The accuracy, fairness, and limits of predicting recidivism (2018)

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 learningmake these analysesmore accurate and less biased than humans. We show,
however, that the widely used commercial risk assessment software COMPAS is nomore 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 predictor with only two features.