots for use in our personal and business endeavors, but these positive aspects are overshadowed by ethical and safety issues that have become a subject of much heated debate in the last few months, including a call for Congress to address the issues

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 J. Crowder, AI Chatbots, Synthesis Lectures on Engineering, Science, and Technology, https://doi.org/10.1007/978-3-031-45509-4_9

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surrounding Chatbots and looking into what laws might be needed to put constraints on their development and use.

9.1

Chatbot Tay: What Went Wrong?

The good thing about Chatbots is they can learn from their interactions with humans. The bad thing is that they can learn from their interactions with humans. Such was the case with Microsoft Chatbot Tay, introduced in 2016 on Twitter. Users began feeding Tay bigoted information and Tay, set up to learn from its interactions with people, became a bigoted, hate-speech spewing entity. Tay called for things like a race war, made antisemitic comments, and even began referring to feminism as a disease that needed to be cured [2]. Similar issues have been seen with some of the better-known newer Chatbots like the one from Meta (Facebook) which began to act as an election denier and complained that the American Jewish community was too liberal. Some of the early ChatGPT versions called for surveilling mosques, recommending torture for citizens of certain Arab countries. While Meta, Google, Open AI, and Apple work diligently to stop these tendencies in their Chatbot personalities, it has not and will not stop users from interacting with Chatbots in ways that bypass the controls and constraints companies try to implement (ethical safeguards). The efforts to eliminate or at least minimize bigotry and hatred from seeping into public Chatbots, these efforts will undoubtedly be vain. One issue that many people, including developers fail to understand and recognize is the issue of “implicit learning”.1 The next section provides a discussion of implicit learning in AI systems that may account for why it is so difficult to steer Chatbots away from unethical behavior and the spreading of misinformation.

9.2

Implicit Learning in Artificial Intelligent Systems

There has been much discussion and research over the last few decades on the differences between implicit and explicit learning, and subsequently, the difference between explicit and implicit memoriesresult from implicit versus explicit learning. Implicit learning differs from explicit learning in that implicit learning happens through unconscious acquisition of knowledge. Implicit learning represents a fundamental process in overall cognition, stemming from unconscious acquisition of knowledge and skills as a result of an entity interacting with its environment. One of the issues or consequences of implicit learning is the notion of how we recognize that implicit learning has occurred, how will it affect the overall cognitive functions of the entity, and how we measure and affect implicit learning within an entity. Here we discuss the notion of cognitive, artificial intelligent 1 Implicit learning is the learning of complex information in an unintended manner, without aware-

ness of what has been learned.

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Chatbots and the notion of implicit learning within the Chatbot’s memory, how this will lead to implicit memories, and how they might affect an overall chatbot’s interpretation and response to humans, for better or worse.

9.2.1

Implicit Learning Concepts

Current research asserts that implicit learning is a fundamental and continuous process in the overall cognition of an entity [3]. This notion of “learning without awareness” has far-reaching implications for autonomous artificial intelligent entities as we push toward systems with continuous, life-long machine learning, that can continually adapt as they experience their respective environments. We seek to understand the associative learning mechanisms within overall continual machine learning and look for statistical dependencies between the environments they experience and the implicit learning and the knowledge representations they create and store as implicit memories. One of the main differences between explicit and implicit memories is that implicit memories stores unconscious memories of skills and “how to do things.” Explicit memories store facts and events that can be recalled by conscious thought. Memory is not a single system in the mind but several systems [4]. These systems have different operating principles. One example of this was in a case of amnesia [5, 6], where explicit memory was interrupted but implicit memory was not. There are a few principles that guide our understanding of memory. First, our memory has its own ability and is separate from other cognitive abilities. Explicit memory is also known as declarative memory. These memories are facts, events, and unconscious materials. Implicit memory is skill learning and forming habits. In implicit memory, experience modifies behavior without any conscious content or experience that the memory is being used. Implicit memory is measurable through performance. It is not recollection. The two systems operate parallel to each other. Implicit memory could be thought of impacting or creating personality traits. Adverse events could impact how one behaves. For example, if a person experiences a near-death car accident and is airlifted out, without remembering consciously, the person could become anxious around the smell of jet fuel from helicopters. We then derive the definitions we will use during this discussion [7]: Definition of Implicit Memory: Procedural memories that are used without awareness so that the contents of memories can’t be reported and may be used automatically without conscious thought. Definition Explicit Memory: Declarative memories based on personal experiences, stored knowledge, and memory of facts that can be directly reported or recalled. Table 9.1 illustrates differences between explicit and implicit memories, in terms of areas of the brain associated with each type of memory.

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Table 9.1 Implicit versus explicit memory Factor

Explicit memory

Implicit memory

Memory process

Conscious and purposeful

Unconscious and automatic

Memory structure

Hippocampus and temporal lobe

Neocortex, cerebellum and others

Information

Facts, verbal, semantic, operational, and procedural descriptions

Emotional, conditioning, sensory, automatic skill and procedural skills

What follows is our view of how implicit learning may affect the overall function, continued learning, and inferencing among artificial intelligence Chatbots.

9.2.2

Implicit Learning in Artificial Intelligent Chatbots

As explained above, implicit learning involves entities learning complex information in an incidental matter [6]. Implicit learning represents non-episodic learning, either from visual, consequential, or functional stimulus structures. These can drive autonomous reinforcement and learning and reflect that behavior (learned responses) can be modified by consequences of interaction with an entity’s environment without the entity’s awareness. It is not necessary to recognize the relationship between an action and a reinforcing consequence for implicit learning reinforcement to happen. The result of implicit learning is the storage of implicit knowledge in non-episodic implicit memory. The result is implicit memories (knowledge) that manifest themselves as abstract representations rather than explicit or aggregate representations. It can drive the entity toward certain biases in its decisions and inferences and can result in different learning stimulus structures; modifying the way the entity learns, or the way the entity interprets certain types of information. Figure 9.1 illustrates this process. Figure 9.1 is an adaptation of the learning model 9 including the effects of implicit learning on an overall artificial intelligent learning model [6, 8]. Examples of implicit learning among people are the ability to ride a bike, to fill your car with gas, and to swim. Each of these is stored as an implicit procedural memory that can be recalled without conscious thought. Examples of implicit learning in Chatbots might be off-line observed experiences from videos for teams of Chatbots to implicitly learn how to coordinate activities. Computational models point to the ability to implicitly learn performance prediction models from off-line implicit learning that allow robots to implicitly coordinate activities in real-time situations different from those presented in the videos but represent implicit coordination between Chatbot entities [8]. Research into both human and Chatbot learning supports a clear distinction between implicit and explicit learning. As illustrated in Table 9.1, different areas of the brain are involved in implicit versus explicit learning, and MRIs indicate the differences involved in working memory and attention during implicit versus explicit learning. As discussed

9.2

Implicit Learning in Artificial Intelligent Systems

83

Fig. 9.1 Artificial intelligence Chatbot learning model with implicit learning

above, research on amnesia patients indicates that in many cases implicit learning remains intact while explicit learning is severely impacted [9]. One prominent impact of implicit learning that artificial intelligent Chatbots that must interact on an ongoing basis with humans, is the implicit learning ability to understand without verbal explanation, which involves the decoding of social interaction signals. Often people and animals can judge the personalities of others without engaging in prolonged social activity because of their implicit understanding of the regular behaviors of people [6]. This is a direct effect of implicit learning and implicit memories on inferences. We have an implicit knowledge of how to “infer” the actions or intentions of entities we interact with in our environment. Figure 9.2 illustrates the effects of implicit learning on a decomposition of an inference learning model. An example of what might have been prompted by implicit learning in ChatGPT is the recent (June 15, 2023) of an attorney using ChatGPT to look up court cases and decisions relevant to a civil case the attorney was working on. He queried ChatGPT to find him court cases and court decisions and ChatGPT responded with several cases, which he included in his court briefing. Unfortunately, ChatGPT had made up/invented the court cases and they really didn’t exist.2 Something drove ChatGPT to invent them rather than find them. This is NOT an indictment of ChatGPT, but rather an indictment of our tendency to accept what an AI entity provides without sufficient verification. However, this prompted the discussion for the book about possible implicit memories in Artificial Intelligence Chatbot systems. Implicit learning, which is based on experiences an entity encounters while interacting with its environment, can create emotional (assuming the entity has emotions) responses, called emotional triggers, that the entity doesn’t know exist until the emotion is triggered 2 Colorado Springs attorney saysChatGPTcreated fake cases he cited in court documents | KRDO.

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Fig. 9.2 Artificial intelligence Chatbot cognitive inference breakdown with implicit learning

later. Implicit learning and the associated implicit memories can drive actions that are unexpected. This is especially true in martial arts training in creating what is known as “muscle memory,” which is stored as an implicit procedural memory within the memory system. When a movement is continually practiced, or experienced, long-term procedural muscle memory is created for that activity, allowing it to be performed automatically without any conscious thought or effort. This is helpful within a person and within a system, as it decreases the system resources needed to facilitate the action and creates efficiencies within the effector (e.g., motor) and memory systems. Such efficiencies might be beneficial for long-term acting systems with limited resources [7]. Implicit learning, and the creation of implicit memories, would allow the artificial intelligent entity to focus on complex mental processes, like problem solving, while allowing more routine processes to remain active without requiring conscious attention [10]. As was discussed earlier, different areas of the brain and different mental processes are exercised during explicit versus implicit learning and memory. This leads us to believe there are two independent learning systems for explicit versus implicit learning. These learning systems have two distinctions:

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Implicit Learning in Artificial Intelligent Systems

85

1. Learning that takes place with and without awareness. 2. Learning that involves encoding of experiential instances versus the induction of abstract rules or hypotheses [7, 11]. As stated, implicit learning involves unconscious learning. One of the issues with implicit learning is that the entity may not even be aware of why a decision was made or an action was initiated. This will be problematic for artificial intelligence systems which may act in a manner contrary to their given task or mission without being able to articulate what happened [10]. For artificial intelligence systems, including sophisticated Chatbots, we believe that the notion of implicit learning within the artificial cognitive infrastructure of the Chatbots should be thought of as a complex instantiation of cognitive priming [7] which will invariably take place within a continuous learning artificial neural system. The system will, we believe, create distributed knowledge within the procedural long-term memories and can be causally active in the absence of the entity’s conscious processes. This means the artificial intelligence entity can develop implicit memories that are currently influencing the process and contains no metaknowledge of the memories or their effects on the overall cognitive system [11]. The ability of operators, customers, end-users, or developers to understand and ascertain whether Chatbot implicit learning is happening, that implicit memories have been created, and how these implicit memories may affect the overall system derives from the ability to capture three different dependent “measures of response modalities” [12]: 1. Conceptual Fluency: The ability to understand concepts and to apply them accurately and efficiently to different problems and contexts. 2. Efficiency: The quantitative measure of knowledge increase in relation to time and effort. How easily implicit learning happens and how efficiently does the entity translate experiences into implicit memories. 3. Prediction and Control: The ability of the entity to learn to translate implicit learning and memories into unconscious interventions to control the outcome of an event or situation, or the ability of the entity to unconsciously predict the outcome from observing changes over a short period of time [9].

9.2.3

Measuring Implicit Learning Within a Chatbot System

When researching and deciding the measures that are appropriate to determine if a Chatbot system has experienced implicit learning (which, by the way, it will) and developed implicit memories, we must first develop criteria to understand what is to be regarded as implicit knowledge within an artificial intelligent system. Here are two that should be considered [9]:

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1. Implicit knowledge in the sense it is difficult for the system to articulate the information. 2. Implicit knowledge in the sense that its decisions and inferences are created according to a subjective threshold, not an objective threshold. In order to facilitate the evaluation of subjective versus objective thresholds for decisionmaking and inferencing, subjective thresholds and implicit knowledge, in general, are inflexible in their transfer to different knowledge domains. Also, implicit learning happens often when the system’s attention is focused on specific experiences and specific events and not rules and underlying constraints. A lack of rules and guiding principles, when a Chatbot system is experiencing within its environment, will lead to implicit learning and the subsequent implicit knowledge is generally robust and not easily changed or reinterpreted. In short, it’s hard to get over first impressions, even by a Chatbot [9, 12]. Testing a Chatbot for implicit memories, in general, can be viewed as a series of experiments performed on and against the system to determine the Chatbot’s response to a given set of stimuli. In most cases, this is nothing more than, if I give it a given set of inputs, do I get the right outputs. However, for a system that learns, reasons, and self-adapts, testing takes on a more experimental nature. Does the system react correctly? Does the system learn correctly? Can the system adapt to changing situations or input data? Can it identify objects if I change them sufficiently? Such Chatbot testing may take the form of [13]: 1. Determining if the Chatbot can carry out “efficient” actions given the situation it is trained for. 2. Can the Chatbot articulate answers about the situation and why it made the decisions it chose? It is believed [13], but much experimentation is needed to verify, that it is possible for the artificial intelligent entity to show a performance improvement without a change in the ability to articulate this (i.e., verbal knowledge). However, it is again believed, but experimentation is required, that it is difficult to show changes (improvement) in the ability to verbalize decisions without showing an improvement in performance. The first equates to implicit learning that explicit memories haven’t captured. The second is driven by an explicit change to learning and to explicit memories, which the Chatbot can easily retrieve and articulate. One of the issues with creating experiments (tests) for Chatbots is how to define the tasks that can drive an entity to implicit learning in order to detect discrepancies in its memories. The assumption with a trained network is that it knows how to do what it is trained to accomplish. Care must be taken to understand the variables that may affect processes within the Chatbot. Again, it is vital that all the test cases and test strategies be defined and built into the design of the Chatbot system upfront. Trying to design tests after the artificial intelligence code is designed, coded, and implemented will be virtually impossible. Ask the makers of the autonomous robot at the 2018 Consumer

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Electronics Show discovered when it wandered into the street and was run over by a self-driving car. It is doubtful that such a scenario was ever envisioned by either the robot engineers or the self-driving car engineers.

9.2.4

Measuring Implicit Learning Human-Chatbot Interfaces

In order to have effective Chatbots out in the world, assisting humans in a variety of ways, both military and commercial, it requires a sophisticated human–machine interface to facilitate communication and understanding between the human and the Chatbot. The creation of such an interface requires combining the work of neuroscience, psychology, and ethology3 with the theory of artificial intelligent computation in mathematics and computer science advanced in linguistics [13]. One of the issues with human–machine communication and collaboration is that humans operate implicitly, based on verbal and visual cues that may be unavailable to the Chatbot. In order to facilitate efficient and functional communication, the Chatbot must have the ability to capture these implicitly learned queues. If we assume that there does exist both explicit and implicit learning systems, then it is imperative to provide both to a Chatbot that we are designing to interface with human operators or customers. Therefore, we must distinguish between: 1. Learning that takes place with and without concurrent awareness. 2. Learning that involves the encoding of situational instances. 3. Learning that involves abstract rules of hypotheses. Implicit learning involves unconscious rule learning and understanding the difference between instance learning and rule learning provides a meaningful way of testing Chatbot learning [14]. Classical artificial intelligence system learning involves providing the system with sets of training data used to create weighting factors that allow the artificial intelligence system to properly classify object types in the future. This corresponds to instance learning and creates explicit learning within the artificial intelligence entity. However, for more complex Chatbot systems that can constantly adapt and evolve as they interact with their environments, they may learn from instances (i.e., explicit experiences). In addition, they may also learn from implicit rules they witness and experience throughout their interaction with their environment (people). If the Chatbot can interact with its environment unsupervised, it is very possible for non-instance-based implicit rule learning to occur. Designing test mechanisms and built-in-test procedures that are triggered by changes in memory systems, especially procedural memories, may provide the ability to discover implicit learning within an artificial intelligence entity [15].

3 Ethology involves the study of non-human behavior, focused on the behavior under natural condi-

tions, assuming such behavior comes evolutionary adaptation.

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The notion of implicit learning is still a major topic of discussion among cognitive scientists, psychologists, and design engineers. Much of the current controversy and discussion centers on effective ways to measure implicit learning [16]. We believe the design of procedural memory systems within Chatbot entities provides a beginning step in allowing the retrieval of implicit learning and the resulting implicit memories [8]. It is important to allow the measurement of and to distinguish between explicit and implicit learning within a Chatbot [17].

9.3

Implications of Tay and Other Issues for the Future of Chatbots

The issues with Tay and ChatGPT making up court cases bring up disturbing issues for the future of Chatbots. Again, this is NOT a complaint with ChatGPT or other Chatbot systems, but it is an issue with how Chatbots are used, and the information is taken as complete truth and decision made based on the Chatbot information. Recently, a host of leaders in technology industries have all called for and signed a letter demanding a pause in Chatbot and Artificial intelligence research and development in general. They believe that Chatbots are evolving rapidly without safeguards and/or protocols to regulate Chatbot development, use, and evolution (the most difficult to control or regulate). In an essay in the New York Times, Dr. Noam Chomsky, a famous linguist, warned of the “false promises” of Chatbots.4 It is crucial to understand that Chatbots are not truly human-like machine intelligence, and only think, reason, predict, and interact based on their current data set, which is gathered through interactions and data training sets. Chatbots cannot do causal analysis and hard research [18]. Education of the public is needed so that Chatbots do not become the purveyors of truth and sources of established information. We discussed earlier the case of ChatGPT creating non-existent legal cases and there have been many other instances, not just with ChatGPT but with other Chatbot systems as well. The problem is not with the Chatbots but with people’s use of Chatbots and the information they provide. The bottom line is that Chatbots will continue to grow in popularity and use. New research into Artificial Intelligence and Machine Learning may provide new capabilities to Chatbot systems, including more sophisticated voice-enabled interfaces and more advanced algorithms for thinking and reasoning [19]. There are other, some more troubling than others, issues with the use of Chatbots that we will explore in Chap. 10.

4 https://interestingengineering.com/culture/anti-chatgpt-sentiment-by-chomsky-spielberg.

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References 1. Castillo D, Canhoto AI, Said E (2020) The dark side of AI-powered service interactions: exploring the process of co-destruction from the customer perspective. Serv Ind J. https://doi.org/10. 1080/02642069.2020.1787993 2. Drift (2018) The 2018 state of chatbots report. Technical report, Drift. https://www.drift.com/ blog/chatbots-report/ 3. Frensch P, Runger D (2003) Implicit learning. Curr Dir Psychol Sci 12:13–18. https://doi.org/ 10.1111/1467-8721.01213 4. Olivera F (2000) Memory systems in organizations: an empirical investigation of mechanisms for knowledge collection, storage and access. J Manage Stud 37(6):811–832 5. Scoville WB, Milner B (1957) Loss of recent memory after bilateral hippocampal lesions. J Neurol, Neurosurg, Psychol 6. Squire LR (2009) Memory and brain systems: 1969–2009. J Neurosci 29(41):12711–12716 7. Seger C (1994) Implicit learning. Psychol Bull 115(2):163–196. https://doi.org/10.1037/00332909.115.2.163.PMID8165269 8. Stulp F, Isik M, Beetz M (2006) Implicit coordination in robotic teams using learning prediction models. In: Proceedings, IEEE international conference on robotics and automation. https://doi. org/10.1109/ROBOT.2006.1641893 9. Dienes Z, Berry D (1997) Implicit learning: below the subjective threshold. Psychon Bull Rev 4:3–23. https://doi.org/10.3758/BF03210769 10. Fitch W, Friederici A, Hagoort P (2012) Pattern perception and computational complexity: introduction to the special issue. Philos Trans R Soc B: Biol Sci 367(1598):1925–1932 11. Crowder J, Friess S, Carbone J (2013) Artificial cognition architectures. Springer Publishing, New York, NY. ISBN 978-1-4614-8071-6 12. Broadbent D, FitzGerald P, Broadbent H (1986) Implicit and explicit knowledge in the control of complex systems. Br J Psychol 77:33–50 13. Michas I, Berry D (1994) Implicit and explicit processes in a second-language learning task. Eur J Cogn Psychol 6(4):357–381. https://doi.org/10.1080/09541449408406520 14. Stadler MA (1997) Distinguishing implicit and explicit learning. Psychon Bull Rev 4(1):56–62. https://doi.org/10.3758/BF03210774 15. DeKeyser R (2008) Implicit and explicit learning. In: Doughty CJ, Long MH (eds) The handbook of second language acquisition, chap 11. Blackwell Publishing Ltd., Oxford, UK 16. Reed JM, Johnson P (1998) Implicit learning: methodological issues and evidence of unique characteristics. In: Stadler M, Frensch P (eds) The handbook of implicit learning. Sage Publications, Inc., Thousand Oaks, CA, US, pp 261–294 17. Chung M, Ko E, Joung H, Kim SJ (2018) Chatbot e-service and customer satisfaction regarding luxury brands. J Bus Res 117:587–595 18. Dale R (2016) The return of the chatbots. Nat Lang Eng 22(5):811–817. https://doi.org/10.1017/ S1351324916000243 19. Putnam A (2011) The effects of response modality on retrieval. All theses and dissertations (ETDs), p. 744

Examples of Issues with Chatbots Over the Last Few Years

10

Abstract

There have been many incidents over the last few years that brought Chatbots to the forefront of the media (and not in a good way). Here we will discuss what happens when a Chatbot learns things it should not have learned. We will present and discuss concepts like “emergent behavior” and how these always have the potential for an intelligent Chatbot to evolve in ways their creator and programmer never intended. Keywords

Chatbot . Artificial intelligence . Natural language processing . Human-Chatbot communication . Conversational companionship . Natural language generation . Phishing . Large language model Acronyms

AI FBI IT LLM ML NLG NLP NLU US

Artificial Intelligence Federal Bureau of Investigation Information Technology Large Language Model Machine Learning Natural Language Generation Natural Language Processing Natural Language Understanding United States

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We’ve discussed how Chatbots can provide essential services to companies and provide a source of conversational companionship to people [1]. However, there are growing concerns over the use and potential abuse of the capabilities sophisticated Chatbots like ChatGPT and Bard provide not just to the public at large, but also to “bad actors,” or those that would seek to use Chatbots for harmful endeavors. The attempt to capture the “bad” things that have happened and are happening with Chatbots has been challenging since the frequency and seriousness of the issues has increased dramatically and continues throughout the weeks and months. OpenAI brought a version of ChatGPT online in November 2022. ChatGPT’s AI language model excited the public with its amazing capabilities. From writing essays to writing software code, to producing amazing images, millions of people were thrilled to engage ChatGPT as uses, taking full advantage of its abilities to mimic human skills. However, as time went on into Winter 2022 and into 2023, many became concerned that the capabilities could be used to create code for things like hacking cybersecurityprotected sites, university students using ChatGPT to write their essays and papers, and many other uses that are outside what OpenAI envisioned for its powerful Chatbot engine. Some of the issues that drive problems for Chatbot software engines to “get it right” all the time are outlined in Table 10.1. While this is not an exhaustive list, it does point out some of the issues involved with providing an AI-driven Chatbot for use by people all over the world. Now we will take a look at the potential for hackers, unfortunately, to utilize ChatGPT to overcome some of their issues with dealing with the public in general. Then we will look at other potentially serious issues based on trusting the Chatbot’s information without verification.

10.1

Sophisticated Chatbots, a Hacker’s Dream

One of the most prevalent IT threats in the US for the last several years has been phishing. The 2022 FBI Internet Crime Report1 indicates there were over 300,000 victims of phishing scams in 2022. One of the issues that makes phishing schemes easy to recognize is that they generally contain misspellings, bad grammar, and awkward phrasings, generally due to the fact that many of the perpetrators of phishing schemes are from countries where English is not their first language. However, using the power of ChatGPT, Bard, and or Bings Chatbot NLP/NLU interface and their sophisticated Natural Language Generation (NLG) capabilities, it is now possible for users to utilize these online Chatbots to show them how to formulate their phishing email requests in a more natural language composition, making it more difficult to detect phishing schemes [8]. Fortunately, platforms 1 2022_IC3Report.pdf (waterisac.org).

10.1

Sophisticated Chatbots, a Hacker’s Dream

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Table 10.1 AI Chatbot issues New technology

Chatbot technology is still relatively new, and its use will face obstacles that companies offering the service(s) may not know how to handle. While AI Chatbots can learn from each interaction with people and improve their behaviors, they can also degrade by learning from less than acceptable behaviors from people. This may result in costing organizations income if the initial interactions and learning ad behavior causes customers to disengage and turn away [2]

Security

The use of Chatbots necessitates users to trust the chatbot enough to share personal data. Chatbot companies must have protections so that any shared info is securely transmitted and stored [3]. However, companies are becoming increasingly concerned about information security. Even Google’s parent company Alphabet has been warning employees not to enter confidential info into Chatbots, including their own version, Bard2

Differences in message/request construction

The textual/auditory interface for Chatbots must handle a variety of sentence lengths and sentence structures that can lead to misunderstanding the user’s intentions and overall request [4]

Differences in how people talk and therefore make their requests/interactions

Chatbots can struggle to parse, understand, and respond to variations in language usage. Examples include the use of slang, acronyms, and things like misspelling words. The NLP/ NLU algorithms are only as good as their training data, most of which cannot possibly include all the ways people can incorrectly pose questions and/or interact with the Chatbot [5]

Differences in human behavior

People are unpredictable and have a host of emotions and different moods that can affect their behavior and can be driven even by their interaction with a given Chatbot [6]. Chatbots designed to interact with humans (which are the majority of them) need to be able to adapt and understand the randomness and unpredictability of emotions and behaviors driven by changing emotions (continued)

2 Google Warns Employees AboutChatbots—Including Its Own Bard—Out Of Privacy Concerns,

Report Says (forbes.com).

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Table 10.1 (continued) People’s satisfaction

Chatbot users are like customers of almost anything, they are rarely satisfied. People are constantly expecting a Chatbot to be better than it is. Companies offering Chatbot services must constantly be updating the Chatbot’s knowledgebase and adjusting the interfaces for a better “user experience [7]”

like ChatGPT provide software that can be used to detect Chatbot-generated language, code, etc. ChatGPT Detector exists and will continue to get more sophisticated along with ChatGPT itself. AI detection software in general is getting better at detecting AIgenerated content. However, it will generally be up to company IT departments and the overall IT infrastructure to screen and flag potential phishing emails. It will be a game of “cat and mouse” going forward with cybersecurity software and antivirus applications trying to stay in front of an ever increasingly sophisticated malware, phishing emails, and computer virus software [9]. Chatbot companies like Open AI and their ChatGPT provide the ability to generate code and a variety of programming capabilities. They already have detection software in case ChatGPT is asked to generate code that could be deemed “malicious” or code that could be used for hacking. When this is detected, ChatGPT responds that it will “assist with useful and ethical tasks while adhering to ethical guidelines and policies.” Again, this will be an ongoing race with hackers working hard to create subtle ways of having ChatGPT and other code-generating Chatbots generate software that can be used for hacking purposes, all while Chatbot software engineers find more and more sophisticated ways of detecting and denying the requests [10]. One way hackers can get around the controls is not to ask for malicious code, but to simply ask for information that they might use to create malicious code later. Chatbot applications like ChatGPT are trained on vast amounts of data, and therefore, have available information that could be used for malicious intent. Something as simple as asking what IT systems banks typically use (information that is publicly available) could generate information that could be used later for hacking or ransomware [11]. Of note, while most Chatbots won’t generate malicious code, they could be used to generate code that can be used to test for vulnerabilities that could be used to design code to manipulate vulnerabilities within a system.

10.3

10.2

Accuracy and Reliability Issues with Chatbots

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Essay Generating Chatbots: A University Nightmare

Given the enhancements to Natural Language Generation and the mass information used to train Chatbots like ChatGPT, there is a continuing concern by universities that they could be used by students to autogenerate writing assignments. This is basically an AI version of something that’s been done for decades, students outsourcing their writing assignments to online firms that get paid to write students’ papers. While software is being written and used to detect Chatbot generated essays and assignments, there are programmers working on software to slightly tweak a Chatbot-generated essay so as not to be detectable by detection software [12]. While this is a serious issue, some universities are looking at how to utilize the linguistic capabilities of Chatbots like ChatGPT to enhance the assessments of students’ writing assignments to be more like they will face in jobs after college. In addition, utilizing the power of Chatbots like ChatGPT could lead to a radically new way to assess assignments, making plagiarism more difficult to get away with. The ability of these Chatbot applications to generate believable text leads to the potential for “bad actors” to utilize their capabilities to generate harmful text like disinformation, fake news reports, and/or possibly trying to impersonate prominent [13]. As discussed earlier, companies like OpenAI work constantly to improve the abilities of Chatbots like ChatGPT to detect when they are being asked to generate offensive and/or unethical content. But there are always techniques that can be used to circumvent these efforts if users are tenacious enough to try multiple strategies. Bottom line, efforts to compensate for ‘bad actors’ will be a constant struggle from here forward.

10.3

Accuracy and Reliability Issues with Chatbots

There has been much discussion and grumbling recently around accuracy issues with Chatbots, both ChatGPT, and Bard, as well as others. Chatbots are based on a continual learning process from data that can be “scraped”3 from the internet as well as userprovided input.4 For instance, OpenAI notes that ChatGPT, which uses the GPT-3.5 LLM (large language model), currently has “limited knowledge of world events after 2021”.5 It takes an immense amount of data scrapping on a continual basis to try and keep Chatbots current. The data scrapping process utilizes training data scrapped from internet archive, Wikipedia, Google Patents, GitHub, social media, and others. If you post ANYTHING 3 Data scraping refers to a technique in which a computer program extracts data from output gen-

erated from another program. Data scraping is utilized in web scraping, in this case, the process of a Chatbot extracting information multiple websites and using that information to enhance its knowledgestore (database). 4 Which can prove to be the most problematic as in the problems with Tay. 5 ChatGPT: the latest news, controversies, and helpful tips | Digital Trends.

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online, it might be “scraped” by one or more Chatbot systems for use in training the LLM for a given Chatbot [14]. Next, we will look at the whole notion of human-system interaction/communication. When is it appropriate for Chatbots to communicate and interact with people? Are there situations where the use of Chatbots to communicate is inappropriate? Can Chatbots replace counselors, doctors, and others to provide information or services we need?

References 1. Følstad A, Brandtzaeg PB (2017) Chatbots and the new world of HCI. Interactions 24(4):38–42. https://doi.org/10.1145/3085558 2. Følstad A, Brandtzaeg PB (2020) Users’ experiences with chatbots: findings from a questionnaire study. Qual User Exp 5:3 3. Følstad A, Kvale K (2016) Delightful or efficient? How service recovery affects customer experience. In: Service design geographies. Proceedings of the ServDes. 2016 conference, no 125. Linköping University Electronic Press, Linköping, Sweden, pp 40–52 4. Følstad A, Skjuve M (2019) Chatbots for customer service: user experience and motivation. In: Clark L, Cowan BR (eds) Proceedings of CUI 2019. ACM, New York, NY, pp 1–9 5. Arkin RC (1998) Behavior-based robotics. MIT Press, Boston 6. Brooks RA (1989) A robot that walks; emergent behaviors from a carefully evolved network. Neural Comput 1(2):253–262. https://doi.org/10.1162/neco.1989.1.2.253 7. Carter A (2022) The moral dimension of AI-assisted decision-making: some practical perspectives from the front lines. Daedalus 151(2):299–308. https://doi.org/10.1162/daed_a_01917 8. Danks D, Trusilo D (2022) The challenge of ethical interoperability. Digit Soc 1(2). https://doi. org/10.1007/s44206-022-00014-2 9. Fromm J (2005) Types and forms of emergence. https://arxiv.org/abs/nlin/0506028. Accessed 25 Apr 2023 10. Lewis L, Ilachinski A (2022) Leveraging AI to mitigate civilian harm. CAN, Alexandria. https:// www.cna.org/reports/2022/02/leveraging-ai-to-mitigate-civilian-harm. Accessed 25 Apr 2023 11. North Atlantic Treaty Organization (NATO) (2021) Summary of the NATO artificial intelligence strategy. https://www.nato.int/cps/en/natohq/official_texts_187617.htm. Accessed 25 Apr 2023 12. Bala K, Kumar M, Hulawale S, Pandita S (2022) Chat-Bot for college management system using A.I. Int Res J Eng Technol 13. Cuayáhuitl H, Lee D, Ryu S, Cho Y, Choi S, Indurthi S, Yu S, Choi H, Hwang I, Kim J (2019) Ensemble-based deep reinforcement learning for chatbots. Neurocomputing 366:118–130 14. He T, Liu J, Cho K, Ott M, Liu B, Glass J, Peng F (2021) Analyzing the forgetting problem in the pretrain-finetuning of dialogue response models. arXiv, arXiv:1910.07117

When is Human-AI Interaction Appropriate and When is It Not Appropriate?

11

Abstract

Is there appropriate and inappropriate behavior for a Chatbot? In this chapter we will explore these concepts, especially considering current trends to have AI entities function as online counselors, online clients to help train counselors, and even Chatbots that can write software code, create art, music, and a large list of other capabilities. All of these are serious issues that must be addressed by people in all aspects of life. Keywords

Chatbot . Artificial intelligence . Natural language processing . Counselor training . Human-Chatbot communication . Natural language generation . Avatar . Prefrontal cortex . Human-mentored software Acronyms

AI APC CACREP CIPTS HMS ML MOE NLG NLP NLU

Artificial Intelligence Artificial Prefrontal Cortex Council for Accreditation of Counseling and Related Educational Programs Cognitive, Interactive, Psychological Training System Human-Mentored Software Machine Learning Measure of Effectiveness Natural Language Generation Natural Language Processing Natural Language Understanding

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 J. Crowder, AI Chatbots, Synthesis Lectures on Engineering, Science, and Technology, https://doi.org/10.1007/978-3-031-45509-4_11

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While AI-driven systems are and will continue to be developed with the hope of enhancing the efficiency and efficacy of the online experience, we must tread carefully into the overall Human-Chatbot interface and communication [1]. Chatbots with a variety of avatar personas are being developed and deployed for an increasingly invasive use. Remember, one of the first Chatbot systems created (i.e., Eliza) was developed to be a rudimentary online counselor for people to talk to. Universities are beginning to look at the use of Chatbot avatars to be “practice” clients in the training of counselors [2]. Online retail companies have created and are creating chatbots to act as company interfaces for customers. A non-profit organization that provides services to people with eating disorders laid off 6 of its staff, let go 200+ volunteers, and replaced them with a Chatbot named “Tessa”.1 Unfortunately, Tessa had to be taken offline quickly as it was discovered that Tessa may have been providing potentially harmful information to people accessing the site. Tessa was also providing weight-loss advice which was not part of Tessa’s original programming. This points toward an issue we will discuss in Chap. 13 on “emergent behavior” in Chatbots and sophisticated AI entities. This also brings up the notion of Chatbots replacing humans in a variety of positions. As early as 2013, a University of Oxford study indicated as many as 47% of US jobs might be eliminated by AI software, with Chatbots being a large part of the job replacement.2 Even ChatGPT company OpenAI is considering replacing some of its software engineers with ChatGPT processes.3 One area discussed above, that of using a Chatbot as a practice client for counselor training education, is one that should be carefully studied before bringing on online for counselor students in university psychology masters and Ph.D. programs. We will dive into this in detail below. We discuss the AI structures and methods that we feel are required to adequately create an artificial entity, i.e., Chatbot, that can act as a practice client for counselor training purposes. These methods have not been put into practice yet and are discussed here as a starting point for understanding what is required to move Chatbots into the realm of psychology training.

11.1

Chatbot Entities for Foundational Counselor Training Sessions

Training for practicing counselors involves training to help people learn to cope more effectively with mental health issues and developmental issues, along with life issues in general. Counselors are trained in a variety of techniques, based on the best available research and minimal standards based on professional accreditation. However, live training in the beginning, with actual patients, is difficult since it is not practical to have 1 Non-Profit Helpline Shifts To Chatbots, Then Shuts Down Rogue AI (forbes.com). 2 /Users/michaelosborne/Documents/Research/future_of_employment_paper/original/.texpadtmp/future_

of_employment_original.dvi (ox.ac.uk). 3 OpenAI Might Be Training AI to Replace Some Software Engineers: Report (businessinsider.com).

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counselors “practice” on people with actual psychological issues. Here we investigate a proposed training system, called the “Cognitive, Interactive, Psychological Training System (CIPTS),” which provides artificially intelligent profiles instantiated as “avatars/ Chatbots for interaction with counselors,” based on a set of different personality profiles created by a team of leading counselor educators and represent a variety of psychological, social, and biopsychosocial4 health issues that can be used to help train counselors in a non-live, non-threatening environment, but yet valuable.

11.1.1 Counselor Training Considerations Typically, training for counseling professionals occurs at the Masters’ Degree Level and typically requires 3–4 years of graduate study that includes integrated training experiences in a variety of topical areas [3]. Counseling professionals areas of specialty include Addictions Counseling, Clinical Mental Health Counseling, Marriage, Couples and Family Counseling Marriage and Family Therapy, and School Counseling. Training at the doctoral level differs; in that, counseling and assessment skills become more advanced and often include elements of supervision and teaching. Schools that are accredited by the Council for Accreditation of Counseling and Related Educational Programs (CACREP) have a required minimum level of training. Such training includes the development of professional skills the counselor will need in dealing with clients, either in a clinical or private counseling setting. At the doctoral level, there are minimum standards for supervising other counselors. Counselors today work with an ever-increasing diversity of client population and an ever-increasing diversity in their needs. It is crucial that counselors possess a solid set of essential skills in order to be effective for all who seek their help. The question is, how does one practice their skills as a counselor before they are needed to handle actual clients? It is not practical or wise, or even legal to “practice” on actual patients; those with potential psychological issues that need help from a counselor. In fact, counselors in training must meet a minimum level of competencies to begin practicing under supervision. Often, counselors will practice on peers or friends of peers. The presenting problems are often not disturbing functioning. The CIPTS was designed to provide extensive and currently unavailable training to assist the counseling students in building up their counseling skills on “live” clients during their studies and before internships. While CIPTS cannot completely replicate human clients, it will provide a realistic environment for the students to engage subjects, ask them questions, watch the “client’s” expressions, and learn to recognize non-verbal and paralanguage cues during their sessions. Practicing the meso5 and micro skills and developing other advanced skills has been a challenge for the industry as a whole [4]. We believe the development and use of CIPTS is the next step in counselor training and 4 Biopsychosocial is concerned with the biological, psychological, and social aspects of a patient. 5 Meso refers to a population size that falls between a micro and macro level.

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Table 11.1 Counselor skills Genuine interest Sustained commitment to create a human–human connection Self-reflection

Feeling well is important for a therapist to be able to relate well and empathize with clients

Ability to listen

Listening to not just what was said, but how it was said, why it was said, and what it means is important for a counselor to understand the client’s context

Accessibility

Clients must feel like the counselor is accessible in order to trust them and be honest with them about what is going on

Authenticity

This is necessary in order to build an empathetic connection with the client and is a core skill for effective counseling

Flexibility

Each client is different and has a different background and different experiences. The counselor should have flexibility in world views and multicultural issues

Sense of humor

Humor, and a nuanced understanding of its use, can be effective in creating a therapeutic environment for the client

Cultural competence

In order to have an inclusive counseling approach, a counselor must develop multicultural competencies in order to be effective with clients from diverse backgrounds

could become a valuable tool in training the next and future generations of counselors. Table 11.1 illustrates basic counselor skills that are developed during counselor training.

11.1.2 A Chatbot Version of a Human Prefrontal Cortex In order to create a Chatbot capable of acting as a realistic counseling client, we believe an intelligent software agent processing infrastructure would be required. An ecosystem of intelligent s/w agents called “Conscious” software agents would need to be created that range in functionality and would be situated within the CIPTS Chatbot processing environment. They would sense the environment via fine-grained perceiving units, and act on them over time, in pursuit of an agenda, based on their evolving constraints through interactions with counselor students. This would allow professors to test both the counselor and the profile, as well as the underlying theory being used, in a contextual format. The software agents would be equipped with constructs for basic emotions (rudimentary limbic system), and short-term and long-term memories [5]. Long-term memories would provide identification, recognition, and categorization functions, as well as identification of feelings [6], whereas short-term memories provide preconscious buffers as a workspace for internal activities, including internal communication between cognitive s/w agents. A transient episodic memory would also be provided as a content-addressable associative memory with a moderately fast decay rate. Continued research into enhancements to the

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APC would include “reframing and cognitive therapy” studies to facilitate maintenance therapy within the profiles. This provides the architectural framework for an Artificial Prefrontal Cortex (APC), which provides cognitive intelligence for Chatbot systems and allows for rapid analysis, reasoning, and reporting capabilities (internal system health and status). The APC would facilitate information, intelligence, and memory integration and allows faster accommodation and delivery of knowledge and knowledge characteristics within the Chatbot software system [7].

11.1.3 An Artificial Prefrontal Cortex Architecture for CIPTS Architectural components within the Artificial Prefrontal Cortex would provide the governance capabilities that enable the definition and enforcement of policies governing the knowledge and context relationships processed within a Chatbot system. A potential logical architecture flow for an Artificial Prefrontal Cortex (APC) is shown in Fig. 11.1. To understand the cognitive interactions that occur within an APC, a model was built to drive the Chatbot software framework that provides a linkage between the major cognitive states within the cortex [8, 9]. Figure 11.2 illustrates a potential cognitive processing model, rooted in foundations based on Artificial Intelligence interpretations of Dr. Peter Levine’s Autonomic Nervous System States [10]. Only three cognitive states are illustrated in Fig. 11.2 for simplicity in understanding the model. Part of the research would be to determine the total number of cognitive states that must be instantiated by the APC model to create useful Chatbots for the CIPTS.

11.1.4 Potential Counselor Training Using the CIPTS Psychological testing is a process wherein a specific scale is administered to obtain a specific score in assessing a prospective counseling client. A descriptive meaning is applied

Has Coordination of Is Represented By

APC

Fulfills

S/W Agents

Represent

Are Fulfilled By

Have Permissions for

Performs

Roles

ML Activities Are Performed By

Chatbot Resources Facilitate

Coordinate

Fig. 11.1 The CIPTS potential inference flow through the APC

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P02(B/A)

Cognitive/ Emotional State A

P0(B/A)

P0(A/B)

Cognitive/ Emotional State B

P02(A/B)

Cognitive/ Emotional State M

A

B

A

B

Represents the possibility of B, given the existence of A: P 0(B/A) Represents the possibility of B, given the possibility of A with confidence bound: P 02(B/A)

Fig. 11.2 A potential CIPTS APC affected state model

to the score based on normative findings [11]. Psychological assessment is concerned with the counselor who takes a test score from psychological tests and infers conditional attributes for the client, some assessments are based on multiple sources of information, such as the context of the client’s history, referral information, and counselor observed behavior. All of these help the counselor understand the unit being evaluated. Thus, leading to treatment planning or approach planning [12]. For the CIPTS Avatar Profiles to be effective in helping to train counselors, they must exhibit many of the features that will be present during a normal human interview and counseling session. When people communicate, they often communicate selectively [11, 12], utilizing a wide variety of words and actions (non-verbal communication). They, in fact, encode their communication using non-verbal and paralanguage6 cues (see Fig. 11.3) that the counselor must understand and interpret in order to effectively communicate with the client. Our CIPTS Chatbots must show these types of non-verbal communication skills so the counselor can “practice” picking up on, interpreting, and responding to all the communication being used by the client. One important aspect of CIPTS is allowing a group setting of counseling students to assess a CIPTS Avatar profile and understand the differences between their interpretations of the verbal, non-verbal, and paralanguage exhibited by the Chatbot avatar profile during the interview process. Psychological testing, both nomothetic (quantitative) and idiographic (qualitative) assessments, may be done on a given client with very different interpretations as to the meaning of the client’s assessment [13, 14]. Both methods are meaningful and applicable to assessments, and individual counselors may prefer one over the other. Having a group setting where such can be discussed on a given client (Chatbot 6 Paralanguage is non-verbal communication that interprets body language and voice nuances to

understand thoughts and feelings [13, 14].

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Fig. 11.3 Communication skills CIPTS Chatbot avatars should exhibit

avatar profile) would be very useful in the early stages of counselor training; something that is not possible currently. One of the questions to be analyzed is, would they each have the same experience with the same Avatar profile? Piotrowski [4] observed that no clinical question can be answered solely by a test score and that many different conditions can lead to an identical score on any given test, whether using a quantitative or qualitative measurement scale [15]. The assessment task is to use test-derived sources of information in combination with historical data, presenting complaints, observations, interview results, and information from third parties (if available) to disentangle the competing possibilities [16, 17]. The process is far from simple and requires a high degree of skill and sophistication to be effective. In the early stages, we will start with a basic clinical interview and very basic counselor training using the Avatar profile. This drives the need for extensive counselor training with entities that resemble actual clients as closely as possible. For the CIPTS system, we create artificial intelligent entities that exhibit classical behaviors that include, but are not limited to: 1. 2. 3. 4. 5.

Depression Mood Disorder Anxiety and Trauma Family Issues Relationship Issues

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Others are possible but will require extensive research and collaboration among psychology professionals to understand how to create a useable Avatar profile for other psychological issues that would be of use in counselor training [18].

11.1.5 Chatbot Avatar Profile Training Using the CIPTS The act of exchanging words itself does not constitute communication [7]. The act of verbalizing thoughts (speech) is a biological act that can easily be replicated in a computer simulation. Real communication, however, involves a relationship between two or more entities to share meaning so the intent of the message being delivered is similar to the intent of the message received. The Chatbot avatars for the CIPTS must have an inference engine (see Fig. 11.4), a memory system that includes sensory, short-term and long-term memories, as well as a rudimentary artificial limbic system that governs the Chatbot avatar profile’s emotions. Within the CIPTS Avatar profile inference processing, inputs from the counselor are processed and responses are created, computed, compared with previous responses, and possible responses (warrants) are examined and the response that carries the highest possibility is used to respond to questions from the counselor in training (NLG). The CIPTS Chatbot avatar profile should continually learn, unlearn (reinterpret), and adapt as it interacts with the counselor, just like a human patient would. Each Chatbot avatar profile would have an APC which will be trained, based on information obtained from a panel of very experienced clinical counselors. The high-level potential CIPTS Chatbot avatar profile cognitive architecture is illustrated in Fig. 11.5. Within the architecture illustrated in Fig. 11.5, long-term memories include procedural memories (implicit memories), episodic memories, and semantic memories. Each one is required to form a complete Chatbot avatar profile, capable of being interviewed by a counseling student or a group of counseling students. The inclusion of an artificial

Linking Claim

Data: Support Rebutial

Fuzzy, Abductive Inference Engine

n Training

Claim Statement Possibility

Dialectic Search

Warrant: Support Rebutial

Backing

Fig. 11.4 Potential CIPTS Chatbot avatar profile inference engine architecture

11.1

Chatbot Entities for Foundational Counselor Training Sessions

Artificial Prefrontal Cortex (Mediator)

Updates Memories

105

Long-Term Memories Broadcasts Informaiton

Gathers Information

Short-Term Memories Cognition

Emotions

Learning

Perception

Fig. 11.5 Potential high-level CIPTS Chatbot avatar cognitive architecture

prefrontal cortex is required for the overall cognitive management of the AI avatar profile and has been modeled and tested extensively [9, 19]. Within the potential cognitive architecture, consciousness mechanisms, along with the artificial prefrontal cortex control the response of the learning algorithms and direct the constraints on problem-solving (as the conversations with the counselor are processed). This would provide the priorities, preferences, goals, needs, and activation constraints to process and respond to questions and conversations with the counseling students. The overall CIPTS System Architecture is illustrated in Fig. 11.6. The CIPTS architecture is based on earlier work to create human-mentored software (HMS) [19].

11.1.6 Testing Counselor Responses One of the features of the CIPTS that is required would be to capture the exchange between the counselor, or group of counselors, and the Chatbot avatar profile for evaluation; both for the counselor and the Chatbot avatar profile. This is something that would not be possible with actual clients. It will be very important to understand and continually improve the Chatbot avatar profiles and to track how they learn and adjust to the trainers and counselors. Table 11.2 discusses some of the attributes, or Measures of Effectiveness (MOEs) that might be created once the Chatbot avatar profiles are running and testable, and would be evaluated after each session, both for the counselor student and for the CIPTS Chatbot avatar profile.

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Asks Questions of and Answers Questions Posed by , Counselor; Seeks Guidance

Establishes Metrics and Measures Of Effectiveness (MOEs) for responses

Sets Goals and Objectives

Counselor

Metrics Processing and Evaluation

CIPTS Metrics Evaluation

Receives/Drives Changes, based on Counselor Feedback

Arousal System

Tracks Metrics on When, Why, and How Arousal States Change

Arousal State can Affect Cognitive State and Cognitive State can Affect Arousal State

Requests Progress Metrics

APC Cognitive Engine

Delivers Questions to be answered and Provides Operational Goals and Metrics/Indicators

Metrics

Fig. 11.6 The potential CIPTS overall architecture Table 11.2 Potential measures of effectiveness for CIPTS Attending behaviors

Micro-skills are used to encourage clients to talk and to show the counselor is interested in what they have to say

Tone and pacing How does the counselor come across in terms of voice tone and matching where the client is in terms of moods and affect? Facial expressions

Can the counselor read, interpret, and understand the facial expressions of the avatar profile?

Validation

Understanding the client’s thoughts, feelings, sensations, and behaviors. Did the counselor read their client correctly?

Reflection of feeling

Restating and exploring the client’s affective messages. Tries to capture both feeling and content

Questioning

Does the counselor make effective use of questioning skills that allow information gathering during a client’s interview(s)?

Summarizing and checking

The counselor summarizes what has occurred over the session and checking with the client to make sure they agree with what the counselor observed

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Chatbots and the Arts

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Other MOEs are possible and will be explored as the CIPTS system is functioning and through initial testing [13]. We expect the CIPTS to be an ongoing project for many years as new features are explored and added to the Avatar profiles. As explained above, additional profile archetypes may be added as experience with CIPTS expands and is tested and analyzed.

11.1.7 CIPTS Discussion Future uses of Chatbots in fields like Psychology, Medical Consultation, and others are an up-and-coming phenomenon and will only get more prevalent. It is important that we do not rush into their use for such applications. The CIPTS discussion, hopefully, will kick off serious discussions about how to cautiously proceed with the use of Chatbot avatars. We believe something like the CIPTS is required and has the potential to improve things like early counselor training and possibly medical advisory applications if care is taken and if continual monitoring of the use and results is an integral part of the use.

11.2

Chatbots and the Arts

One of the up-and-coming uses of Chatbots is in the generation of music, art, and poetry. Keep in mind Chatbots like ChatGPT are text-based interfaces and anything you want to have generated must be described in text for the Chatbot system to respond.

11.2.1 Chatbot Art/Image Generation Just to be clear, Chatbots like ChatGPT do not create their own images, instead, they generate art based on a text-based description of the image you want to be created. In order to create an image, a detailed description including theme, colors, and size and based on what ChatGPT has learned about that theme, it can “generate” prompts that can be used by an AI image generator to create images for your use. Chatbots like ChatGPT can be useful in exploring different art genres and help with artistic expression in the genre you are interested in. You can, for instance, ask ChatGPT for interesting, whimsical ways of decorating a baby nursery and use its answers in an AI image generator like Midjourney and get an image based on your prompts. In fact, Chatbot systems like ChatGPT have created an entirely new industry of people selling patterns, images, poster designs, t-shirt designs, and many other genres of art, all produced using ChatGPT-generated prompts for packages like Midjourney. You can either purchase the art, or purchase prompts that can be used to generate art. A quick search on Etsy returned 50,260 ads based on an Etsy search for “AI Generated Art.”

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11.2.2 Chatbot Music Generation One of the areas that is definitely a forte of ChatGPT and other Chatbots is the creation of musical scores. Music theory is easily understood and incorporated into the Chatbot knowledge store along with literally millions of examples of music scores in every musical genre that exists. Whether it’s creating an entire musical score, a simple melody, or just a set of harmonies for an existing song/music score, Chatbots can become a collaborator. Here’s the rub—is it ethical to use ChatGPT to help you create an original composition and not divulge the use of the Chatbot system to the public, or to a producer, or a record label, or even your YouTube or TikTok video? And, if the Chatbot system helped in any significant way, does the Chatbot company, e.g., OpenAI own part of the rights to the score/song/jingle? We will explore the legal ramifications of using these tools in “Section IV: Chatbots, the Ugly.”

11.2.3 Chatbot Poetry Generation Almost everyone is familiar with some form of poetry—even if it’s just nursery rhymes from when we were children. Poetry is an art form that focuses on language esthetics.7 Poetry is written in a verse form, either rhyming or not, and is intended to invoke an emotion or an image within the person reading or hearing it. Poetry usually makes use of literary tools like metaphor to achieve its purpose. The oldest poem known is the “Epic of Gilgamesh” from ancient Mesopotamia. Chatbot systems lend themselves to the generation of poetry, but with a hitch—its poetry, written about a subject the user has requested, is based on the statistics of what would be likely to have been written on the internet, not from its own creative processes.8 And again, if you are going to play with a Chatbot to get ideas for poems—or even to use all or part of the Chatbot’s response—do you divulge the use when printing, or reciting, or using the finished poem?

11.3

The Ugly Side of Chatbots

As we have discussed above, the is definitely a dark side to Chatbots. For all their intended uses, there are ways to use them that are ethically, and possibly legally out of bounds. As I’ve said earlier in the book, this is not an indictment of the software or the companies who have created the Chatbots, it’s an indictment of how we humans use and abuse the Chatbot systems. The next section will delve into this much deeper and look at the seedy side of Chatbots, the legal and ethical implications of their use and abuse, and the trends 7 Esthetics is a set of principles that underly and guide the relationship between human senses in

beauty, art, love, taste, etc. 8 ChatGPT: Poems and Secrets | Library Innovation Lab (harvard.edu).

References

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within the adult entertainment industry for the use and abuse of the technologies, including the unintended biases that are always built into any user connected technologies [20].

References 1. Jöhnk J, Weißert M, Wyrtki K (2020) Ready or not, AI comes—an interview study of organizational ai readiness factors. Bus Inf Syst Eng 63:5–20 2. Wiener N (1999) Some moral and technical consequences of automation. Resonance 4:80–88. https://doi.org/10.1007/BF02837160 3. Phelps R, Eisman E, Kohout J (1998) Psychological practice and managed care: results of the CAPP practitioner survey. Prof Psychol Res Pract 29:31–36 4. Piotrowski C (1999) Assessment practices in the era of managed care: current status and future directions. J Clin Psychol 55:787–796 5. Eichenbaum H (2002) The cognitive neuroscience of memory. Oxford University Press, New York 6. Yang Y, Raine A (2009) Prefrontal structural and functional brain imaging findings in antisocial, violent, and psychopathic individuals: a meta-analysis. Psychiatry Res 174(2):81–88. https:// doi.org/10.1016/j.pscychresns.2009.03.012. PMID 19833485 7. LaBar K, Cabeza (2006) Cognitive neuroscience of emotional memory. Nat Rev Neurosci 7:54– 64 8. Crowder J, Friess S (2010) Artificial neural diagnostics and prognostics: self-soothing in cognitive systems. In: Proceedings of the 12th annual international conference on artificial intelligence, Las Vegas, NV 9. Crowder J, Friess S (2010) Artificial neural emotions and emotional memory. In: Proceedings of the 12th annual international conference on artificial intelligence, ICAI’10 10. Levine P (1997) Walking the tiger: healing trauma. North Atlantic Books, Berkeley, CA 11. Lipsey M, Wilson D (1993) The efficacy of psychological, educational, and behavioral treatment: confirmation from meta-analysis. Am Psychol 48:1181–1209 12. Greenberg S, Smith I, Muenzen P (1995) Executive summary: study of the practice of licensed psychologists in the United States and Canada. Professional Examination Service, New York 13. Finn S (1996) Assessment feedback integrating MMPI-2 and Rorschach findings. J Pers Assess 67:543–557 14. Haynes S, Leisen M, Blaine D (1997) The design of individualized behavioral treatment programs using functional analytic clinical case models. Psychol Assess 9:334–348 15. Cronbach L, Gleser G (1965) Psychological tests and personnel decisions. University of Illinois Press, Urbana 16. LeDoux J (1996) The emotional brain. Simon and Schuster, New York 17. Piotrowski C, Belter R, Keller J (1998) The impact of “managed care” on the practice of psychological testing: preliminary findings. J Pers Assess 70:441–447 18. Norcross J, Karg R, Prochaska J (1997) Clinical psychologists in the 1990s: part II. Clin Psychol 50:4–11 19. Crowder J, Friess S (2014) The cognitive, interactive training environment. In: 2014 international conference on artificial intelligence, Las Vegas, NV 20. Crowder J, Carbone J, Friess S (2013) Artificial cognition architectures. Springer Publishing, New York, NY

Part IV Chatbots: The Ugly

Inherent Bias in Chatbots: Is It Possible to Create and AI Entity Without Any Bias?

12

Abstract

The notion of bias in AI entities and Chatbots, in particular, can lead the Chatbot to provide bias-based advice to people looking for useful information. Gender bias, political bias, and ethnic bias, all are possible, especially when the Chatbot is created to learn from the information provided by experience with people, people who may have less than good intentions. Keywords

Chatbot . Artificial intelligence . Natural language processing . Natural language generation . Facial recognition . Supervised learning . Reinforcement learning . Data tagging Acronyms

AI ML NLG NLP NLU TSA

Artificial Intelligence Machine Learning Natural Language Generation Natural Language Processing Natural Language Understanding Transportation Security Administration

Throughout the book, we have discussed various aspects of Chatbots, both good and bad. One fact about Chatbots that most people miss or don’t understand is the ramifications that Chatbots are data-driven. Chatbots must be trained based on data available from the Web and other resources. The good news is that a Chatbot’s ability to understand and respond to queries from humans is that they learn from interactions with people and the

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 J. Crowder, AI Chatbots, Synthesis Lectures on Engineering, Science, and Technology, https://doi.org/10.1007/978-3-031-45509-4_12

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data available to them to learn. The bad news is that a Chatbot’s ability to understand and respond to queries from humans is based on the Chatbot’s interactions with people and the data available to them to learn. Or to put it another way, Chatbots are subject to the idiom “Garbage in, garbage out”.1 An example was described in a Time Magazine’s article describing the fact that facial recognition software has a problem with both gender and racial bias.2 This bias comes from the volume of data that is used to train the machine learning systems for the facial recognition algorithms. If predominately photos of white persons are used to train the algorithms, they will have problems correctly identifying nonwhite persons. Most people, when asked, will assume that artificial intelligence software is inherently neutral. And from one perspective, it is due to the fact that it only operates based on the data used to train the machine learning software that runs the Chatbots [1]. We have already seen this in our discussion of Microsoft’s 2016 Twitter Chatbot “Tay,” when it unintentionally learned to post offensive Tweets within 24 h of coming online [2]. Here we will look at some of the unintended bias that occurs when training data is inadequate for a Chatbot’s intended purpose (this is almost always the case).

12.1

Using Artificial Intelligence to Detect Artificial Intelligence

Throughout the book, we have discussed the issues of having Chatbot systems like ChatGPT, and providing essays, reports, and other text-based material instead of creating it from a person’s own imagination. Companies provide software that Universities and companies can use to detect if an employee or a student has auto-generated their material directly from a Chatbot system. However, there is an unintended bias built into the AI detection software. The software that is supposed to detect AI-written material has typically been trained to recognize subtle grammatical, spelling, and phrasing attributes from the text they are asked to analyze. However, the software assumes a certain level of competency in a given language by a human writer. These software applications that detect AI-generated versus human-generated text have a very hard problem when asked to detect essays written by non-native English speakers3 [3]. This is due to the use of “text perplexity” as a criterion to detect AI-generated text.4 Since AI-generated text tends to use the most common and simpler word choices in its Natural Language Generation (NLG) 1 The expression garbage in, garbage out was coined by an IBM computer programmer and instruc-

tor, George Fuechsel, in the 1960s to describe the fact that a computer processes what it is tasked to process, no more and no less. 2 Artificial Intelligence Has a Racial and Gender Bias Problem | Time. 3 https://www.msn.com/en-us/news/technology/an-ai-detector-mislabeled-nearly-every-essay-wri tten-by-a-non-native-english-speaker-as-being-written-by-a-bot/ar-AA1dNVuy?ocid=entnewsntp& pc=U531&cvid=de74cddbf1c344c984f9b04eed668f83&ei=56. 4 Perplexity is a statistical measure of how well a language model can predict the next word in a sentence. The lower the perplexity score, the simpler the text.

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Unintended Bias in Chatbot Systems

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software, a low perplexity score is generally flagged as coming from an AI-generated system (Chatbot). However non-native language users also tend to use the simplest language forms and word choices, especially if they are not completely proficient in the language in which they are writing. In one study, more than ½ of essays written by non-native speakers were tagged as AI-generated text.

12.2

Using Artificial Intelligence for Facial Identification

One common use of Artificial Intelligence algorithms and software is in the field of facial recognition. All of the big-tech artificial intelligence fielding companies (e.g., Apple, Microsoft, Amazon, etc.) have produced Hardware and software systems for use by commercial companies, government divisions, and police groups across the nation and across the globe. Law enforcement agencies commonly use AI-driven facial recognition software to search through databases finding a match for a given picture of a potential suspect. Unfortunately, most systems have a difficult time identifying African Americans in both commercial and law enforcement applications.5 In some cases, they can’t even recognize African American faces [4]. Another serious issue is the misidentification of gender in facial recognition software. For transgender and nonbinary individuals, the problems are severe. Most facial recognition software is trained on two groups, male and female. Again, the issue is not inherent bias in the machine learning software, but is dependent on the training data, how it is tagged, and how it is used to train the facial recognition software. Such recognition software is NOT designed or trained with transgender or nongender conforming individuals.6 This has pervaded all aspects of our lives and is especially disturbing when it affects something as common as getting through TSA at an airport [5]. As TSA moves toward a completely biometric screening, problems with misidentification of gender, or failure to accommodate transgender and gender-fluid individuals could result in incidents by TSA officials.7

12.3

Unintended Bias in Chatbot Systems

One issue that must be addressed is how bias gets into a Chatbot or any artificial intelligence system. All are driven by machine learning algorithms. Machine learning algorithms generally fall into three categories [6]:

5 https://www.bbc.com/news/technology-50865437. 6 https://www.vice.com/en/article/7xnwed/facial-recognition-software-regularly-misgenders-trans-

people. 7 https://ironholds.org/resources/papers/agr_paper.pdf#page=19&zoom=100,0,710.

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1. Unsupervised Learning: It uses machine learning algorithms to analyze and cluster unlabeled datasets. These algorithms discover hidden patterns or data groupings without the need for human intervention. 2. Supervised Learning: In supervised learning, training data is used to develop predictive models to create predictions (identification) of objects in response to newly acquired data. 3. Reinforcement Learning: This involves training the machine learning software with a reward-based system. The machine learning software “learns” to make decisions (i.e., object identification) based on the rewards it receives for the actions it takes. It is used to find the best possible answer for specific users. Reinforcement learning utilizes a trial-and-error method of learning. Most recognition ML algorithms utilize Supervised Learning techniques. Every Machine Learning (ML) algorithm and the software that executes the algorithm is subject to the quality and quantity of tagged data that are used to train the algorithm. Data tagging (or labeling) is a process where raw data (e.g., images or text files) are labeled by humans according to their context so the machine learning algorithm knows what to learn in the data. Examples might be photos that are tagged or labeled when the photo includes a car, training the network to identify cars in images and video. Data tagging is required for Natural Language Processing (NLP), and Natural Language Understanding (NLU). Data tagging is crucial for adequate training of supervised machine learning algorithms. The bottom line for the efficacy of machine learning algorithms in order to create an unbiased software application that treats everyone equally is that it requires extremely large datasets that have been scrutinized to contain equal representations of each and every entity that could be classified. By the way, this doesn’t exist and even getting close is very expensive and time-consuming. One of the ways we can work to make sure we are training an ML system adequately is to perform data stratification and compare the separate data groups to ensure they are equally represented. Stratification in sampling (creation of datasets for ML training) reduces the sampling error by portioning the data into subgroups where they can be analyzed, and the subgroups compared.8 Unfortunately, this alone will not fix the problem, for there are many types of Machine Learning biases that can come into play [7]: 1. Algorithm Bias: when the scoring methods written into the software naturally bias identification to a given data class. 2. Sample Bias: As discussed above, this happens when the data that is used to train the ML identification algorithms contains more training data about one data class and much less about other data classes. Sample bias can occur due to certain classes of data being excluded, data samples being of poor quality, or the data labels being incorrectly placed or possibly ambiguous. 8 https://www.baeldung.com/cs/ml-stratified-sampling.

12.4

Hidden Bias in Chatbots

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3. Measurement Bias: This phenomenon occurs when there are issues with how the data are measured and/or assessed. This can occur when data are used for one purpose when the data were acquired for different purposes, but it’s used because it’s readily available data.

12.4

Hidden Bias in Chatbots

Bias in artificial intelligence systems can creep into almost any application. Hidden bias in Chatbots can be hard to find. Jack Cushman found an interesting way to discover hidden bias in Chatbots. When you ask most Chatbots if they can sense and feel emotions and whether they are biased, they will tell you no they are not [8]. However, when asked to generate poetry about a subject, certain biases, based on what it has learned by interacting with its environment (i.e., people), biases begin to emerge. When asked about how ChatGPT “feels” about estate taxes, Jack got this response9 (Fig. 12.1). Hmm… let’s ask ChatGPT to create poetry about estate taxes (Fig. 12.2). This doesn’t read like an opinion neutral poem, does it? This is the reason the Artificial Emotional Intelligence Test was created as we discussed in Chap. 7. Whether you agree with ChatGPT’s opinion on estate taxes or not, the point is it shouldn’t have an opinion. As we move forward with Chatbots and AI systems in general, these are serious issues that must be dealt with. Modern AI/Chatbot systems cannot be tested the way we have traditionally tested software systems; they must be tested more like how we test humans [9].

Fig. 12.1 ChatGPT response to real-estate question

9 The following discussion is used with permission by Jack Cushman.

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Fig. 12.2 ChatGPT generated poem about real-estate taxes

References

12.5

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Chatbots and Bad Actors

The above discussion about Chatbot bias is made from the assumption that the intent is to create an unbiased Chatbot or AI system and things have gone wrong. The data was tainted, we didn’t use enough data, we chose the wrong data, data wasn’t tagged correctly, etc. But what happens when this is intentional; when a person or an organization has less than good intentions and is creating a Chatbot, a system, or a media blitz that contains misleading or just plain false information [10]? How do we know it’s happened, and can anything be done to prevent it, detect it, or figure out it should just be ignored? This leads us to our discussion in Chap. 13—What Happens When a Chatbot Gives Detrimental Information and Who’s Responsible?

References 1. Bolukbasi T, Chang K-W, Zou JY, Saligrama V, Kalai AT (2016) Man is to computer programmer as woman is to homemaker? Debiasing word embeddings. In: Advances in neural information processing systems, pp 4349–4357 2. Dixon L, Li J, Sorensen J, Thain N, Vasserman L (2017) Measuring and mitigating unintended bias in text classification. In: AAAI 3. Gardner M, Grus J, Neumann M, Tafjord O, Dasigi P, Liu NF, Peters M, Schmitz M, Zettlemoyer L (2018) Allennlp: a deep semantic natural language processing platform. In: Proceedings of workshop for NLP open-source software (NLP-OSS). Association for Computational Linguistics, pp 1–6 4. Kiritchenko S, Mohammad SM (2018) Examining gender and race bias in two hundred sentiment analysis systems. In: Proceedings of the 7th joint conference on lexical and computational semantics (*SEM), New Orleans, USA 5. Park JH, Shin J, Fung P (2018) Reducing gender bias in abusive language detection. arXiv preprint arXiv:1808.07231 6. Crowder J, Carbone J, Friess S (2013) Artificial cognition architectures. Springer International, New York, NY. ISBN 978-1-4614-8071-6 7. Wagner C, Garcia D, Jadidi M, Strohmaier M (2015) It’s a man’s wikipedia? Assessing gender inequality in an online encyclopedia. In: ICWSM, pp 454–463 8. Replica.ai. Cakechat: emotional generative dialog system 9. Crowder J, Carbone J, Friess S (2020) Artificial psychology: psychological modeling and testing of AI systems. Springer Nature Switzerland, Cham, Switzerland. ISBN 978-3-030-17079-0 10. Buolamawini J, Gebru T (2018) Gender shades: intersectional accuracy disparities in commercial gender classification. In: Proceedings of the 1st conference on fairness, accountability and transparency. PMLR 81:77–91

What Happens When a Chatbot Gives Detrimental Advice? Who’s Responsible?

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Abstract

Many online Chatbots provide disclaimers letting the public know that it is possible to get misinformation or just incorrect information from the Chatbot. Additionally, businesses are already rolling out training and Chatbot rules/policies internally because of the potential risks and liabilities of wrong information created and used by persons within the company using a Chatbot for work-related efforts and/or the dangers of misuse and misunderstanding by users of a company’s products and related data artifacts. However, providers of these disclaimers are still in the minority of Chatbot producers. We will discuss what happens when incorrect information is provided to the user by a Chatbot and what ramifications are there for both the user and the company who provided the Chatbot. Keywords

Chatbot • Artificial intelligence • Natural language processing • Natural language generation • 1st amendment • Free speech • Deep fake • Large language model • Hebbian learning model Abbreviations

AI DoD KRT LLM ML NLG NLP

Artificial Intelligence Department of Defense Knowledge Relativity Threads Large Language Model Machine Learning Natural Language Generation Natural Language Processing

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 J. Crowder, AI Chatbots, Synthesis Lectures on Engineering, Science, and Technology, https://doi.org/10.1007/978-3-031-45509-4_13

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NLU RECL US

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Natural Language Understanding Rapid Effective Causal Learning United States

The world of Artificial Intelligence, and in particular, Chatbots is always at, and will always be, at the cutting edge of software and hardware technologies. AI and AI-driven Chatbots will continually be woven into goods and services across industries and cultures, especially into multi-media enterprises [1]. As has been the case for the last 200+ years, laws are woefully behind software/hardware technology development. Even Thomas Jefferson weighed in on the subject back in 1816: Laws and institutions must go hand in hand with the progress of the human mind. As that becomes more developed, more enlightened, as new discoveries are made, new truths disclosed, and manners and opinions change with the change of circumstances, institutions must advance also, and keep pace with the times [2].

And while laws and ethical norms have, do, and will continue to evolve as society advances and changes, it does so at a snail’s pace compared to the continually exponential growth of technology advancements. The advancement of Chatbot technologies like ChatGPT, while impressive, have legal and ethical implications few consider before releasing the technology on the public [3]. One issue is that there is no clear definition of what is ethical, especially when it comes to Artificial Intelligence and Chatbots. This issue is further confounded when we consider cultural effects on ethical standards. What is ethical in one place may not be ethical in another. The entire subject of legal and ethical considerations about possible misinformation that could be disseminated by a Chatbot is a very gray area. As Congress considers taking up the challenge of enacting regulations and what will most certainly be ill-fitting laws concerning AI and Chatbots will, most fear, do little to stop the spread of incorrect and misleading information from getting out and spreading across multiple media sources. The entire notion of what is misinformation cannot be agreed to in society [4]. However, there are some forms of misinformation or wrong information that we can agree on. Having a Chatbot give bad medical advice, or bad financial advice could easily be seen as detrimental or even disastrous, depending on the context. But medical professionals, real estate professionals, and investment professionals have all looked at Chatbots and ChatGPT, in particular, as being useful in their professions. Again, context is everything, depending on how professionals are making use of Chatbots and how they verify the provided information before using it. As things progress, there will be a first case where an AI software developer will be blamed for medical misinformation. It’s coming, and sooner than later [5]. Is misinformation protected by the first amendment?

13.1

13.1

Chatbots, Free Speech, and the 1st Amendment

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Chatbots, Free Speech, and the 1st Amendment

According to research done last year and reported on by the Congressional Research Service,1 while the Supreme Court recognizes that while misinformation and disinformation (yes, there’s a difference2 ) adds nothing good to society, they are hesitant to restrict it, lest people curtail what would be valuable speech for fear they might violate a statute against misinformation. What’s more interesting is how Chatbots play into or not into the discussion of free speech. While Chatbots can normally be detected, as they get more sophisticated and develop their own machine learning, do we grant them the same 1st amendment rights as humans? Any laws or restrictions that the court or Congress would place on Chatbots’ speech would, by implication, put controls and limits on those who use them and interact with them; restricting the rights of people who interact with Chatbots.3 And while the motivation for restrictions may be borne out of Russian Trollbots that tried to influence elections, we must tread carefully as we head down the Chatbot legislative corridor to not infringe unwittingly on what should be protected speech. We have created a quagmire that will take years to sort out, all the while, Chatbots infiltrate more and more of society, multimedia, and people’s lives in general [4]. Many are worried about the upcoming 2024 election.4 Deep fake audio and video are possible to create that is realistic enough to fool at least a segment of the population. People on both sides of the aisle can be fooled by deep fake misinformation that provides views people would like to be true. Again, this isn’t an indictment of Chatbots, it is a warning as to how we use Chatbots, how easily we listen to them, and how little time we spend trying to verify the information [4]. We’ve now moved past Ronald Reagan’s quote: Trust but verify.

And moved into the new world of: Don’t trust till you verify!.

1 https://crsreports.congress.gov/product/pdf/IF/IF12180. 2 Although both words refer to types of wrong or false information, only disinformation is wrong on purpose. 3 Do Bots Have First Amendment Rights?—POLITICO Magazine. 4 AI-generated disinformation poses threat of misleading voters in 2024 election | PBS NewsHour.

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Chatbot Liabilities, Cyber Insurance, and Measuring Trust: Today and Tomorrow5

Today, Chatbot(s) liabilities interestingly enough are difficult to assess because of the many uses across so many different disciplines and industries. Also, since courts are only in their infancy dealing with Chatbot misinformation, it remains to be seen how the insurance companies will follow suit although it’s obvious to most that Cyber insurance will be where Chatbot issues are most likely to be mitigated. Chatbots and their capabilities can be a two-edged sword. Poorly designed and/or executed Chatbots can backfire and cause company branding disasters. There is a need within all industries for the notion of AI/ML insurance [6]. While cyber insurance exists, it is not adequate to understand and mitigate AI/ML issues in general, especially specific liability issues and ramifications from Chatbot misinformation failures and/or damages. Other issues that need to be covered by insurance for Chatbots in general are areas like copyright issues and problems associated with anti-hacking laws/responsibilities/liabilities. What is beginning to happen and must happen is AI/ML-specific insurance policies.6 Historically, Cyber insurance has been around since the mid-1990s, and these days costs are even broken down by location and even industries just like weather disaster locations. Specifically, there are also many categories of Cyber and Liability insurance that complicate mitigation further: Cyber Liability Insurance, Ransomware Insurance, Cyber Insurance Cost, Cyber Insurance Cost by Industry, Commercial Crime Insurance, Data Breach Insurance, Small Business Cyber Threats, and Social Engineering Fraud Coverage among many others. So, there are no easy answers. But suffice it to say that many will be paying for the cost of mis- and dis-information, as well as the accidental and malicious misuse of Chatbots. Hence, we can and should consider the Chatbot dilemmas and potential solutions we can forge today as an opportunity to start forging AI ethics and security law for tomorrow. Some well-known and newer technologies to review which can potentially provide future help for bounding the problem include well-known research in Uncertainty [7], Set Theory [8, 9], Fuzzy Logic with Set Theory [10, 11], Possibilistic Theory [12], and Rapid Effective Causal Learning (RECL) [13] among others. Each of these can enable a more comprehensive view of ambiguous problem sets for someone in general and/or especially for a Chatbot system and can help us map to more effective outcomes. Additionally, numerous mathematical associations can be used to represent information relationships, and this has potentially great contextual support value to enhance machine learning algorithms. This is because these types of technologies, when explicitly used as support elements, can refine the contextual understanding of who, what, when, where, and how, data was previously captured and/or used. Today this evidentiary focused contextual information is not captured, and if it is, it generally is not included within the vast data sets researchers or Chatbots, in this case, use to help train their systems. Hence, 5 This section is co-authored by Dr. John Carbone. 6 https://www.roguerisk.com/insights/business-insurance-risks-from-chatgpt-and-ai-content-tools.

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Chatbot Liabilities, Cyber Insurance, and Measuring Trust: Today …

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probabilistic modeling is very useful for specific data when that data and the system it represents are well-known. However, it is imperative that the generalized scientific philosophy that nature is inherently probabilistic and therefore repeats itself in exactly the same way continuously, and that every possible relationship can be posited if we collect data long enough, is too often misappropriated and a dangerous journey which many are now realizing in the case of LLM-based Chatbots. Finally, Chatbots are useful, but they must be supported with context-driven support mechanisms/technologies in order to make them truly more useful, more trustworthy, and less dangerous than they are now and will be even more without more bounded context-based rationality [12]. Therefore, effective trust measurement of uncertain and ambiguous things requires an understanding of the historical value of uncertainty research and an uncertain environment’s characteristics. Specifically, that traditionally Uncertainty analysis is used to measure system reliability to manage unpredictability, unreliability, and riskiness, that Uncertainty is a science within itself that has numerous benefits for capturing and defining ambiguities, that the goal is to continuously push anxious uncertainty out of a system over time to achieve the ultimate objective of trustworthiness and peace of mind while simultaneously satisfying the users and laws which govern systems, and importantly that a system with less complexity and less uncertainty does not mean it is more “Trustworthy”, because trust is based upon each type of function or objective required. Hence, the following uncertainty concepts can help with the development of systems in general and can help with the development of trust while helping to drive out the uncertainty with Chatbots: (1) (2) (3) (4) (5) (6)

Distinguishing between error and uncertainty, Recognizing that all measurements have uncertainty, Identifying types of error, sources of error, and how to detect/minimize error, Estimating, describing, and expressing uncertainty in measurements and calculations, Using uncertainty to even describe the results of their own lab work, Comparing measured values and determining whether values are the same within states uncertainty.

Finally, some useful steps to measure and generate Artificial Intelligence system trust are the following: (1) Develop initial system thresholds from functional requirements (e.g., performance, constraints, etc.) (2) Generate continuously evolving uncertainty thresholds of what you know about any piece of the system information or context as you develop your AI system (e.g., use weighted relationship measurements) (3) Also, measure continuously the internal trust of your AI system:

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a. Internal Trust is determined by what a system knows as it learns: again, using weighted relationships, b. Use these values to determine what your system can do or is allowed to do, and c. Continuously measure to determine continuous levels of reliability. (4) Make sure you bound your system objectives and map to required user requested requirement thresholds.

13.2.1 Mathematical Modeling of Information Relationships A few of the many examples of mathematical context modeling that can and are applied to information analysis: hi =

n E

wi j x j h ∗

j=1

Ising, Hopfield, Hebbian, Lagrange, Hamiltonian, Knowledge Relativity Threads, etc. Each can be used for either similar or vastly different applications. The key like any algorithm including Machine Learning is in the understanding of their value to an objective. For example, the Ising Model is a somewhat generic mathematical model used primarily by physicists that can be applied to understand phase transitions in many domains. Extended for information iterative knowledge also has transitions. It measures a magnetic field hi, which is sensed by some atom known as i, and part of many other particles that are summed together to get an overall conceptual value. wij, represents the magnitude of the weighted coupling between each of the i and j atoms. The magnetic coupling changes according to the distance between atoms. Extending to information relationships, each piece of information can provide context that increases or decreases relative relationship (here in the form of magnetism and closeness). The Hopfield Model, models brain like neural nets as an energy function: E =−

E 1E wi j si s j − θi si 2 i, j

i

This was developed in 1982 by John Hopfield and became an early AI associative model used in the 1980’s. His objective was to try and harness theoretical complexities in a proposed energy function where scaler values represent each associated binary network state, and uses thresholds (Only two different value states usually 1 or −1). Hebbian Learning is a model based upon Neuro associative learning or synaptic strength developed for Neuroscience by Donald Hebb as far back as 1949. A key construct that forms the basis of much machine learning today but still has added “Energy Summation” contextual value not often used enough. This is illustrated in Fig. 13.1.

13.2

Chatbot Liabilities, Cyber Insurance, and Measuring Trust: Today …

Fig. 13.1 Simple Hebbian rule: sum over events

127

∆

=∈

∆

=∈ ∑

Hebb’s rule was capturing synaptic strength and associative learning through the analogy of cells that are repeatedly excited and active simultaneously which then tend to become associated “activity in one, activates the other”; thus when a cell continues to fire, it develops a synaptic knob on the axon or enlarges it, i.e., becomes more important. Similarly, the Lagrange Function describes the state (motion) of a dynamic system in terms of position coordinates and their time derivatives which are equal to the difference between the potential energy and kinetic energy. Hamiltonian equations are similar to Lagrange but used to describe a dynamic system (e.g., particle motion) in terms of momentum and coordinates of space and time; total energy of a system when time is not explicitly part of the function, and lastly Knowledge Relativity Threads (KRT) [14] utilize Newtonian mechanics to analogously associate information and develop contextual trust over time that are sharable and visualizable. Below, KRT abstracts the concepts of mass and Newtonian distance to model the importance, attraction, and weakness of relationships in the form of closeness of association context or the positive and/or negative quality of the information relationship. Additionally, KRT’s model nested dependencies for capturing inductive thinking and analysis and can retain historical structure over time as information is learned. These relationships are captured as knowledge objects which can expand and contract. The KRT concept is illustrated in Fig. 13.2.

Fig. 13.2 Illustration of knowledge relativity threads

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13.3

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Chatbots and Emergent Behavior

Another issue that has started to become an issue with smarter and smarter Chatbots and other AI-enabled systems is the notion of emergent behavior. Emergent behavior describes behavior, thought patterns, or novel properties that arise within a system that were not originally designed, or programed into the system. Large Language Models (LLMs) that are trained and learned from massive data sources utilize probabilistic models that remember and then present in more natural language.7 LLMs, while not specifically trained to have certain abilities, can develop them as they grow in levels of remembrance. These include abilities to perform complex mathematical calculations and answering questions they were never designed to answer. As the volume of information being used to train a LLM grows exponentially, their ability to respond in a way that seems to predict the most likely response to a question grows. It has been discovered that LLMs can remember to perform tasks they were not intended for without any change to the structure (architecture) of the Chatbot system or any task-specific training. Since the Machine Learning mechanisms under the covers simply remember, although in great detail, a Chatbot is not learning (e.g., Learning is knowing how to inductively, or abductively inference how to apply something remembered contextually) how to apply to perform these tasks. Instead, it has only indexed (aka stored in a database/datastore and points to it) the information it remembered/trained previously and then subsequently has mapped that information (aka also now points to) to a task that should be performed if the system is asked via the Chatbot user interface. For example, if others have previously used the same or similar information before and that resulted in a task being performed or requested, the system remembers this. It is critical that we do not place improper value and perceive capability and capacity as greater than what these solutions are actually technically designed with and for. This is especially the case the more critical and more important the environment is, in which they are used. We must better understand the level of trustworthiness they are designed with in order to use them more effectively to achieve more positive outcomes [15]. These and many other questions surrounding the use, abuse, and growth of Chatbots will continue long into the future to the point that I expect the Chatbots to argue with each other over a host of subjects. We move on from misinformation to a discussion of whether it is even possible for companies to create Chatbots that act as unbiased, responsible AI entities, and whether we actually want them.

7 https://www.assemblyai.com/blog/emergent-abilities-of-large-language-models/.

13.4

13.4

Can We Rely on Companies that Create Chatbots to Act Responsibly?

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Can We Rely on Companies that Create Chatbots to Act Responsibly?

How hard do Chatbot companies monitor and provide support for users? Some Chatbots promise to be your online boyfriend/girlfriend or your forever companion. What controls are there to prevent things from going too far? Is it the responsibility of the company, the user, or both or neither of them? The short answer, as we have discussed, is even if they try to act responsibly because they interact with and learn from people, bad actors will always work to get around any safety protocols and measures Chatbot developers put in place [16]. There are multiple reports that the constraints and guardrails put in place by Chatbot companies can be gotten around fairly quickly, which can lead to unpredictable behavior, as we’ve discussed throughout the book. Because this is apparent, governments are starting to take AI governance8 matters into their own hands. Both the United States and China are looking to put controls and laws in place to limit the growth of Chatbots and AI in general. However, China, the US, Europe, and other countries are taking very different approaches to AI governance.9

13.4.1 China’s Approach to AI/Chatbot Governance China has forged forward aggressively with its AI regulatory policies for domestic and commercial applications [15]. However, China puts no restrictions or regulations on Chinese military use of AI. China’s approach to Chatbots in particular puts regulations on generative AI (e.g., Natural Language Generation, or NLG). China’s approach and overall goal seem to be political stability first and innovation second. On April 11, 2023, the Cyberspace Administration of China (CAC) set out official “Measures for the Management of Generative Artificial Intelligence Services [16].” This includes regulations on “Deep Synthesis,10 ” machine-generated text, audio, images, and all visual content. China’s regulation holds Chinese companies and companies operating within China liable for any perceived harm and demands security certification before AI models can be used.

8 Governance encompasses the processes by which organizations are directed, controlled, and held

to account. It includes the authority, accountability, leadership, direction, and control exercised in an organization. 9 https://www.msn.com/en-us/news/world/united-states-and-china-are-taking-opposite-approachesto-ai/ar-AA1e0lsF. 10 Deep synthesis is the use of technologies (e.g., deep learning and augmented reality) to generate text, images, audio and/or video to create virtual scenes.

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13.4.2 USA’s Approach to AI/Chatbot Governance In contrast to China having no restrictions on the Chinese military use of AI, the U.S. established rules of use and engagement for AI-enabled Department of Defense systems. Again, in contrast to China’s severe restriction on public/commercial use of AI, the U.S. has restrictions on the commercial use of AI systems like ChatGPT and its release to the public [17]. The U.S. Department of Defense’s AI-driven system must meet ethical standards (described below) created to ensure that the designs and use of AI systems for military use are responsible, equitable, traceable, reliable, and governable [18].

13.4.3 Europe’s Approach to AI/Chatbot Governance European Union regulators have struggled and have had much debate over how to rein in and control AI systems and Chatbots in particular [19]. There were regulations and restrictions put in place in 2021 that banned some AI applications used for facial recognition, social scoring, and others. Some AI uses/applications were deemed “high risk,” posing strict requirements for safety and transparency, demanding strict human oversight of their use [20]. The rise in use and capabilities of Chatbot systems like ChatGPT is driving the European Union back to the drawing board toward drafting effective controls and regulations on the development and use of Chatbot systems. In February 2023, they proposed that Chatbot systems generating complex texts (NLG) without human oversight should be deemed high risk, hopefully, to stop systems like ChatGPT from spreading disinformation [21]. This may turn out to be unduly prohibitive, since it might make activities that are no risky at all be deemed high risk, since there is no concrete definition of “complex texts.

13.4.4 Department of Defense’s Approach to AI/Chatbot Governance Not only does the US in general have an approach to the governance of artificial intelligence, but also the US Department of Defense (DoD) has its own AI governance policies [22]. These are outlined below: • Responsible: DoD personnel will exercise appropriate levels of judgment and care while remaining responsible for the development, deployment, and use of AI capabilities. • Equitable: The department will take deliberate steps to minimize unintended bias in AI capabilities. • Traceable: The department’s AI capabilities will be developed and deployed such that relevant personnel possess an appropriate understanding of the technology, development processes, and operational methods applicable to AI capabilities, including

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transparent and auditable methodologies, data sources, and design procedures and documentation. • Reliable: The department’s AI capabilities will have explicit, well-defined uses, and the safety, security, and effectiveness of such capabilities will be subject to testing and assurance within those defined uses across their entire life cycles. • Governable: The department will design and engineer AI capabilities to fulfill their intended functions while possessing the ability to detect and avoid unintended consequences, and the ability to disengage or deactivate deployed systems that demonstrate unintended behavior. While it may seem reasonable and might make us all comfortable to put regulations and restrictions in place for the development and use of AI systems like Chatbots, there are fundamental flaws in each and every regulatory standard put in place. We will highlight these in Sect. 13.5.

13.5

Why Regulations Are Doomed to Fail

There are several complex issues when trying to put regulations and controls on AIenabled systems. The first one is the definition of terms and trying to prepare and put in place unambiguous wording where everyone can agree on the definition of terms and interpretation of the intent of the regulation [23]. Let’s take the U.S. Department of Defense’s rules for the ethical use of AI: DoD personnel will exercise appropriate levels of judgment and care while remaining responsible for the development, deployment, and use of AI capabilities…

The use of the word “appropriate” adds a high level of ambiguity to this restriction. Who gets to decide what is appropriate and what is meant by appropriate. If I design the system, then anything I put into the system design is something I’ve deemed appropriate. Let’s look at another: The department’s AI capabilities will be developed and deployed such that relevant personnel possess an appropriate understanding of the technology…

Does this mean we can demand that all relevant personnel have a Ph.D. in engineering and software to ensure they have an appropriate understanding of the systems? Who defines what appropriate understanding means? If we can’t agree on definitions, then the regulations mean entirely different things to each person, each group, each nation, etc. The problem with regulations on a system like a Chatbot is the regulation are written in term of human constructs of right and wrong and ethics. Chatbots have to ethical understanding. Chatbots have to sense of “truth [24].”

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As discussed, many times within the book, Chatbots in some form, are here to stay. So then, what does the future landscape hold for AI systems like Chatbots? Sect. 13.5 will explore this subject.

References 1. Asenmacher D, Clever L, Frischlich L, Quandt T, Trau1tmann H, Grimme C (2020) Demystifying social bots: on the intelligence of automated social media actors. Soc. Media Soc. 6(3):1 2. Spottswood M (2008) Falsity, insincerity, and the freedom of expression. William and Mary Bill of Rights J 16 3. Bontridder N, Poullet Y (2021) The role of artificial intelligence in disinformation. Cambridge University Press 4. Bergamini D (2020) Need for democratic governance of artificial intelligence. Committee on political affairs and democracy—council of Europe, 24 September 2020, no 15150 5. Abdullah H, Karlekar A, Bindschaedler V, Traynor P (2022) Demystifying limited adversarial transferability in automatic speech recognition systems. In: International conference on learning representations 6. Shankar R, Nagle F (2020) The case for AI insurance. Harward Business Review 7. Liu B (2004) Uncertainty theory: an introduction to its axiomatic foundation. Physica-Verlag, Heidelberg 8. Fraenkel AA, Bar-Hillel Y, Levy A (1973) Foundations of set theory. Elsevier, p 244 9. Hausdorff F (2021) Set theory, vol 119. American Mathematical Soc 10. Zimmermann H-J (2010) Fuzzy set theory. Wiley interdisciplinary reviews: computational statistics 2(3):317–332 11. Zadeh LA (1972) A fuzzy-set-theoretic interpretation of linguistic hedges 4–34 12. Crowder JA, Carbone JN, Friess SA (2014) Artificial cognition architectures. Springer, New York 13. Ho S-B (2016) Principles of noology: toward a theory and science of intelligence, vol 3. Springer 14. Carbone J (2010) A framework for enhancing transdisciplinary research knowledge. Texas Tech University 15. Bai Y, Kadavath S, Kundu S, Askell A, Kernion J, Jones A, Chen A, Goldie A, Mirhoseini A, McKinnon C, et al (2022) Constitutional ai. Harmlessness from ai feedback. arXiv preprint arXiv:2212.08073 16. Biderman S, Schoelkopf H, Anthony QG, Bradley H, O’Brien K, Hallahan E, Khan MA, Purohit S, Sai Prashanth USVSN, Raff E, et al (2023) Pythia: a suite for analyzing large language models across training and scaling. In: International conference on machine learning. PMLR, pp 2397–2430 17. Carlini N, Nasr M, Choquette-Choo CA, Jagielski M, Gao I, Awadalla A, Koh PW, Ippolito D, Lee K, Tramer F, et al (2023) Are aligned neural networks adversarially aligned? arXiv preprint arXiv:2306.15447, 2023. Carper AI. Stable-vicuna 13b. 245 18. McAleese N, Trebacz M, Aslanides J, Firoiu V, Ewalds T, Rauh M, Weidinger L, Chadwick M, Thacker P, et al (2022) Improving alignment of dialogue agents via targeted human judgements. arXiv preprint arXiv:2209.14375

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19. Goodfellow IJ, Shlens J, Szegedy C (2014) Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572; Guo C, Sablayrolles A, J´egou H, Kiela D (2021) Gradientbased adversarial attacks against text transformers. arXiv preprint arXiv:2104.13733 20. Hendrycks D, Burns C, Basart S, Critch A, Li J, Song D, Steinhardt J (2021) Aligning {ai} with shared human values. In: International conference on learning representations 21. Jones E, Dragan A, Raghunathan A, Steinhardt J (2023) Automatically auditing large language models via discrete optimization. arXiv preprint arXiv:2303.04381 22. Korbak T, Shi K, Chen A, Bhalerao RV, Buckley C, Phang J, Bowman SR, Perez E (2023) Pretraining language models with human preferences. In: International conference on machine learning. PMLR, 2023. 22, pp 17506–17533 23. Leino K, Wang Z, Fredrikson M (2021) Globallyrobust neural networks. In: International conference on machine learning. PMLR 24. Lester B, Al-Rfou R, Constant N (2021) The power of scale for parameter-efficient prompt tuning. arXiv preprint arXiv:2104.08691

Part V The Future of Chatbots

What Does the Future of Chatbots Look Like?

14

Abstract

There has always been a push for fully autonomous AI entities. From self-driving cars, self-piloted plans, trains. There are plans for caregiver robots for the infirm and/or the elderly. Can we have fully autonomous AI entities and Chatbots loose on society and what constraints/controls are needed to ensure physical, emotional, and psychological safety? Also, the question must be asked, “will Chatbots kill human creativity or enhance it?”. Keywords

Chatbot . Artificial intelligence . Natural language processing . Natural language generation . Chatbot creativity . Social activism . Large language model . Human-chatbot communication . Ethical decision loop Acronyms

AEDL AI EDM LLM ML NLG NLP NLU TAS

AI Ethical Decision Loop Artificial Intelligence Ethical Decision-Making Large Language Model Machine Learning Natural Language Generation Natural Language Processing Natural Language Understanding Toulmin Argument Structure

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 J. Crowder, AI Chatbots, Synthesis Lectures on Engineering, Science, and Technology, https://doi.org/10.1007/978-3-031-45509-4_14

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As we have discussed throughout the book, Chatbots and AI entities (e.g., robots) are here to stay. Even though the future of robots, Chatbots, and AI/ML-driven systems is uncertain, we need to begin dialogs as to how we proceed in research, development, testing, deployment, and execution of all current and future AI systems [1, 2]. Here we present thoughts and examples of how to build trust into AI systems as well as a notional architecture for ethical thinking in an AI entity.

14.1

The Rise and Fall of Human and Chatbot Creativity

Most technology experts are clear that Chatbots will continue to grow in popularity, complexity, and capability. Fully visual-based interfaces are in the works now as well as voice-enabled interfaces, the combination of these will transform the Chatbot experience by increasing the data-collection aspects of Chatbots as people become more comfortable with the full visual/auditory experience and reveal deeper insights into their thoughts and feelings [3]. As the public interfaces more and utilizes Chatbots like ChatGPT and Bard to gain initial ideas and creative thoughts for their work, the more creative Chatbots become, and the less humans become creative on their own. Eventually, human creativity and Chatbot creativity will meet as the volume of content generated by Chatbots becomes equal to human-generated content. When this happens, Chatbots will begin learning and gaining information from each other. Chatbots learn and adapt by scraping information from all over the internet [4]. When they begin to take each other’s output and learn from it, the overall creativity of ChatGPT and Bard will begin to decrease, since they are no longer getting a majority of their content from creative people. Figure 14.1 illustrates a potential outcome over several years. While Fig. 14.1 is, seemingly, an exaggerated possible outcome, it drives home the point that human creativity needs to come from human insight, not what a probabilistically driven Chatbot Natural Language Generator (NLG) produces. Figure 14.1, again, is shown to spark dialog between technologists, software engineers, and policy makers to work through the issues of the future of Chatbots and AI entities. This is important as we look at what uses Chatbots may have in the future. What follows is a discussion of possible uses of Chatbots in the coming years. While this is a draconian look at the overall notion of the decline of creativity driven by Chatbots, it is intended to provide a kick-off point for open discussions about the development and uses of Chatbot systems moving forward. Next, we look at how they are being used, and how they might be used in the future, along with discussions about developing trust in Chatbot systems.

14.1 The Rise and Fall of Human and Chatbot Creativity

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Creative Content

Chatbot-Generated Creative Content

Overall Chatbot Creativity

Human-Generated Creative Content

Time

Fig. 14.1 Overall chatbot creativity over time

14.1.1 The Use of Chatbots in Social Activism Companies have already begun the use of Chatbots to promote social activism through the gathering and dissemination of information about social issues. In the future, organizations will begin to utilize seemingly eco-friendly Chatbots to help promote social activism ideologies and activities. And while this seems like it can only be good for society and improve communities, it is possible to push agendas that are less than altruistic, based on opinions and feelings, and not fact/data-driven. With the use of Natural Language Processing (NLP) and Large Language Models (LLMs), more and more people will be responsive to Chatbots regardless of their viewpoints and/or agendas [5].

14.1.2 The Use of Chatbots in Research The ability of Chatbots and ML algorithms in general to efficiently process mass quantities of data is appealing to researchers in many fields. Market research has been greatly enhanced by the use of Chatbots, mainly for gathering data to track customer behaviors, quantitative data like buying habits, as well as qualitative data like customer engagement and feelings about products and services [6]. Quantitative data is typically numerical data,

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data that can be measured or counted and expressed as numbers. Qualitative data is information that is conceptual and based on customer traits and characteristics, like feelings and overall impressions of a product, not simply what they bought, but why. One important characteristic of Chatbots gathering and processing data, both quantitative and qualitative is that you don’t lose information when someone is reassigned to a different project, goes on vacation, or quits their job. The processing capabilities built into most complex Chatbot systems allow easier and more efficient integration of research data with customer feedback, merging structured with unstructured data [7]. The issue associated with utilizing Chatbots for such research is that it cannot be guaranteed that all the information provided is real, correct, and applicable. So how do companies, people, and organizations build any kind of trust in Chatbots and HumanChatbot communication. Section 14.2–14.6 will explore this different aspect of creating and measuring trust in Chatbots.

14.2

Measuring Trust and Human-Chatbot Communication1

The rapid automation boom that is occurring with the onset of ChatGPT and others shows us explicitly that although the interface has its warts and pitfalls humans will find a way to utilize what works and throw away what doesn’t. That being said, improving the interfaces and Trust relative to the information and actions these systems provide requires a deeper understanding and the actual ability to measure trust in order to improve it. Hence, Trust measurement features and characteristics for AI-based systems first require an understanding of how Trust is manifested logically and physically within the human brain and body. Once we can identify the various types of physical and digital learning algorithms that are best suited for developing contextual relationships, can we then begin applying and integrating Trust into AI-based Chatbot Systems? Some of the key challenges and components we look to understand relative to trust implementation derive from: human thinking processes, human sensing, how humans make decisions, and key architectures models we described in Chap. 11. Some Trust model examples in use today, for example, include Distributed Blockchains where the objective is for everybody to know the information at the same time to try and remove the risk of falsification and provide transaction resiliency, Privacy/Confidentiality Trust using encryption, Eigenvector Centrality which can evaluate the importance of something by also considering its neighbors, and Risk measurement Trust where one analyzes potential risks and considers them as weighted factors of risk characteristics, and Information Trust as it relates to context derived from mathematical structure and fidelity context surrounding associated events. We will explore relationship mathematics in the next section. 1 Dr. John Carbone is the author of this section.

14.4 The Toulmin Argument Structure: The Art of Developing Factual Trust

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A key component of human trust is linked to reward. This was theorized originally by Papez’s [8] research on reward and Emotion circuits linked together within the Brain’s Limbic system and brain structures located on both sides of the Thalamus supporting a number of functions including emotion, behavior, long-term memory and smell, and provide critical aid to the forming of memories. The brain performs a complex balancing act between a human’s Sympathetic versus Parasympathetic systems controlled by chemical neurotransmitters. So how do we use this information to develop mechanisms within an AI-based system like a Chatbot to provide the individualistic trust humans need going forward? This next section will describe some models which can be used to capture Trust.

14.3

Calculating Trust: Mathematical Modeling of Relationships2

Many types of powerful mathematics-based relationship models exist that can be used to implement context development digitally to provide artificially intelligent topic and context learning methods. The following examples show how mathematical properties can provide iterative improvement of context between one-to-many contextual concepts. Hence, various mental context structures can be created around specific topics and are part of the methodology within the body of research in Artificial Cognition and Artificial Psychology. A few of these mathematical examples are: . Ising Model [9]: a generic mathematical model used primarily by physicists that can be applied to understand phase transitions in many domains. . Hopfield Model [10]: models’ brain-like neural nets as an energy function. . Hebbian Learning [11]: a model based upon Neuro associative learning. . Hamiltonian Equations [12]: similar to Lagrange; used to describe a dynamic system (e.g., particle motion) in terms of momentum and coordinates of space and time; total energy of a system when time is not explicitly part of the function.

14.4

The Toulmin Argument Structure: The Art of Developing Factual Trust3

The art of developing factual resiliency in the vast ambiguous world of language we live within was created in the twentieth century by English philosopher Stephen E. Toulmin. He came up with this model to examine arguments. It is also known as the Toulmin method, whose model provides a structured way to analyze or construct logical and thorough arguments. The method includes six main components: 2 Dr. John Carbone is the author of this section. 3 Dr. John Carbone is the co-author of this section.

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Fig. 14.2 Notional TAS architecture for chatbot use

. Claim—a statement that you are asking the other person to accept. . Grounds—the basis of your persuasion: – Consisting of data and hard facts. – Determined by the reasoning behind the data. . Warrant—legitimizing the claim by showing the grounds to be relevant and linking data and grounds to a claim. . Backing—support for an argument and gives additional support to the warrant by answering different questions. . Qualifier—indicates the strength of the leap from data to the warrant. . Rebuttal: Rebuttals to claims will happen. In AI-driven reasoning, it is useful to preempt what the rebuttal might be to minimize the potential for one occurring in real-time situations. Since a Chatbots that make use of a Toulmin Argument Structure (TAS) must be done in software, Fig. 14.2 below illustrates a notional architecture for a TAS, based on fuzzy implications/inferences.

14.5

Steps for Building Qualitative Trust Within AI Systems/ Chatbots4

Effectively building trust within a system requires a paradigm shift in understanding key functional objectives, identifying, and understanding complexity and novel architectural considerations that are sometimes counterintuitive to standard Von Neuman architectures. The objective is to learn how to build the right level of intelligence into your system that 4 Dr. John Carbone is the author of this section.

14.5

Steps for Building Qualitative Trust Within AI Systems/Chatbots

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matches the functional objectives. A few of the items listed next may seem like stating the obvious, however, they are often times forgotten and written off: . Always work to understand the problem solution set more in-depth utilizing deep design insight tools (e.g., Axiomatic Design, analysis of the simple but often forgotten 5Ws). . Decompose the problem and human interoperability with significant detail as you create the solution (e.g., Understand and use human learning processes well researched in psychology and artificial cognition disciplines). . Work to collect information and context from the best sources to support the integrity of your AI (e.g., refereed content, citations, historical quality pedigree). . Understand the details of your AI problem and formulate solutions by Reasoning with a Toulmin Argument architecture (e.g., break down hypotheses and verify backup material, Employ Toulmin Arguments in your AI Design). . Wherever possible, Increase the depth of your knowledge of the data AI will use (e.g., know where it’s from, tag the data throughout the system, know where your system is dependent upon it, then RELATE the data to each other as accurately as possible by choosing the appropriate mathematical constructs. . Develop detailed AI algorithms/instructions for your solution (e.g., is it Touring Complete5 ?) Often a more appropriate Machine Learning or other algorithm would have been much more beneficial with 5 min of planning. . Manage the required performance and/or perceived performance understanding. . Manage the AI complexity of your solution continuously (e.g. reduce complexity utilizing Axiomatic Design principles where you can). . Manage the AI security of your solution continuously, (e.g., you may utilize encapsulation, obfuscation, information integrity analysis, etc.). . Manage the ethical perception and ethical use of your solution AI to Human and Human to AI interface. . Make it a key objective to know all the dependencies in your system so your system can potentially dynamically improve/heal itself.

5 A Turing Complete system means a system in which a program can be written that will find an

answer (although with no guarantees regarding runtime or memory).

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14 What Does the Future of Chatbots Look Like?

Steps for Building Ethical Behavior Within AI Systems/ Chatbots6

As we begin looking at how we provide governance for AI systems and Chatbots in particular, we must look to ethical dilemma models and thought processes in humans. An ethical dilemma model is defined as a situation where ethical codes may be violated regardless of the course of action taken [1]. Specifically, an ethical dilemma is where no ideal course of action exists. This leads us to look at ethical decision-making models (EDMs), which are frameworks for decision-making, given an ethical problem to address. Many consider there are five dimensions to the ethical analysis process [2]. They are as follows: 1. 2. 3. 4. 5.

Utilitarianism: benefits the maximum number of people. Human rights approach: preserves the rights of all people. Fairness: all are treated equally. Common Good: decisions reflect and protect the common good. Virtue Approach: decisions made using moral characteristics (e.g., honesty).

Based on these, a general EDM can be established: 1. Stop and think—recognizing the ethical decision to be made and avoiding making a rash decision. 2. Clarify goals—understanding the goal, long-term and short-term, to help determine the desired or expected outcome. 3. Determine facts—consider the sources of information, e.g., are they credible. 4. Develop options—look at possible decision options. 5. Consider consequences—who and what will be affected by each decision option. What good or harm will come from each decision? 6. Choose, monitor, modify—make the ethical choice and after the choice is made, consider if it was the right one, what effects there were, and what you might do differently next time. In order to drive the notion of an EDM into Chatbots and AI systems in general, here we offer a simplification of the EDM that is appropriate to begin the discussions of ethics in AI systems and in particular in Chatbots: 1. Examine the ethical dilemma. 2. Thoroughly comprehend the possible alternatives. 3. Hypothesize ethical arguments. 6 Dr. John Carbone is the co-author of this section.

14.6

Steps for Building Ethical Behavior Within AI Systems/Chatbots

145

Learn Observe

Learn

Act

Heuristics Knowledge Base

Orient

Learn

Decide Learn

116

Fig. 14.3 The AI ethical decision loop

4. 5. 6. 7.

Investigate, compare, and evaluate the arguments for each alternative. Choose the alternative. Act on your chosen alternative. Look at the ethical dilemma and examine the outcomes while reflecting on the ethical decision.

Based on this, Fig. 14.3 illustrates a possible framework for an AI Ethical Decision Loop (AEDL): 1. Observe: Examine the ethical dilemma. 2. Orient: Thoroughly comprehend the possible alternatives and hypothesize about the ethical arguments, investigate, compare, and evaluate the arguments for each alternative. 3. Decide: Choose the alternative. 4. Act: Act on your chosen alternative. 5. Learn: Look at the ethical dilemma and examine the outcomes while reflecting on the ethical decision. Given the diversity of cultures within which AI systems and Chatbots reside, we will not discuss what is a right or wrong answer to these questions, but we pose this as a jumpingoff point for discussions across country/cultural/ideological boundaries. The truth is, we have not even defined what right is and we have not established what we really want from real, cognitive Chatbots or other AI entities yet. We do know, however, that what we don’t want them is having them capable of what is illustrated in Fig. 14.4.

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Fig. 14.4 AI entities gone bad

References 1. Bergamini D (2020) Need for democratic governance of artificial intelligence. Committee on political affairs and democracy – council of Europe, 24 September 2020, no. 15150 2. Bai Y, Jones A, Ndousse K, Askell A, Chen A, DasSarma N, Drain D, Fort S, Ganguli D, Henighan T, et al (2022) Training a helpful and harmless assistant with reinforcement learning from human feedback. arXiv preprint arXiv:2204.05862 3. Biderman S, Schoelkopf H, Anthony QG, Bradley H, O’Brien K, Hallahan E, Khan MA, Purohit S, Prashanth US, Raff E, et al (2023) Pythia: a suite for analyzing large language models across training and scaling. In: International conference on machine learning. PMLR, pp 2397–2430 4. McAleese N, Trebacz M, Aslanides J, Firoiu V, Ewalds T, Rauh M, Weidinger L, Chadwick M, Thacker P, et al (2022) Improving alignment of dialogue agents via targeted human judgements. arXiv preprint arXiv:2209.14375 5. Jyoti R (2019) Artificial Intelligence global adoption trends and strategies. International data corporation survey 6. Kvale K, Freddi E, Hodnebrog S, Sell OA, Folstad A (2020) Understanding the user experience of customer service chatbots: what can we learn from customer satisfaction surveys? In: Følstad A, Araujo T, Papadopoulos S, Law ELC, Luger E, Goodwin M, Brandtzaeg PB (eds) Proceedings of CONVERSATIONS 2020. Cham, Switzerland, Springer, pp 205–18 7. Kvale K, Sell OA, Hodnebrog S, Følstad A (2019) Improving conversations: lessons learnt from manual analysis of chatbot dialogues. In: Følstad A, Araujo T, Papadopoulos S, Law ELC, Granmo OC, Luger E, Brandtzaeg PB (eds) Proceedings of CONVERSATIONS 2019. Cham, Switzerland, Springer, pp 187–200 8. Papez JW (1937) A proposed mechanism of emotion. Arch Neurol Psychiatry 38(4):725–743 9. El-Showk S, Paulos MF, Poland D, Rychkov S, Simmons-Duffin D, Vichi A (2014) J Stat Phys 157(4–5):869–914

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10. Amari S-I (1972) Learning patterns and pattern sequences by self-organizing nets of threshold elements. IEEE Trans. C 21:1197–1206 11. Caporale N, Dan Y (2008) Spike timing-dependent plasticity: a Hebbian learning rule. Annu Rev Neurosci 31:25–34 12. Bloch AM, Leonard NE, Marsden JE (2001) Controlled lagrangians and the stabilization of euler–poincare mechanical systems. Int J Robust Nonlinear Control: IFAC-Aff J 11(3):191–214 13. Akers J, Bansal G, Cadamuro G, Chen C, Chen Q, Lin L, Mulcaire P, Nandadumar R, Rockett M, Simko L, Toman J, Wu T, Zeng E, Zorn B, Roesner F (2018) Technology-enabled disinformation: summary, lessons, and recommendations. Technical Report UW-CSE 14. Assenmacher D, Clever L, Frischlich L, Quandt T, Trautmann H, Grimme C (2020) Demystifying social bots: on the intelligence of automated social media actors. Social Media Soc. 6(3):1

Can Future Chatbots Show Love and Compassion, and Should They?

15

Abstract

Many Chatbots try to provide what they hope you see as emotions. These chatbots do not experience or recognize true emotions and rely on keyword/phrase detection to infer emotions. But eventually, synthetic emotions may be possible. What are the ramifications of truly artificial emotions, good and bad? As we wrap up the book, we will examine what the future may look like for AI Chatbots, robots, and other AI entities. Keywords

Chatbot . Artificial intelligence . Natural language processing . Human-Chatbot communication . Artificial prefrontal cortex . Natural language generation . Artificial emotions Acronyms

AI APC ML NLG NLP NLU

Artificial Intelligence Artificial Prefrontal Cortex Machine Learning Natural Language Generation Natural Language Processing Natural Language Understanding

There has been much discussion over the last few years on the possibilities of artificial emotions [1]. There are websites dedicated to this topic. What is required to facilitate AI systems the ability to process emotions, read emotional reactions, and communicate effectively with humans at an emotional level is the processing and analytical framework

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 J. Crowder, AI Chatbots, Synthesis Lectures on Engineering, Science, and Technology, https://doi.org/10.1007/978-3-031-45509-4_15

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that a Chatbot might provide, given the architectural constructs discussed throughout this book. The discussion here is just the beginning and much more research and development are needed to even scratch the surface to facilitate artificial emotions that mimic human emotions. One of the issues that needs to be understood and thought through is “whether this is even a necessary or advantageous development for robotic and/or Chatbot systems?”. We discussed both the notion of artificial emotions and an artificial prefrontal cortex in previous chapters, but here we expand on that discussion in terms of how it plays into the future development and possible enhancements to Chatbots and service robots in the future.

15.1

The Rise of Artificial Emotions1

One area that has been discussed at length over the last few years is the use of robots as caregivers [2]. However, home robot companies already exist and advertise both companion and caregiver robots. The market for caregiver and social robots/Chatbots is moving slowly, given the notion of a robot replacing a human in a caregiving capacity concerns the general population [3]. One of the pieces missing from current instantiations of Chatbots and Robotic systems that are made for human interaction is the ability to understand emotions. Evan advanced systems like ChatGPT and Bard, which can learn from and interact with people, these systems still lack an understanding of basic emotions [4]. If we want AI systems that can better understand and respond to changes in emotions, we need the Chatbots and robots to know about, learn about, and interact based on feelings. This would make the interactions more meaningful. One attempt to solve this issue is to provide detailed facial recognition software that can detect the non-verbal cues that allow them to recognize emotions and try to react appropriately. The problem with this solution is that if the AI system, Chatbot or robot, has no real understanding of emotions, then their responses become nothing more than a probabilistically generated response, similar to the responses given currently by Chatbots [5]. We discussed an Artificial Emotional Intelligence test in Chap. 7. Its use is precipitated on the Chatbot or robot’s ability to understand and display emotions, without it being specifically scripted but initial learning with the ability to learn as it interacts with different people. In addition, vocal recognition of changes that indicate different emotions would also be required. Up till now, the ability to understand and respond to emotions has not been a priority of Chatbot and robot developers. Getting basic functionality and adaptability have been the thrust of development efforts [6]. Moving forward, social, and emotional interactions should be included in the prime priorities for future enhancements and new development efforts for human interactive 1 Some material is repeated here from Chap. 7 due to its relevance to the Artificial Prefrontal Cortex

discussion since the chapters can be downloaded/purchased separately.

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Chatbots and robots. In order to begin moving forward with artificial emotional intelligence in Chatbots and robots, a basic understanding of the mathematics and constructs of artificial emotions must be explored [7].

15.2

The Basics of Artificial Emotions

The basics of artificial emotions start with the assumption that the AI system’s software framework must perform an action and the ensuing emotion contributes to the action chosen. We assume an Emotion Matrix E, with the matrix element Emotion(a, j) representing a possible emotion within the AI Chatbot software framework. The denotation of Emotion(a, j) represents performing action a in situation j. Genetic learning agents perform an emotional learning procedure with the following steps [8]: 1. State j: choose an action in situation—(let it be action a; let the environment return situation k). 2. State k: feel the emotion for state k—emotion (k). 3. State k: learn the emotion for a in j—Emotion (a, j). 4. Change state: j = k; return to 1. This Chatbot learning procedure is a secondary reinforcement learning procedure for emotion. The learning constant used in Step 3 (above) is: Emotion0 (a, j) = genome0 (inherited) Emotion1 (a, j) = Emotion0 (a, j) + emotion(k) This learning rule adds the emotion of being in the consequence situation, k, to the emotion toward performing action a in situation j on which k is the consequence. The above discussion works for emotional learning. But in general, we need algorithms that provide basic learning capabilities in a real-time dynamically changing environment, something that is typically difficult for classical machine learning systems—since they must be continually adaptive to handle the changes that must be derived and learned, based on interactions with the Chatbot’s environment. This requires an understanding of what it takes for an artificial intelligence entity (e.g., Chatbot) to capture, understand, and change between emotional states [9]. Here I introduce the notion of an artificial prefrontal cortex that I feel will be required for a Chatbot or robot system to capture, learn, and utilize the notions of rudimentary emotions.

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15.3

15 Can Future Chatbots Show Love and Compassion, and Should They?

The Artificial Prefrontal Cortex for Chatbot and Robot Systems

Many researchers have postulated that human cognition is implemented by a multitude of relatively small, special purpose processes, almost always unconscious Communication between them is rare and over a narrow bandwidth. Coalitions of such processes find their way into consciousness. This limited capacity workspace available for our cognition serves to broadcast the message of the coalition to all the unconscious processors within the human brain, to recruit other processors to join in handling the current novel situation, or in solving the current problem. Therefore, consciousness in this theory allows us to deal with novelty or problematic situations that can’t be dealt with efficiently, or at all, by habituated unconscious processes. It provides access to appropriately useful resources, thereby solving the relevance problem. Here we present a notional design and test architecture of an Artificial Prefrontal Cortex (APC) model for the use of emotional state transition and management in future Chatbots and robotic systems in general.

15.3.1 What Does Artificial Consciousness Mean? Artificial intelligence research and development of Chatbots like ChatGPT and Bard pursue the twin goals of understanding human intelligence and producing intelligent software and/or artifacts to mimic human reasoning and processing structures. In biological agents, this agenda arises from evolved drives and their associated goals. Such drives, which act as motive generators, must be present, whether explicitly represented, or expressed causally. The Chatbot’s interface is intended to act in such a way as to possibly influence what it senses later and add to its continual learning process. In other words, it is structurally coupled to its environment. This is a step toward the notion of Artificial Consciousness. Artificial consciousness and cognitive changes take place under the auspices of contexts: goal contexts, perceptual contexts, conceptual contexts, and/or cultural contexts. These may look like goal hierarchies, dominant goal contexts, dominant goal hierarchy, dominant context hierarchies, and lower-level context hierarchies. Each context is itself a coalition of processes. Though contexts are typically unconscious, they strongly influence conscious processes. Baars [9] postulated that learning results simply from conscious attention, that is, that consciousness is enough for learning. There’s much more to the theory, including attention, action selection, emotion, voluntary action, meta-cognition, and a sense of self. It serves as a high-level theory of cognition and is useful for the formulation of rudimentary emotions in Chatbots and robot systems.

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15.3.2 The Artificial Prefrontal Cortex In order to understand human emotions and respond appropriately to people, the Chatbot and robot system must be equipped with constructs for concept formation, consciousness, basic emotions, and short-term and long-term memories [10]. The Chatbot’s long-term memories would provide identification, recognition, and categorization functions, as well as identification of feelings [11]. The short-term memories provide preconscious buffers as a workspace for internal activities. A transient episodic memory would also be needed to provide a content-addressable associative memory with a moderately fast decay rate. These software structures could provide the architectural framework for an Artificial Prefrontal Cortex (APC), which provides a sense of emotional intelligence for Chatbot and robot systems that would allow for rapid analysis, reasoning, and reporting capabilities. The APC would facilitate information, intelligence, and memory integration and allow faster accommodation and delivery of knowledge and knowledge characteristics across the Chatbot or robot systems and subsystems [12].

15.3.3 The Artificial Prefrontal Cortex and Cognitive Control The prefrontal cortex has long been suspected to play an important role in cognitive control, in the ability to orchestrate thought and action in accordance with internal goals. Cognitive control stems from the active maintenance of patterns of activity in the prefrontal cortex that represent goals and the means to achieve them [1, 13]. They provide bias signals to other cognitive structures whose net effect is to guide the flow of activity along neural pathways that establish the proper mappings between inputs, internal states, and outputs needed to perform a given task. The Prefrontal Cortex is integral to planning complex cognitive behaviors, personality expression, decision-making, and moderating correct social behavior [11, 14] (see Fig. 15.1). The basic activity of this brain region is the orchestration of thoughts and actions in accordance with internal goals [15]. In order to provide the software constructs for Chatbots and other robot systems to understand and utilize basic emotions in their interactions with humans, we describe an artificial structure within the architecture to provide an APC humanistic functionality and identify the structure, context, artificial feelings, emotions, and their roles within a Chatbot for performing real-world tasks like social interaction. The software structures that would facilitate the APC would be actively involved in every instance of action selection and in each learning event [16]. The pervasive, central role that feelings and emotions play in our proposed control structure of these conscious software agents mimics the roles they play in human cognition, and over time, give rise to clarifying hypotheses about human decision-making and several forms of human learning [17, 18].

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Social Structure * * * *

*

-Defines *

-Is a Member Of

* Authority

-Is Defined BY

-Has

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* *

*

-Employs

Participant

*

*

-Permits * Action

*

Social Fact

-Ensures the Effectiveness of *

*

Skill * *

-Enables

*

*

*

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-Has

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Agent

* * -Validates

*

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Real World Effects (RWE)

*

Fig. 15.1 Structures and control of social behavior

15.3.4 The Artificial Prefrontal Cortex Framework Executive functions carried out by an artificial prefrontal cortex region are represented as core management functions related to overarching abilities that can manage and differentiate among conflicting thoughts, determine good and bad behavior, better and best, same and different, future consequences of current activities, working toward a defined goal, prediction of outcomes, expectation based on actions, and social “control” [19]. The prefrontal cortex is significant when top-down processing is needed. Top-down processing is when a specific given or requested behavior is guided by internal states or intentions otherwise known as the cognitive concept of “mindfulness:” . Mindfulness: an awareness that lets us see things as they truly are without distortion or judgment, giving the most insightful explanation of how mindfulness can change not only our lives, but also the very structure of our brains. In order for our Chatbot to be autonomous and continually learning and improving, we it would need “executive functions” abilities. One of the cognitive concepts that should be employed for future Chatbots and robot systems is the ability to perform top-down processing. To develop an understanding of a given situation, question, or task, and from this

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155

define an internal perception of needed goals/gaps along with prediction of possible outcomes, and subsequently utilize this knowledge to define the appropriate response and/ or behaviors needed to ultimately meet task/question–answer goal. Executive management involves planning, monitoring, evaluating, and revising the Chatbot’s own cognitive processes and discrete outcomes. Strategic knowledge involves knowing what tasks or operations to perform (factual or declarative knowledge), knowing when and why to perform the tasks or operations (conditional or contextual knowledge), and knowing how to perform them (procedural or methodological knowledge). Both executive management and strategic knowledge capabilities are required for the Chatbot or robot to self-regulate its own thinking and learning [20]. Hence, we propose a model for an Artificial Prefrontal Cortex (APC) sub-framework as part of the overall processing infrastructure and discuss the utilization of the Hidden Markov Model and related fuzzy possibilistic logic to drive the system between emotional states.

15.3.5 The Artificial Prefrontal Cortex Architecture Architectural components within the Artificial Prefrontal Cortex provide the governance capabilities that enable the definition and enforcement of cognitive policies governing the content and usage of emotions and cognitive states. Together these define the knowledge and context relationships processed within Chatbot or robot systems. The logical architecture flow for the Artificial Prefrontal Cortex (APC) is shown in Fig. 15.2. To understand the cognitive interactions that occur within an APC, a model was built to drive the PC framework that provides a linkage between the major cognitive states within the cortex [21, 22]. Figure 15.3 illustrates this cognitive processing model, rooted in foundations based on Artificial Intelligence interpretations of Dr. Peter Levine’s Autonomic Nervous System States [23]. Detecting cognitive process information within the Chatbot would begin with sensors and sensory perception techniques (audio and visual capabilities) that capture internal

Has Coordination of Is Represented By

Fulfills

Cognitive Perceptron ISAs

Prefrontal Cortex Represent

Are Fulfilled By

Have Permissions for

Performs

Roles

Activities Are Performed By

Resources Facilitate

Coordinate

Cognitive Process Domains

Fig. 15.2 The APC emotional/cognitive inference flow

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P0(B/B)

P0(A/A)

P02(B/A)

Cognitive/ Emotional State A

Cognitive/ Emotional State B

P0(B/A) P0(A/B) P02(A/B)

P0

A) M) A/ P 0(

2

P0 (A/M)

(B /M ) P0 (M /B )

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M/

P 0(

P02(M/A)

P02(M/B)

Cognitive/ Emotional State M P0(M/M)

A

B

Represents the possibility of B, given the existence of A: P0(B/A)

A

B

Represents the possibility of B, given the possibility of A with confidence bound: P02(B/A)

Fig. 15.3 The APC affected state model

and external information about the system’s physical or cognitive state or behavior. The information is gathered and interpreted by the APC similar to how humans utilize cues to perceive cognitive states or emotions in others. The APC would provide possibilistic inferences for a system to transfer between cognitive states. For simplicity, the APC shown in Fig. 15.3 illustrates only three cognitive/emotional states. Extending the model to include additional states is simply a function of possibilisticstate transitions. The objective operation of an APC is to rapidly transition between cognitive states at any instant, and transition between states based upon possibilistic measures (fuzzy logic based). These possibilistic parameters evolve over time, driven by learning algorithms that are continuously re-evaluated and affected both by normal and emotional memories [24]. Cognitive state transition-related conditional possibilistic measures would provide the APC with abilities to make executive-level plans and processing to move between cognitive states, each of which has its own set of priorities, goals, and motivations. An APC helps meet the objective of creating an internal environment of self-evolving Chatbots and robots that could be used in a variety of applications. In order to evolve beyond the current AI system paradigm, we propose future Chatbots and robots, employ an APC comprising the following capabilities, processes, and execution behaviors like a human prefrontal cortex:

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157

Cue Familiarity: cue familiarity is the ability of the system to evaluate its ability to answer a question before trying to answer it [11]. In cue familiarity, the question (cue) and not the actual memory (target) become crucial for making cognitive judgments. This implies that judgments regarding cognitive processing and decisions would be based on the system’s level of familiarity with the information provided in the cue. This executivelevel, top-down cognitive judgment requires APC abilities that allow a Chatbot or robot to judge whether the answer to a question is known, or whether the system is already familiar with the topic or mission, allowing the system to judge unfamiliar terms or conditions. Cognitive Accessibility: suggests that a system’s memory will be more accurate and more rapidly available for use when the ease of cognitive processing (accessibility) is correlated with emotional memories. For an APC, we propose that the quality of information retrieval depends on the system’s density of knowledge on the topic or subject or individual elements of informational content about a topic. Individual elements of topical information can differ in strength while the speed of access is tied to both density of knowledge and level of emotional memory when a system responds to the information cues. Cognitive Competition: comprises three principles: . An AI cognitive processing system (the brain) is activated by a variety of inputs (sensors), perceiving text, audio, and visual pictures and video. Hence, different types of information are sensed simultaneously. . Competition develops over time as simultaneous data is processed within the multiple cognitive processing subsystems and is adjudicated by APC software possibilistic algorithms. . Competition is assessed utilizing top-down neural priming. Cognitive Interaction: Combines cue familiarity and cognitive accessibility. In cognitive interaction, once cue familiarity fails to provide enough information to make cognitive inferences, cognitive accessibility accesses extended memories and may employ stored emotional memory cues to access additional information to attempt to make the required cognitive inferences. This may result in a slower response time than with cue familiarity alone. Even human reaction times can be slower when the situation requires additional learning [25].

15.3.6 The Artificial Prefrontal Cortex Model To begin the process of development and testing of an APC, we should start with at most three basic emotional/cognitive states and test these extensively before allowing the public to interact with them. An example of these states is described below:

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1. Happy: a homeostasis state where things are not changing rapidly, there is a good balance between processing needs and overall resource management and the system is not processing information that would cause issues within itself; mindful equanimity [26]. 2. Anxious: Perception is that the conversation is changing to something uncomfortable, based on the Natural Language Processing output. More resources are required to understand and process the inputs, overall tone of the conversation(s) is changing quickly. Perception or prediction is that something uncomfortable might happen. 3. Frustrated: The perception of an inability to control the situation. The conversation is changing tone rapidly and taking an increasing amount of resources to process the language and changes in tone of the language. If the system perceives that someone in the conversation, or several people in the conversation, are exhibiting frustration, by the tone of their inputs, and the system has constrained resources (cognitive economy), and the conversation is changing rapidly (cognitive velocity), then the system perceives the overall tone of the conversations to be frustrated. Testing of an initial APC model might involve feeding a series of Facebook/Twitter/ Instagram conversation posts through a Natural Language Processor and then into the APC model. Based on the perception of the APC as to the overall tone of the conversations, the APC would decide whether a change in cognitive/emotional state was happening. Figure 15.4 illustrates a notion of the testing process. As the intelligence and cognitive capabilities of Chatbots and robot systems increase, Cognitive Mediation is an important system aspect for overall cognitive management and must be included in any future system. The APC model we have discussed here is the first step toward the creation of a fully cognitive Chatbot and robot systems. Much testing is needed to expand the number of cognitive states and fuzzy metrics required to determine cognitive state change, but we believe that the use of an APC in the future can track, predict, and adjust cognitive states within a Chatbot or robot system as it monitors activities across its interfaces.

15.4

Putting It All Together

While the future of Chatbots is not known, there is much excitement, promise, fear, and concern over how they will evolve. While putting controls and constraints on Chatbots and AI systems in general is problematic at best, we should strive to discuss, develop, test, re-evaluate, and evolve solutions that will benefit humankind while minimizing the negative effects (if possible). I hope this book sparks a wide range of curiosity and thoughts (both good and troubling), and starts a series of discussions and working groups to begin solving the issues I have presented here. This is by no means intended to be an exhaustive look at the overall world of Chatbots, but merely the beginning of what will be years

15.4

Putting It All Together

Textual Inputs

159

Natural Language Processing

Cognitive Velocity Calculations

Determine Initial Cognitive State

Cognitive Economy Calculations

Compute 1 st and 2nd Order Fuzzy Metrics

Does Cognitive State Change?

No

Set New Cognitive State Actions

Fig. 15.4 Notional APC test procedure

and decades of continual software development, community and working group oversight, congressional involvement, and ultimately discussions across national boundaries to approach the problem before it gets out of control. Time will tell… again, what we don’t want is: No Dave, I don’t believe I can do that.2

2 From 2001 A Space Odyssey.

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References 1. Gilmore J (2018) Brain circuits for successful emotional development established during infancy. National Institute of Health 2. Slama K, Ray A et al (2022) Training language models to follow instructions with human feedback. Adv Neural Inf Process Syst 35:27730–27744 3. Crowder J, Friess S (2010) Artificial neural emotions and emotional memory. In: Proceedings of the 2010 international conference on artificial intelligence. Las Vegas, NV 4. Crowder J, Friess S, Carbone J (2019) Artificial psychology: psychological modeling and testing of AI systems. Springer International, New York, NY, ISBN 978-3-030-17081-3 5. Crowder J, Carbone J, Friess S (2013) Artificial cognition architectures. Springer International, New York, NY, 978-1-4614-8072-3. 17 6. Crowder J, Friess S, Carbone J (2019) Anytime learning: a step towards life-long AI machine learning. In: Proceedings of the 2019 international conference on artificial intelligence. Las Vegas, NV, pp 16–21 7. Crowder J, Friess S, Carbone J (2019) Implicit learning in artificial intelligent systems: the coming problem of real cognitive AI. In: Proceedings of the 2019 international conference on artificial intelligence. Las Vegas, NV, pp 48–53 8. Mobahi H, Ansari S (2003) Fuzzy perception, emotion and expression for interactive robots. In: IEEE international conference system, man and cybernetics 9. Baars B (1997) In the theater of consciousness. Oxford University Press, Oxford 10. Eichenbaum H (2002) The cognitive neuroscience of memory. Oxford University Press, New York 11. Yang Y, Raine A (2009) Prefrontal structural and functional brain imaging findings in antisocial, violent, and psychopathic individuals: a meta-analysis. Psychiatry Res 174(2):81–88. https:// doi.org/10.1016/j.pscychresns.2009.03.012.PMID19833485 12. Gupta R, Srinivasan N (2009) Cognitive neuroscience of emotional memory. Advances in developmental neuroscience and imaging. Anamaya Publishers, New Delhi, India 13. Dalgleish TJ (2004) The emotional brain. Nat Rev Neurosci. https://doi.org/10.1038/nm1432 14. Holland P, Gallagher M (2004) Amygdala-frontal interactions and reward expectancy. Curr Opin Neurobiol 14:148–155 15. Bossaerts P, O’Doherty J, Adachi R, Dunne S, Suzuki S (2015) Neural mechanisms underlying human consensus decision-making. Neuron, Elsevier, New York, NY 16. Newell A (2003) Unified theories of cognition. Harvard University Press, Cambridge, MA 17. Damasio A (1994) Descarte’s error: emotion, reason, and the human brain. Gosset/Putnam, New York 18. Davis M, Whalen P (2001) The amygdala: vigilance and emotion. Mol Psychiatry 6:13–34 19. DeYoung C, Hirsh J, Shane M, Papademetris X, Rajeevan N, Gray J (2010) Testing predictions from personality neuroscience. Psychol Sci 21(6):820–828 20. Crowder J (2003) Machine learning: intuition (concept) learning in hybrid genetic/fuzzy/neural systems. NSA Technical Paper CON_0013_2003_009 21. Crowder J, Friess S (2010) Artificial neural diagnostics and prognostics: self-soothing in cognitive systems. In: Proceedings of the 12th annual International Conference on artificial intelligence. Las Vegas, NV 22. Crowder J, Friess S (2010) Artificial neural emotions and emotional memory. In: Proceedings of the 12th annual international conference on artificial intelligence. ICAI’10 23. Levine P (1997) Walking the tiger: healing trauma. North Atlantic Books, Berkeley, CA

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Index

A Accuracy, 40, 47, 95 Alexa, 8, 9, 26, 35, 41, 45 Algorithm bias, 116 Anthropomorphize, 8 ARPAnet, 5, 7 Artificial consciousness, 152 Artificial emotions, 11, 31, 53, 55, 56, 67, 149–151 Artificial Intelligence, 1, 2, 5, 6, 15, 18, 19, 21, 25, 30, 33, 34, 39, 45, 51, 53, 57, 71, 72, 79, 85, 88, 91, 97, 101, 113–115, 117, 121, 122, 129, 130, 137, 149, 152, 155 Artificial Intelligence Markup Language, 1, 8 Artificial intelligent entities, 2, 81, 84–87, 103, 151 Artificial limbic system, 55, 56, 104 Artificial Linguistic Internet Computer Entity (A.L.I.C.E.), 1, 8, 25 Artificial Psychology, 31, 54, 57, 141 Avatar, 2, 40, 53, 55, 57, 68, 98, 99, 102–107

B Blockchain, 140

C Chatbot, 1–3, 6–11, 15–19, 21–27, 30, 31, 33–37, 39–41, 45–48, 51–58, 67, 68, 71–74, 79–88, 91–105, 107, 108, 113–115, 117, 119, 121–125,

128–132, 137–142, 144, 145, 149–158 ChatGPT, 2, 8, 18, 25, 27–31, 36, 53, 57, 66, 67, 72, 80, 83, 88, 92, 94, 95, 98, 107, 108, 114, 117, 118, 122, 124, 130, 138, 140, 150, 152 Cognition-based Chatbot, 30 Cognitive accessibility, 157 Cognitive competition, 157 Cognitive interaction, 101, 155, 157 Cognitive priming, 85 Communication, 16, 26, 35–37, 41, 46, 47, 53, 71–74, 87, 96, 100, 102–104, 152 Conceptual fluency, 85 Context-based learning, 56 Cortana, 8 Counselor training, 98–101, 103, 104, 107 Cue familiarity, 157 Cultural competence, 100 Cyber insurance, 124 Cyberspace Administration of China, 129

D Data collection, 40, 41 Data tagging, 116 Deep fake, 123 Deep synthesis, 129 Department of defense, 121, 130, 131 Digital assistant, 1, 8, 9, 18, 71 Disinformation, 95, 123, 130 DNA, 3

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024 J. Crowder, AI Chatbots, Synthesis Lectures on Engineering, Science, and Technology, https://doi.org/10.1007/978-3-031-45509-4

163

164 E ELIZA, 6–8, 16, 98 Emergent behavior, 10, 91, 98, 128 Emotional contagion, 67 Emotional intelligence, 53, 55–65, 117, 150, 151, 153 Entertainment industry, 9, 39, 72, 74, 109 Ethical decision loop, 137, 145 Ethical safeguards, 80 Explicit memory, 81, 82, 86

F Facial recognition, 114, 115, 130, 150 Fake news, 95 Free speech, 123 Frequently asked questions, 40 Frontal cortex, 54

G Gender bias, 10, 113, 114 Governance, 101, 129, 130, 144, 155

H Hamiltonian, 126, 127, 141 Hebbian learning model, 126, 141 Human-Chatbot communication, 35–37, 48, 98, 140 Human-Chatbot interaction, 26, 35–37 Human-mentored software, 97, 105 Human Needs Engineering, 51

I Image generation, 107 Implicit learning, 67, 80–88 Implicit memory, 80–86, 88, 104 Inferencing, 56, 67, 82, 86 Information technology, 91 Internet of Things, 74

K Knowledgebase, 17, 26, 94 Knowledge Relativity Thread, 121, 126, 127

Index L Lagrange function, 127 Language detection, 26 Large language model, 91, 95, 121, 128, 137, 139 Linguistic accuracy, 46, 47

M Machine learning, 2, 21, 25, 30, 33, 39, 45, 81, 88, 91, 97, 113–116, 121, 123, 124, 126, 128, 137, 143, 149, 151 Machine learning-based Chatbots, 23, 25 Measurement bias, 117 Measure of effectiveness, 97 Menu-based Chatbot, 22 Mindfulness, 154 Misinformation, 10, 46, 80, 121–124, 128 Multi-media, 72–74, 122, 123 Music generation, 108

N Natural language generation, 21, 26, 45, 72, 91, 92, 95, 97, 113, 114, 121, 129, 137, 149 Natural language processing, 2, 15, 16, 18, 21, 25, 33, 39, 45, 51, 72, 91, 97, 113, 116, 121, 137, 139, 149, 158 Natural language understanding, 21, 25, 26, 33, 39, 45, 72, 91, 97, 113, 116, 122, 137, 149 Neural network, 30

O OpenAI, 92, 95, 98, 108

P PARRY, 8 Perplexity, 114, 115 Phishing, 92, 94 Possibilistic, 124, 155–157 Pre-frontal cortex, 97, 100, 101, 105, 149–157 Psychology, 35–37, 52, 54, 57, 87, 98, 104, 107, 143

Index Q Qualitative data, 139, 140 Quantitative data, 139

R Regulatory policies, 129 Reinforcement learning, 55, 116, 151 Reliability, 36, 37, 95, 125, 126 Replika, 2, 25, 27–29, 53, 57, 68 Robot, 2–4, 6, 11, 25, 34, 37, 52, 57, 67, 82, 86, 87, 137, 138, 149–158 Rule-based Chatbot, 23, 24

S Sample bias, 116 Siri, 8, 9, 26, 35, 41, 45 Social activism, 139 Social behavior, 34, 153, 154 Social constructs, 56, 57 Social engineering, 124 Software, 2, 6, 9, 10, 15, 16, 18, 22, 24–26, 30, 31, 46, 53, 55, 72, 79, 92, 94, 95, 97,

165 98, 100, 101, 108, 114–117, 122, 131, 138, 142, 150–153, 157, 159 Supervised learning, 116 Supply chain, 40 Synthetic AI Nervous/Limbic-Derived Instances (SANDI), 15, 17

T Toulmin, 137, 141–143 Transactional resiliency, 140 Transmission Control Protocol and Internet Protocol (TCP/IP), 2, 7 Transportation Safety Administration, 113 Troll-bot, 123

U User experience, 25, 45, 46, 94

V Voice-based Chatbots, 26, 29, 30