Publications and Research

The Missing Proximate Cause Element in Discrimination Law (in progress)

Unfair Artificial Intelligence: How FTC Intervention Can Succeed Where Discrimination Law Fails (with Solon Barocas) (in progress)

The Legal Construction of Black Boxes: How Machine Learning Practice Informs Foreseeability (with Suresh Venkatasubramanian and I. Elizabeth Kumar) (in progress)

An Institutional View of Algorithmic Impact Assessments, 35 Harvard Journal of Law & Technology __ (forthcoming 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 (with Solon Barocas and Manish Raghavan)

Fairness and Abstraction in Sociotechnical Systems, 2019 ACM Conference on Fairness, Accountability, and Transparency (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)