Written for an interdisciplinary audience, this book provides strikingly clear explanations of the many difficult technical and moral concepts central to discussions of ethics and AI. In particular, it serves as an introduction to the value alignment problem: that of ensuring that AI systems are aligned with the values of humanity. LaCroix redefines the problem as a structural one, showing the reader how various topics in AI ethics, from bias and fairness to transparency and opacity, can be understood as instances of the key problem of value alignment. Numerous case studies are presented throughout the book to highlight the significance of the issues at stake and to clarify the central role of the value alignment problem in the many ethical challenges facing the development and implementation of AI.
Comments
“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