RT Journal Article SR Electronic T1 The accuracy, fairness, and limits of predicting recidivism JF Science Advances JO Sci Adv FD American Association for the Advancement of Science SP eaao5580 DO 10.1126/sciadv.aao5580 VO 4 IS 1 A1 Dressel, Julia A1 Farid, Hany YR 2018 UL http://advances.sciencemag.org/content/4/1/eaao5580.abstract 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.