Penn professors use machine-based process to predict domestic violence reoffenses

Two Penn professors found that the use of machine-learning forecasts at domestic violence arraignments could substantially reduce repeat domestic violence arrests. Social policy professor Susan B. Sorenson of social policy and criminology and statistics professor Richard Berk looked at 28,646 domestic violence arraignments that led to charges and releases between January 2007 and October 2011. The researchers conducted a two-year follow-up for each case. Normally, in the United States, after being arrested, suspects are brought before a judge or magistrate in an arraignment, during which the suspect receives a written document with the list of charges. At this arraignment, the judge or magistrate decides whether the suspect can be released to society. This decision, pursuant to the Bail Reform Act of 1984, is based on the suspect’s risk of flight…


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