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Recidivism Algorithm

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Julia Dressel and Hany Farid have this research article in Science Advances.  Here is the 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.
Along with questions of whether they are really any better, I believe that government should not be making decisions about people's lives using proprietary algorithms whose makers refuse to disclose the inner workings.

The article is Julia Dressel and Hany Farid, The accuracy, fairness, and limits of predicting recidivism, Science Advances  17 Jan 2018: Vol. 4, no. 1, eaao5580; DOI: 10.1126/sciadv.aao5580

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