Using Algorithms to Address Trade-Offs Inherent in Predicting Recidivism

Skeem, J. & Lowenkamp, C., Using algorithms to address trade-offs inherent in predicting recidivism. Behavioral Sciences & the Law, Forthcoming

40 Pages Posted: 12 May 2020

See all articles by Jennifer L. Skeem

Jennifer L. Skeem

University of California, Berkeley

Christopher Lowenkamp

AO US Courts Probation and Pretrial Services Office

Date Written: April 17, 2020

Abstract

Although risk assessment has increasingly been used as a tool to help reform the criminal justice system, some stakeholders are adamantly opposed to using algorithms. The principal concern is that any benefits achieved by safely reducing rates of incarceration will be offset by costs to racial justice claimed to be inherent in the algorithms themselves. But fairness tradeoffs are inherent to the task of predicting recidivism, whether the prediction is made by an algorithm or human. Based on a matched sample of 67,784 Black and White federal supervisees assessed with the Post Conviction Risk Assessment (PCRA), we compare how three alternative strategies for “debiasing” algorithms affect these tradeoffs, using arrest for a violent crime as the criterion. These candidate algorithms all strongly predict violent re-offending (AUCs=.71-72), but vary in their association with race (r= .00-.21) and shift tradeoffs between balance in positive predictive value and false positive rates. Providing algorithms with access to race (rather than omitting race or ‘blinding’ its effects) can maximize calibration and minimize imbalanced error rates. Implications for policymakers with value preferences for efficiency vs. equity are discussed.

Keywords: risk assessment, algorithm, race, fairness, bias

Suggested Citation

Skeem, Jennifer L. and Lowenkamp, Christopher, Using Algorithms to Address Trade-Offs Inherent in Predicting Recidivism (April 17, 2020). Skeem, J. & Lowenkamp, C., Using algorithms to address trade-offs inherent in predicting recidivism. Behavioral Sciences & the Law, Forthcoming, Available at SSRN: https://ssrn.com/abstract=3578591 or http://dx.doi.org/10.2139/ssrn.3578591

Jennifer L. Skeem (Contact Author)

University of California, Berkeley ( email )

120 Haviland Hall
Berkeley, CA 94720-7400
United States

Christopher Lowenkamp

AO US Courts Probation and Pretrial Services Office ( email )

689 Union Drive
Hudson, OH 44236
United States
2025270313 (Phone)

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