The Application of Machine Learning to a General Risk–Need Assessment Instrument in the Prediction of Criminal Recidivism

Autor:	J. Stephen Wormith, Mehdi Ghasemi, Keira C. Stockdale, Raymond J. Spiteri, Mahshid Atapour, Daniel Anvari
Rok vydání:	2020
Předmět:	Recidivism Computer science Level of service 050901 criminology 05 social sciences Applied psychology Case management Pathology and Forensic Medicine Needs assessment 0501 psychology and cognitive sciences 0509 other social sciences Law General Psychology 050104 developmental & child psychology
Zdroj:	Criminal Justice and Behavior. 48:518-538
ISSN:	1552-3594 0093-8548
Popis:	The Level of Service/Case Management Inventory (LS/CMI) is one of the most frequently used tools to assess criminogenic risk–need in justice-involved individuals. Meta-analytic research demonstrates strong predictive accuracy for various recidivism outcomes. In this exploratory study, we applied machine learning (ML) algorithms (decision trees, random forests, and support vector machines) to a data set with nearly 100,000 LS/CMI administrations to provincial corrections clientele in Ontario, Canada, and approximately 3 years follow-up. The overall accuracies and areas under the receiver operating characteristic curve (AUCs) were comparable, although ML outperformed LS/CMI in terms of predictive accuracy for the middle scores where it is hardest to predict the recidivism outcome. Moreover, ML improved the AUCs for individual scores to near 0.60, from 0.50 for the LS/CMI, indicating that ML also improves the ability to rank individuals according to their probability of recidivating. Potential considerations, applications, and future directions are discussed.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::a031b5ca40949fc36d0732081772baf3 https://doi.org/10.1177/0093854820969753 Zobrazit plný text záznamu