Injury, Liability, and Responsibility in Discrimination Law (in progress)
Deconstructing Design Decisions: Why Courts must Interrogate Machine Learning and Other Technologies (with Suresh Venkatasubramanian and I. Elizabeth Kumar) (in progress)
Unfair Artificial Intelligence: How FTC Intervention Can Overcome the Limitations of Discrimination Law, 171 University of Pennsylvania Law Review (forthcoming 2023) (with Solon Barocas)
The Fallacy of AI Functionality, 2022 ACM Conference on Fairness, Accountability, and Transparency 959 (with Inioluwa Deborah Raji, I. Elizabeth Kumar & Aaron Horowitz)
An Institutional View of Algorithmic Impact Assessments, 35 Harvard Journal of Law & Technology 117 (2021)
Negligence and AI’s Human Users, 100 Boston University Law Review 1315 (2020)
The Hidden Assumptions Behind Counterfactual Explanations and Principal Reasons, 2020 ACM Conference on Fairness, Accountability, and Transparency 80 (with Solon Barocas and Manish Raghavan)
Fairness and Abstraction in Sociotechnical Systems, 2019 ACM Conference on Fairness, Accountability, and Transparency 59 (with danah boyd, Sorelle Friedler, Suresh Venkatasubramanian, and Janet Vertesi)
The Intuitive Appeal of Explainable Machines, 87 Fordham Law Review 1085 (2018) (with Solon Barocas)
Disparate Impact in Big Data Policing, 52 Georgia Law Review 109 (2017)
Meaningful Information and the Right to Explanation, 7 International Data Privacy Law 233 (2017) (with Julia Powles)
A Mild Defense of Our New Machine Overlords, 70 Vanderbilt Law Review En Banc 87 (2017) (invited response)
Big Data’s Disparate Impact, 104 California Law Review 671 (2016) (with Solon Barocas)
Contextual Expectations of Privacy, 35 Cardozo Law Review 643 (2013)
Andrew Selbst 