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Published Online: 1 June 2017

Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments

Publication: Big Data
Volume 5, Issue Number 2


Recidivism prediction instruments (RPIs) provide decision-makers with an assessment of the likelihood that a criminal defendant will reoffend at a future point in time. Although 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 article discusses several fairness criteria that have recently been applied to assess the fairness of RPIs. We demonstrate that the criteria cannot all be simultaneously satisfied when recidivism prevalence differs across groups. We then show how disparate impact can arise when an RPI fails to satisfy the criterion of error rate balance.

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Cite this article as: Chouldechova A (2017) Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big Data 5:2, 153–163, DOI: 10.1089/big.2016.0047.

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Published In

cover image Big Data
Big Data
Volume 5Issue Number 2June 2017
Pages: 153 - 163
PubMed: 28632438


Published in print: June 2017
Published online: 1 June 2017


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Alexandra Chouldechova* [email protected]
H. John Heinz III College, Carnegie Mellon University, Pittsburgh, Pennsylvania.


Address correspondence to: Alexandra Chouldechova, Heinz College, Carnegie Mellon University, 4800 Forbes Avenue, Pittsburgh, PA 15213, E-mail: [email protected]

Author Disclosure Statement

No competing financial interests exist.

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