The Accuracy, Fairness, and Limits of Predicting Recidivism

Seminar | March 8 | 3:10-5 p.m. | 107 South Hall

 Hany Farid

 Information, School of

Algorithms for predicting recidivism are commonly used to assess a criminal defendantâs 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 predictions more accurate and less biased than humans. Opponents, however, argue that predictive algorithms may lead to further racial bias in the criminal justice system. I will discuss an in-depth analysis of one widely used commercial predictive algorithm to determine its appropriateness for use in our courts. (This presentation is based on joint work with Julia Dressel.)