Cornell Law School Logo - white on transparent background

Print 105 Cornell Law Review Issue 7


Constitutional Rights in the Machine-Learning State

Aziz Z. Huq

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

, , ,

19 Nov 2020

A new class of “machine learning” tools is able to make allegedly better predictions and inferences from data than has previously seemed feasible. For the state, machine learning is a powerful and supple device to reveal citizens’ hidden beliefs, actions, and expected behaviors. Its deployment to allocate investigative resources, welfare benefits, and coercive penalties to particular individuals, though, can implicate due process, privacy, and equality interests. The substantive doctrinal frameworks and enforcement regimes for those entitlements, however, arose in the context of human action. Neither is tailored to a machine-learning context. 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.

To read more, click here: Constitutional Rights in the Machine-Learning State.