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.