Andrew D. Selbst

Assistant 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 an Assistant Professor of Law at UCLA School of Law. 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 has also previously taught as an Adjunct Professor at Fordham Law School.

Selbst’s research examines the relationship between law, technology, and society. Drawing on resources from computer science, sociology, and STS, he seeks to understand how the creation, use, and proliferation of different technologies can interfere with existing legal regimes, and how legal actors can most usefully anticipate or respond to the social effects of new technology. Over the last several years, Selbst’s research has focused on the effects of machine learning and artificial intelligence on varied legal regimes, including discrimination, policing, credit regulation, data protection, and tort law.

Selbst received S.B. degrees in Physics and in Electrical Science and Engineering and a M.Eng. degree from MIT. Selbst worked as a design engineer at Cirrus Logic, Inc. and Analog Devices, Inc. before attending law school at the University of Michigan, where he received his J.D. cum laude and served as Executive Editor of the University of Michigan Journal of Law Reform. Following law school, Selbst was a Privacy Research Fellow at NYU School of Law’s Information Law Institute, an Alan Morrison Supreme Court Assistance Fellow at Public Citizen Litigation Group, and a Senior Associate in Hogan Lovells US LLP’s Communications group. 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.

Selbst’s publications have appeared or are forthcoming in Boston University Law ReviewCalifornia Law ReviewCardozo Law ReviewFordham Law ReviewGeorgia Law ReviewHarvard Journal of Law and Technology, the Ohio State Law Journal, the University of Pennsylvania Law ReviewInternational Data Privacy Law, and the ACM Conference on Fairness, Accountability and Transparency, among others. Selbst is also the coauthor of a forthcoming casebook on Artificial Intelligence Law.

Website | Professor Selbst's CV


  • Books
    • Artificial Intelligence Law (with Paul Ohm and Margot Kaminski). (in progress) Book Info.
  • Articles And Chapters
    • Distinguishing Generative and Predictive AI in Regulation (with Solon Barocas, Suresh Venkatasubramanian & Jennifer Wang). (in progress).
    • Artificial Intelligence and the Discrimination Injury. (in progress).
    • Deconstructing Design Decisions: Why Courts must Interrogate Machine Learning and Other Technologies (with I. Elizabeth Kumar & Suresh Venkatasubramanian), 85 Ohio St. L.J. 415 (forthcoming 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 U. Pa.L.Rev (2023). Full Text
    • The Fallacy of AI Functionality. 2022 ACM Conference on Fairness, Accountability, and Transparency 959 (with Inioluwa Deborah Raji, I. Elizabeth Kumar & Aaron Horowitz).
      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
  • Other
    • 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, EEOC at 50: Progress and Continuing Challenges in Eradicating Employment Discrimination (with Solon Barocas), July 1, 2015.