Document Type
Article
Publication Date
1-17-2018
Publication Title
Science Advances
Department
Department of Computer Science
Abstract
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. We further show that a simple linear predictor provided with only two features is nearly equivalent to COMPAS with its 137 features.
DOI
10.1126/sciadv.aao5580
Dartmouth Digital Commons Citation
Dressel, Julie and Farid, Hany, "The Accuracy, Fairness, and Limits of Predicting Recidivism" (2018). Dartmouth Scholarship. 1342.
https://digitalcommons.dartmouth.edu/facoa/1342
Included in
Numerical Analysis and Scientific Computing Commons, Software Engineering Commons, Statistics and Probability Commons
