Artificial Intelligence and the Value Alignment Problem: A Philosophical Introduction

Table of Contents:
List of Figures List of Tables List of Cases Preface Acknowledgements Introduction I Basic Concepts 1 A Brief History of Artificial Intelligence 1.1 The Idea of AI 1.2 The Invention of AI 1.3 First-Wave AI: False Promises 1.4 Second-Wave AI: Empty Threats 1.5 Third-Wave AI: Deep Hype 1.6 Summary 2 Artificial Intelligence Today 2.1 Neural Network Architectures 2.2 Data and Datasets 2.3 Machine Learning Methods 2.4 Objectives, Goals, and Values 2.5 Learning Algorithms 2.6 Evaluation 2.7 Scaling Laws 2.8 Summary 3 The Value Alignment Problem 3.1 The Standard Definition of Value Alignment 3.2 Adding Sophistication to the Standard Definition 3.3 The Principal-Agent Framework 3.4 The Value Alignment Problem for Artificial Intelligence 3.5 Benefits of the Structural Definition II Axes of Value AlignmentIntroduction to Part II 4 Objectives 4.1 Proxies and Abstractions 4.2 Insights from the Structural Definition 4.3 Bias and Fairness 4.4 Algorithmic Bias 4.5 The Social Character of Objectives 4.6 Summary 5 Information 5.1 Informational Asymmetries, Economic and Artificial 5.2 Transparency and Opacity 5.3 Explainability, Interpretability, and Understanding 5.4 Data and Datasets 5.5 Interaction Effects 5.6 Summary 6 Principals 6.1 Principals and Their Goals 6.2 The Values of Humanity 6.3 The Values Encoded in AI Research 6.4 The Human Costs of Artificial Intelligence 6.5 Interaction Effects 6.6 Summary III Approaches to Value AlignmentIntroduction to Part III 7 AI Safety 7.1 Adversarial Examples 7.2 Concrete Problems in AI Safety 7.3 Mitigating Risk 7.4 AI Safety and the Value Alignment Problem 7.5 Summary 8 Machine Ethics 8.1 Artificial Moral Agency 8.2 Our Best Normative Theories 8.3 Technical Approaches to Artificial Moral Agency 8.4 Critiques of Artificial Moral Agency 8.5 Related Concepts 8.6 Summary IV Mitigating MisalignmentIntroduction to Part IV 9 Measuring Degrees of Alignment 9.1 Benchmarking 9.2 Benchmarking Ethics 9.3 Aligning Values 9.4 Degrees of Alignment 9.5 The Scaling Hypothesis for Value-Aligned AI 9.6 Summary 10 Normativity and Language 10.1 Linguistic Communication 10.2 Language in Human Value Alignment 10.3 Language, Value Alignment, and Information Transfer 10.4 Objective Functions and Value Proxies 10.5 Implications 10.6 Summary 11 Values and Value-Ladenness 11.1 The Value-Free Ideal of Science 11.2 Against the Value-Free Ideal 11.3 Values and Value Alignment 11.4 Optimism 11.5 Regulation 11.6 Summary 12 Conclusion V Appendix A Superintelligence and Control A.1 Superintelligence A.2 Paths to Superintelligence A.3 Forms of Superintelligence A.4 Intelligence Explosion and the Singularity A.5 Existential Risk A.6 Intelligence, Motivation, and Goals A.7 The Control Problem A.8 Criticism A.9 Summary References Index

About the Author :
Travis LaCroix is Assistant Professor of Philosophy at Durham University and a faculty affiliate at the Schwartz Reisman Institute for Technology and Society (University of Toronto).

Review :
“Travis LaCroix’s book on value alignment is, without a doubt, the best I have read on AI ethics. I highly recommend it to anyone interested in the ethics of artificial intelligence. The text is intellectually rigorous, and many of its ideas are genuinely novel. I found his discussion of measuring value alignment particularly insightful, along with the appendix on superintelligence and the control problem, which provides valuable depth to the topic.” — Martin Peterson, Texas A&M University “LaCroix’s Artificial Intelligence and the Value Alignment Problem offers an insightful overview and evaluation of the predicament we find ourselves in with respect to machine learning. The book doesn't shy away from engaging with the mathematical background of these challenges, but it does so in a way that’s intelligible to readers with limited mathematical experience. The structural characterization of the alignment problem(s) provides a great conceptual tool for exploring the ways that values are (or fail to be) incorporated in machine learning systems. The discussions of values are also inclusive, incorporating views from Western, Eastern, and Indigenous philosophy. This book offers an up-to-date introduction to the topic at a level suitable for undergraduates while also providing a novel analytic tool for anyone already working in the area of AI ethics.” — Gillman Payette, University of Calgary