This report was submitted by Team Aurors as a participant in the U.S. Justice Department’s National Institute of Justice’s (NIJ’s) Recidivism Forecasting Challenge, which aimed to improve the ability to forecast recidivism.
The work reported by the Aurors Team addresses forecasting models that include regression analysis methods (binary logit and LASSO regressions) and machine learning techniques (random forest), combined through a model averaging procedure. The output consists of the percent likelihood of individuals recidivating within one, two, or three years from release. Although this report explains the database construction process and the modeling approach, the results focus on the section of female parolees recidivating within 3 years, since that is the category for which the Aurors team came in second place.
Downloads
Similar Publications
- Criminal Justice Interventions for Offenders With Mental Illness: Evaluation of Mental Health Courts in Bronx and Brooklyn, New York, Executive Summary
- Is the Gender Gap in Overdose Deaths (Still) Decreasing? An Examination of Opioid Deaths in Delaware, 2013–2017
- Catching Career Criminals - A Study of the Repeat Offender Project - Technical Report