Recidivism is the likelihood of a person convicted of a crime to offend again. Currently, this rate is determined by predictive algorithms. The outcome can affect everything from sentencing decisions to whether or not a person receives parole.

To determine how accurate these algorithms actually are in practice, a team led by Dartmouth College researchers Julia Dressel and Hany Farid conducted a study of a widely-used commercial risk assessment software known as Correctional Offender Management Profiling for Alternative Sanctions (COMPAS). The software determines whether or not a person will re-offend within two years following their conviction.

The study revealed that COMPAS is no more accurate than a group of volunteers with no criminal justice experience at predicting recidivism rates. Dressel and Farid crowdsourced a list of volunteers from a website, then randomly assigned them small lists of defendants.

The volunteers were told each defendant’s sex, age, and previous criminal history then asked to predict whether they would re-offend within the next two years. The accuracy of the human volunteer’s predictions included a mean of 62.1 percent and a median of 64.0 percent – very close to COMPAS’ accuracy, which is 65.2 percent.

Additionally, researchers found that even though COMPAS has 137 features, linear predictors with just two features (the defendant’s age and their number of previous convictions) worked just as well for predicting recidivism rates. One area of concern for the team was the potential for algorithmic bias. Read more from…

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