Professor of Law
- S.B. Massachusetts Institute of Technology, 2004
- M.Eng. Massachusetts Institute of Technology, 2005
- J.D. University of Michigan Law School, 2011
- UCLA Faculty Since 2020
Andrew Selbst is a Professor of Law at UCLA School of Law. He has also served as the William J. Friedman and Alicia Townsend Friedman Visiting Professor of Law at Harvard Law School and has taught at Fordham Law School.
Professor Selbst studies the legal concepts through which law understands computational systems. Drawing on computer science, sociology, and science and technology studies, his scholarship examines how foundational legal ideas—including discrimination, fairness, explanation, responsibility, and speech—apply when human activity is increasingly mediated by computational systems. His work explores how legal doctrine gives technological systems legal meaning and, in doing so, shapes both legal outcomes and the governance of emerging technologies.
Applying this perspective, Professor Selbst's scholarship has influenced debates over algorithmic fairness, explainability, automated decision-making, and AI governance in both the United States and Europe. His articles have appeared in leading venues, including the Berkeley Technology Law Journal, Boston University Law Review, California Law Review, Cardozo Law Review, Fordham Law Review, Georgia Law Review, Harvard Journal of Law and Technology, the Ohio State Law Journal, the University of Pennsylvania Law Review, International Data Privacy Law, the ACM Conference on Fairness, Accountability and Transparency, and the ACM Symposium on Computer Science and Law, and has been cited by courts, government agencies, and scholars in law, computer science, and related fields. Professor Selbst is also the coauthor of a forthcoming casebook on Artificial Intelligence Law.
Professor Selbst earned his J.D. cum laude from the University of Michigan Law School, as well as S.B. degrees in Physics and in Electrical Science and Engineering and an M.Eng. degree from the Massachusetts Institute of Technology. Prior to joining UCLA Law, he was a Postdoctoral Scholar at the Data & Society Research Institute and a Visiting Fellow at Yale Law School’s Information Society Project. He clerked for the Honorable Dolly M. Gee of the United States District Court for the Central District of California and the Honorable Jane R. Roth of the United States Court of Appeals for the Third Circuit. Before law school, he designed circuits.
Bibliography
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- Artificial Intelligence Law (with Margot Kaminski and Paul Ohm). (Foundation Press, forthcoming 2026) Book Info.
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- AI and the Doctrine of Speakerless Speech. (with Margot Kaminski) (in progress)
- Artificial Intelligence and the Discrimination Injury, 78 Florida Law Review _ (forthcoming 2026). Full Text
- Responsible AI on the Ground: Six Years of Empirical Research on Industry RAI Practices. (with Agathe Balayn, Solon Barocas, Wesley Deng, Motahhare Eslami, Kenneth Holstein, Jason Hong, Hanna Wallach, & Jennifer Wortman Vaughan (under review)
- An American's Guide to the EU AI Act (with Margot E. Kaminski), 40 Berkeley Technology Law Journal 1081 (2025). Full Text
- The OMB Artificial Intelligence Memoranda (with Sorelle A. Friedler), 40 Berkeley Technology Law Journal 1237 (2025). Full Text
- Distinguishing Task-Specific and General-Purpose AI in Regulation. (with Solon Barocas, Suresh Venkatasubramanian & Jennifer Wang) CSLAW ’26: Proceedings of the Symposium on Computer Science and Law 185 (2026) Full Text
- Deconstructing Design Decisions: Why Courts must Interrogate Machine Learning and Other Technologies (with I. Elizabeth Kumar & Suresh Venkatasubramanian), 85 Ohio State Law Journal 415 (2024). Full Text
- Introduction: Generating Governance—An Essay Series on Strategies and Challenges in AI Regulation, 71 UCLA Law Review Discourse 134 (2024). Full Text
- Unfair Artificial Intelligence: How FTC Intervention Can Overcome the Limitations of Discrimination Law (with Solon Barocas), 171 University of Pennsylvania Law Review (2023). Full Text
- The Fallacy of AI Functionality. (with Inioluwa Deborah Raji, I. Elizabeth Kumar & Aaron Horowitz) ACM Conference on Fairness, Accountability, and Transparency 959 (2022).
Full Text - An Institutional View Of Algorithmic Impact Assessments, 35 Harvard Journal of Law & Technology 117 (2021). Full Text
- Negligence and AI’s Human Users, 100 Boston University Law Review 1315 (2020). Full Text
- The Hidden Assumptions Behind Counterfactual Explanations and Principal Reasons (with Solon Barocas and Manish Raghavan), 2020 ACM Conference on Fairness, Accountability and Transparency (FAccT) (2020). Full Text
- Fairness and Abstraction in Sociotechnical Systems (with danah boyd, Sorelle Friedler, Suresh Venkatasubramanian, Janet Vertesi), 2019 ACM Conference on Fairness, Accountability and Transparency (FAT*) (2019). Full Text
- The Intuitive Appeal of Explainable Machines (with Solon Barocas), 87 Fordham Law Review 1085 (2018). Full Text
- Disparate Impact in Big Data Policing, 52 Georgia Law Review 109 (2017). Full Text
- Meaningful Information and the Right to Explanation (with Julia Powles), 71 International Data Privacy Law 233 (2017). Full Text
- A Mild Defense of Our New Machine Overlords, 70 Vanderbilt Law Review En Banc 87 (2017). Full Text
- Big Data’s Disparate Impact (with Solon Barocas), 104 California Law Review 671 (2016). Full Text
- Contextual Expectations of Privacy, 35 Cardozo Law Review 643 (2013). Full Text
- The Journalism Ratings Board: An Incentive-Based Approach to Cable News Accountability, 44 University of Michigan Journal of Law Reform 467 (2011). Full Text
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- Comment of October 18, 2019 on HUD Disparate Impact Rulemaking (with Michele Gilman). Full Text
- A New HUD Rule Would Effectively Encourage Discrimination by Algorithm, Slate (Aug. 19, 2019). Full Text
- The Legislation That Targets the Racist Impacts of Tech (with Margot Kaminski), NY Times (May 7, 2019). Full Text
- Accountable Algorithmic Futures: Building empirical research into the future of the Algorithmic Accountability Act (with Madeleine Clare Elish and Mark Latonero), Points (Data & Society Blog) (April 19, 2019). Full Text
- Testimony By Data & Society to the NYC Council’s Committee on Technology (with Janet Haven), April 4 2019. Full Text
- Supreme Court Must Understand: Cell Phones Aren’t Optional (with Julia Ticona), Wired (Nov. 29, 2017). Full Text
- Written Testimony (with Solon Barocas), EEOC at 50: Progress and Continuing Challenges in Eradicating Employment Discrimination, July 1, 2015.