Cited by 50Ethics and Social Impacts of AIArtificial Intelligence in LawCriminal Justice and Corrections Analysis

Accuracy and Fairness for Juvenile Justice Risk Assessments

Berk, Richard·2019·Journal of Empirical Legal Studies

Abstract

Risk assessment algorithms used in criminal justice settings are often said to introduce “bias.” But such charges can conflate an algorithm's performance with bias in the data used to train the algorithm with bias in the actions undertaken with an algorithm's output. In this article, algorithms themselves are the focus. Tradeoffs between different kinds of fairness and between fairness and accuracy are illustrated using an algorithmic application to juvenile justice data. Given potential bias in training data, can risk assessment algorithms improve fairness and, if so, with what consequences for accuracy? Although statisticians and computer scientists can document the tradeoffs, they cannot provide technical solutions that satisfy all fairness and accuracy objectives. In the end, it falls to stakeholders to do the required balancing using legal and legislative procedures, just as it always has.

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Crossref · 10.1111/jels.12206

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  • Many variables are explicitly defined as 'sensitive' by specific legal frameworks.

    “We note that many variables are explicitly deined as łsensitivež by speciic legal frameworks, see [25,129,130,191,192,209,268,280,306] and the references therein.”

    From Fairness in machine learning: A survey (2024) · cites this paper as [25]

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Authors
Berk, R.
Affiliations
University of Pennsylvania (US)
Corresponding author
Berk, Richard
Venue
Journal of Empirical Legal Studies
Publisher
Wiley
Published
2019-02-18
Volume
16
Issue
1
Pages
175-194
ISSN
  • 1740-1453 (ISSN-L)
  • 1740-1461
OpenAlex
W2915520221
Cited by
50
References
53
Citations by year
2026: 4 · 2025: 4 · 2024: 6 · 2023: 5 · 2022: 10 · 2021: 14 · 2020: 3 · 2019: 3 · 2018: 1
FWCI
13.11
Type
journal article
Language
en
Open access
closed
Locations
Journal of Empirical Legal Studies (publishedVersion)
Topics
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Social Sciences › Political Science and International Relations › Artificial Intelligence in Law
  • Social Sciences › Sociology and Political Science › Criminal Justice and Corrections Analysis
Keywords
risk assessment algorithm, juvenile justice, algorithmic fairness, algorithmic accuracy, fairness-accuracy trade-off, algorithmic bias, training data bias, algorithmic decision-making

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