Cornell Law Review

Article

Constitutional Rights in the Machine-Learning State

Aziz Z. Huq

Frank and Bernice J. Greenberg Professor of Law, University of Chicago Law School. 

This Article offers a start to the larger project of developing a general account of substantive rules and enforcement mechanisms to promote due process, privacy, and equality norms in the machine-learning state. Cataloging notable state and municipal adoptions of machine-learning tools, it considers how existing constitutional norms can be recalibrated (in the case of due process and equality) or retooled (in the case of privacy). It further reexamines the enforcement regime for constitutional interests in light of machine learning’s dissemination. Today, constitutional rights are (largely) enforced through discrete, individual legal actions. Machine learning’s normative implications arise from systemic design choices. The retail enforcement mechanisms that currently dominate the constitutional remedies context are therefore particularly ill fitting. Instead, a careful mix of ex ante regulation and ex post aggregate litigation, which are necessary complements, is more desirable.

Nov 2020