This study used a large recidivism data set to develop and validate a multiple models tool for predicting recidivism risk. Consistent with prior research, the authors found that the multiple models tool was more accurate than tools built using the traditional single-model approach. In addition, they demonstrated that the predicted recidivism rates produced by the multiple models tool could be summarized in a usable format consisting of four to five statistically distinct risk classes offering an impressive degree of base-rate dispersion. Given that public protection ranks as a primary focal concern of judges, the authors believe that their results justify renewed attention to the potential uses of actuarial tools within the context of judicial decision making.
All Science Journal Classification (ASJC) codes
- Pathology and Forensic Medicine