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PreprintCited by 21Ethics and Social Impacts of AIDigitalization, Law, and RegulationLaw, Rights, and Freedoms

Constitutional Rights in the Machine Learning State

Open access
Huq, Aziz Z.·2020·SSRN Electronic Journal
DOI 10.2139/ssrn.3613282
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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.

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  • A number of prior works, particularly from a U.S.-centric perspective, have connected ML to legal frameworks for human rights, especially antidiscrimination.

    “A number of prior works, particularly from a U.S.-centric perspective, have connected ML to legal frameworks for human rights, especially antidiscrimination [47, 48, 64, 70, 72, 140, 142, 146].”

    From Data Governance in the Age of Large-Scale Data-Driven Language Technology (2022) · cites this paper as [72]

  • These works often focus on the difficulties of constraining algorithmic discrimination in many contexts, proposing alternative legal frameworks that would allow for more regulatory enforcement of algorithmic bias.

    “These works often focus on the difficulties of constraining algorithmic discrimination in many contexts, proposing alternative legal frameworks that would allow for more regulatory enforcement of algorithmic bias.”

    From Data Governance in the Age of Large-Scale Data-Driven Language Technology (2022) · cites this paper as [72]

  • Inspired by the GDPR, many U.S. state consumer privacy laws require risk assessments for automated decision-making or profiling.

    “Inspired by the GDPR, many U.S. state consumer privacy laws require risk assessments for automated decision-making or profiling. 108”

    From Artificial Intelligence and Privacy (2024) · cites this paper as [108]

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Authors
Huq, A. Z.
Affiliations
University of Chicago (US)
Corresponding author
Huq, Aziz Z.
ORCID
Huq, Aziz Z.
Venue
SSRN Electronic Journal
Publisher
Elsevier BV
Published
2020
Volume
105
Issue
7
Pages
1875
ISSN
1556-5068 (ISSN-L)
DOI
10.2139/ssrn.3613282
OpenAlex
W3168162279
Cited by
21
References
13
Citations by year
2026: 1 · 2025: 4 · 2024: 3 · 2023: 4 · 2022: 6 · 2021: 3
Type
posted content
Language
en
Open access
Locations
  • SSRN Electronic Journal (acceptedVersion)
  • bepress Legal Repository (submittedVersion)
  • eYLS (Yale Law School) (submittedVersion)
  • Cornell law review/The Cornell law quarterly
Topics
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Social Sciences › Law › Digitalization, Law, and Regulation
  • Social Sciences › Sociology and Political Science › Law, Rights, and Freedoms
Keywords
machine learning, constitutional rights, due process, privacy, equality, algorithmic decision-making, predictive policing, administrative enforcement, aggregate litigation, ex ante regulation, algorithmic accountability

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