Podcasts Culture Berkman Klein Center for Internet and Society: Audio Fishbowl The Accuracy, Fairness, and Limits of Predicting Recidivism

The Accuracy, Fairness, and Limits of Predicting Recidivism

Published: March 15, 2018, 5:03 p.m.

b'Algorithms for predicting recidivism are commonly used to assess a criminal defendant\\u2019s 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. \\n\\nIn 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.\\n\\nLearn more about this event here:\\nhttp://cyber.harvard.edu/events/2018/luncheon/03/Dressel'