Acknowledgments

This textbook would not exist without the work of a vast and growing community of researchers, advocates, practitioners, and affected communities who have made AI ethics a serious field of inquiry.

Intellectual Debts

The foundations of this book rest on the work of scholars who have shaped AI ethics as a discipline. The pioneering algorithmic fairness research of Solon Barocas, Moritz Hardt, and Aaron Roth gave the field mathematical rigor. Kate Crawford's Atlas of AI demonstrated that AI is not only a software problem but an environmental, labor, and political one. Cathy O'Neil's Weapons of Math Destruction brought algorithmic harm to a general audience before most organizations were paying attention. Virginia Eubanks's Automating Inequality documented the specific ways automated systems target vulnerable communities. Safiya Umoja Noble's Algorithms of Oppression exposed racial and gender bias embedded in information systems. Ruha Benjamin's concept of the "New Jim Code" provided essential vocabulary for understanding race and technology.

In philosophy and ethics, the work of Luciano Floridi on information ethics, Onora O'Neill on trust and accountability, and Martha Nussbaum on capabilities and human dignity provide crucial frameworks. In law, the scholarship of Frank Pasquale, Ryan Calo, and Danielle Keats Citron has been indispensable for understanding how legal systems are (and aren't) responding to AI.

The researchers at organizations including the AI Now Institute, the Algorithmic Justice League, the Partnership on AI, the Alan Turing Institute, and the Future of Life Institute have produced the empirical record on which much of this book depends. The technologists who built LIME, SHAP, Fairlearn, AI Fairness 360, and other open-source tools for fairness and explainability deserve recognition for making ethical analysis practically possible.

Journalists at MIT Technology Review, Wired, The Markup, ProPublica, and many other outlets have documented AI failures in real time, often before academic research caught up. Their investigative work — including the landmark ProPublica COMPAS investigation, The Markup's coverage of algorithmic redlining, and MIT Technology Review's reporting on facial recognition — forms an essential part of the empirical record.

Community Acknowledgments

The communities most directly harmed by unjust AI systems — people who have been denied jobs, credit, housing, or freedom by algorithmic decisions — have demanded accountability and driven the most important reforms in this field. This book honors their experience and their labor.

A Note on Attribution

Throughout this textbook, claims are supported with citations to real research. Where case studies draw on specific documented incidents, sources are identified. In a field where misinformation circulates freely, accuracy in attribution matters.

Errors and omissions remain the responsibility of the author.