APCYMLOct 24, 2016

Fair prediction with disparate impact: A study of bias in recidivism prediction instruments

arXiv:1610.07524v12418 citations
Originality Synthesis-oriented
AI Analysis

This addresses fairness concerns in criminal justice risk assessments, but it is incremental as it critiques an existing criterion rather than proposing a new solution.

The paper examines how applying a fairness criterion from educational testing to recidivism prediction instruments can lead to significant disparate impact when recidivism rates vary across demographic groups, highlighting a bias issue in these tools.

Recidivism prediction instruments provide decision makers with an assessment of the likelihood that a criminal defendant will reoffend at a future point in time. While such instruments are gaining increasing popularity across the country, their use is attracting tremendous controversy. Much of the controversy concerns potential discriminatory bias in the risk assessments that are produced. This paper discusses a fairness criterion originating in the field of educational and psychological testing that has recently been applied to assess the fairness of recidivism prediction instruments. We demonstrate how adherence to the criterion may lead to considerable disparate impact when recidivism prevalence differs across groups.

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