Predicting Recidivism Risk: New Tool in Philadelphia Shows Great Promise

• NCJ Number: 240696
• Journal: NIJ Journal Issue: 271 Dated: February 2013 Pages: 4-13
• Author(s): Nancy Ritter
• Date Published: February 2013
• Length: 10 pages
• Grant Number(s): 2008-IJ-CX-0024
• Agencies: NIJ
• Publication Type: Best Practice/State-of-the-Art Review
• Award: This project will test, with a randomized controlled trial, an intensive community supervision strategy for probationers and parolees who are at extremely high risk of committing homicidal acts.

Annotation

This article discusses the Philadelphia Police Department's use of a computerized system to predict with greater accuracy which probationers would be more likely to reoffend.

Abstract

This article discusses the random forest model, a computerized system developed by criminologists from the University of Pennsylvania and officials with the Philadelphia Department of Adult Probation and Parole to more accurately predict which probationers would be more likely to reoffend within two years of returning to the community. The system, which has been used successfully for over 4 years in Philadelphia, works by assessing each new probation case at its outset and assigning the probationer to a high-, moderate-, or low-risk category. The success of the system is due to the use of a sophisticated statistical approach that considers the nonlinear effects of a greater array of variables with complex interactions. The article presents information on how a jurisdiction can use the random forest model for its own police department. The steps needed to assess whether the model is appropriate include determining what data already exists in electronic form, determining when the forecasting begins and when the forecasting ends, and determining an acceptable error rate. The author notes that the Philadelphia Police Department has had a 66 percent accuracy rate with the system that is has been using. The final sections of the article discuss the benefits of random forest modeling, how it promotes the efficient use of resources, and the fairness and equitability that is inherent in the model's design. Recommendations for future research are discussed.