The application of Machine Learning to risk/need assessment instrument LS/CMI in the prediction of offender recidivism

Research Summary: The Level of Service/Case Management Inventory (LS/CMI) is an offender risk/need assessment inventory that is designed for use with adult offenders who are either in custody or are serving their sentence in the community. Its items are scored in a dichotomous manner and summed to generate a score that correlates moderately with offender recidivism. In this study, we apply machine learning algorithms to gain more predictive accuracy for recidivism. The optimized methods for training the machine learning algorithms in this article are drawing uniform samples. Among all algorithms studied, decision trees perform significantly better than others. Policy Implications: The combination of a strong criminological theory-driven instrument, like the LS/CMI, with machine learning algorithms and clustering offenders according to their LS/CMI-score result in a significant improvement in the prediction of recidivism. Correctional agencies should explore the use of these techniques to improve the predictive validity of the LSI/CMI or similar instruments with their own offender population.