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. 2018 Jan 17;4(1):eaao5580.
doi: 10.1126/sciadv.aao5580. eCollection 2018 Jan.

The accuracy, fairness, and limits of predicting recidivism

Julia Dressel  1 , Hany Farid  1
Affiliations

The accuracy, fairness, and limits of predicting recidivism

Julia Dressel et al. Sci Adv. .

Abstract

Algorithms for predicting recidivism are commonly used to assess a criminal defendant's likelihood of committing a crime. These predictions are used in pretrial, parole, and sentencing decisions. Proponents of these systems argue that big data and advanced machine learning make these analyses more accurate and less biased than humans. We show, however, that the widely used commercial risk assessment software COMPAS is no more accurate or fair than predictions made by people with little or no criminal justice expertise. We further show that a simple linear predictor provided with only two features is nearly equivalent to COMPAS with its 137 features.

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Figures

Fig. 1
Fig. 1. Human (no-race condition) versus COMPAS algorithmic predictions (see also Table 1).
See this image and copyright information in PMC

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