Machine Learning Can Help Prevent Repeat Domestic Violence Offenses

Richard Berk, a professor of criminology and statistics in Penn Arts and Sciences and the Wharton School, and Susan B. Sorenson, director of the Evelyn Jacobs Ortner Center on Family Violence at Penn and professor in the School of Social Policy and Practice, have made an important discovery about domestic violence: Using machine learning risk assessment during the arraignment process, when a judge or magistrate decides whether to detain or release an accused offender, prevented more than 1,000 domestic violence arrests in one year in at least one large metropolitan area.

They published their work in the March issue of The Journal of Empirical Legal Studies.

“Here, machine learning is basically an algorithm that says, ‘Determine which descriptions of individuals are strongly associated with the outcome,’” says Berk. “Then you give the computer certain instructions on how to look.”

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