We appreciate the help of Michael Gallagher, a retired police officer, who currently works with a domestic violence agency in the jurisdiction studied. He provided important information on the meaning of some of our variables and on related law enforcement procedures. We also received important assistance on the prosecutorial context of domestic violence from Marian G. Braccia, Deputy District Attorney in the District Attorney's Office of the jurisdiction. Thanks also go to the anonymous reviewers of this article.
Forecasting Domestic Violence: A Machine Learning Approach to Help Inform Arraignment Decisions
Version of Record online: 8 FEB 2016
© 2016, Copyright the Authors. Journal compilation © 2016, Cornell Law School and Wiley Periodicals, Inc.
Journal of Empirical Legal Studies
Volume 13, Issue 1, pages 94–115, March 2016
How to Cite
Berk, R. A., Sorenson, S. B. and Barnes, G. (2016), Forecasting Domestic Violence: A Machine Learning Approach to Help Inform Arraignment Decisions. Journal of Empirical Legal Studies, 13: 94–115. doi: 10.1111/jels.12098
- Issue online: 8 FEB 2016
- Version of Record online: 8 FEB 2016
Arguably the most important decision at an arraignment is whether to release an offender until the date of his or her next scheduled court appearance. Under the Bail Reform Act of 1984, threats to public safety can be a key factor in that decision. Implicitly, a forecast of “future dangerousness” is required. In this article, we consider in particular whether usefully accurate forecasts of domestic violence can be obtained. We apply machine learning to data on over 28,000 arraignment cases from a major metropolitan area in which an offender faces domestic violence charges. One of three possible post-arraignment outcomes is forecasted within two years: (1) a domestic violence arrest associated with a physical injury, (2) a domestic violence arrest not associated with a physical injury, and (3) no arrests for domestic violence. We incorporate asymmetric costs for different kinds of forecasting errors so that very strong statistical evidence is required before an offender is forecasted to be a good risk. When an out-of-sample forecast of no post-arraignment domestic violence arrests within two years is made, it is correct about 90 percent of the time. Under current practice within the jurisdiction studied, approximately 20 percent of those released after an arraignment for domestic violence are arrested within two years for a new domestic violence offense. If magistrates used the methods we have developed and released only offenders forecasted not to be arrested for domestic violence within two years after an arraignment, as few as 10 percent might be arrested. The failure rate could be cut nearly in half. Over a typical 24-month period in the jurisdiction studied, well over 2,000 post-arraignment arrests for domestic violence perhaps could be averted.