PT - JOURNAL ARTICLE AU - Dressel, Julia AU - Farid, Hany TI - The accuracy, fairness, and limits of predicting recidivism AID - 10.1126/sciadv.aao5580 DP - 2018 Jan 01 TA - Science Advances PG - eaao5580 VI - 4 IP - 1 4099 - http://advances.sciencemag.org/content/4/1/eaao5580.short 4100 - http://advances.sciencemag.org/content/4/1/eaao5580.full SO - Sci Adv2018 Jan 01; 4 AB - 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. In addition, despite COMPAS’s collection of 137 features, the same accuracy can be achieved with a simple linear classifier with only two features.