This article originally appeared in the March/April 2022 issue of Corrections Today as submitted by the National Institute of Justice. It is reprinted with permission of the American Correctional Association, Alexandria, VA. All rights reserved.
Recidivism is a major concern for our criminal justice system. Although our ability to predict recidivism through risk and needs assessments has improved, many tools used for prediction and forecasting are insensitive to gender-specific needs and suffer from racial bias. In addressing these issues, the National Institute of Justice (NIJ) recently hosted the Recidivism Forecasting Challenge. The primary aim of this research competition was to understand the factors that drive recidivism, which was measured by an arrest for a new offense. Challenge entrants were asked to develop and train software models to forecast recidivism for individuals released on parole from the state of Georgia. Entrants were given a dataset that allowed them to train their forecasting models by exploring gender, racial and age differences for individuals on parole, in addition to a host of other information. Submissions showed how data sharing and open competition can improve recidivism forecasting accuracy compared to simple forecasting models.