Using Machine Learning to Identify High Risk Domestic Violence Offenders in NYC, Final Summary Overview

• NCJ Number: 305443
• Author(s): Jens Ludwig
• Date Published: 2022
• Length: 11 pages
• Grant Number(s): 2017-VA-CX-0033
• Agencies: NIJ-Sponsored

Annotation

This report details a project designed to develop and test a novel machine-learning based statistical model to predict the risk of domestic-violence victimization to improve intervention in cases at high risk for violence.

Abstract

The purpose of this project, a collaboration between the University of Chicago Crime Lab and the New York City Police Department, was to develop and test a novel machine-learning based statistical model to predict the risk of domestic-violence victimization to improve intervention in cases at high risk for violence. The field intervention with the NYPD was launched in July 2017. NYPD command maintains a list of high-priority individuals who are thought to be at risk for serious domestic assault. Individuals on this list receive regular home visits from one of the local NYPD’s Domestic Violence Officers (DVOs) to reduce the risk of future victimization. The researchers believe that upon the completion and dissemination of this work, the results will be relevant to researchers and policymakers who are assessing ways to reduce domestic violence.