Using machine learning to reduce domestic violence -- GCN

Using machine-learning to forecast which accused perpetrators of domestic violence -- particularly those whose crimes result in injuries -- will be re-arrested on similar charges can cut such recidivism in half, according to a recent report. Machine learning used during the arraignment process prevented "well over" 1,000 domestic violence incidents annually in at least one large metropolitan area, according to authors Richard Berk, a professor of criminology and statistics in the School of Arts & Sciences and the Wharton School, and Susan B. Sorenson, director of the Evelyn Jacobs Ortner Center on Family Violence. For their study, "Forecasting Domestic Violence: A Machine Learning Approach to Help Inform Arraignment Decisions," Berk and Sorenson analyzed 28,646 domestic violence arraignments that led to official charges and the corresponding releases. "Under current practice, about 20 percent of the individuals released after arraignment are arrested for domestic violence within two years. If magistrates only released offenders our forecasts identified as good bets… [f]ailures could be cut in half."