A multiple models approach to assessing recidivism risk: Implications for judicial decision making

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.