Penn Study: Machine Learning at Arraignments Can Cut Repeat Domestic Violence

In one large metropolitan area, arraignment decisions made with the assistance of machine learning cut new domestic violence incidents by half, leading to more than 1,000 fewer such post-arraignment arrests annually, according to new findings from the University of Pennsylvania. In the United States, the typical pre-trial process proceeds from arrest to preliminary arraignment to a mandatory court appearance, when appropriate. During the preliminary arraignment, a judge or magistrate chooses whether to release or detain the suspect, a decision intended to account for the likelihood that the person will return to court or commit new crimes. This is especially important in domestic violence, which is often a serial offense and directed at a particular individual. Arraignments are usually brief, with outcome projections made based on limited data. However, Richard Berk, a criminology…


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