The Accuracy, Fairness, and Limits of Predicting Recidivism - a podcast by Berkman Klein Center for Internet & Society at Harvard University

Algorithms for predicting recidivism are commonly used to assess a criminal defendant’s likelihood of committing a crime. Proponents of these systems argue that big data and advanced machine learning make these analyses more accurate and less biased than humans.

In this talk researcher Julia Dressel discusses a recent study demonstrating 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.Learn more about this event here:
http://cyber.harvard.edu/events/2018/luncheon/03/Dressel

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