Constitutional Rights in the Machine Learning State

53 Pages Posted: 23 Jun 2020 Last revised: 1 Sep 2020

See all articles by Aziz Z. Huq

Aziz Z. Huq

University of Chicago - Law School

Date Written: May 28, 2020

Abstract

A new class of “machine learning” tools is able to make better predictions and inferences from data than was previously feasible. For the state, machine learning is a powerful and supple device to reveal citizens’ beliefs, actions, and expected behaviors. Its deployment to allocate investigative resources, material benefits, and coercive penalties to particular individuals, though, can implicate due process, privacy, and equality interests. Substantive doctrinal frameworks and enforcement regimes for those entitlements, however, arose in the context of human action. Neither is apt for a machine learning context. This Article offers a start to the larger project of developing a more general account of substantive rules and enforcement mechanisms to promote due process, privacy, and equality norms in the machine learning state. After cataloging notable state and municipal adoptions of machine learning tools, it considers how existing constitutional norms can be re-calibrated (in the case of due process and equality) or retooled (in the case of privacy). It further reexamines the enforcement regime for constitutional interests. 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 inapt. Instead, a careful mix of ex ante regulation and ex post aggregate litigation, which are necessary complements, is more desirable.

Keywords: algorithms, machine learning, due process, equal protection

Suggested Citation

Huq, Aziz Z., Constitutional Rights in the Machine Learning State (May 28, 2020). Cornell Law Review, Vol. 105, 2020, U of Chicago, Public Law Working Paper No. 752, Available at SSRN: https://ssrn.com/abstract=3613282 or http://dx.doi.org/10.2139/ssrn.3613282

Aziz Z. Huq (Contact Author)

University of Chicago - Law School ( email )

1111 E. 60th St.
Chicago, IL 60637
United States

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