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

Abstract

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.

Machine Learning and Criminal Justice: A Systematic Review of Advanced Methodology for Recidivism Risk Prediction

“…For a better comparison of the studies, it is possible to perform a sorting by the type of recurrence they aim to predict. The sorting leads to four categories: general [ 22 , 23 , 24 , 28 , 31 , 32 ], sexual [ 31 , 32 ], violent [ 22 , 25 , 31 ] and all other recidivism. The last category includes studies that considered a specific type of crime [ 21 ] or referred only to males [ 27 ] or youth [ 26 , 29 , 30 ].…”

“…The most used ML model is the logistic regression [ 21 , 30 , 31 , 32 ] and two of its variants, the LogitBoost [ 22 ] and the generalized linear models with ridge and lasso regularization (Glmnet) [ 24 ]. The second most popular model is the random forest [ 23 , 26 , 27 , 33 ]. The other ML models to mention are multi-layer perceptron (MLP) [ 25 ], linear discriminant analysis (LDA) [ 31 ] and penalized LDA [ 32 ].…”

Artificial Intelligence Solutions to Detect Fraud in Healthcare Settings: A Scoping Review

“…More details about the characteristics of the included studies are shown in Appendix 2. The included studies utilized AI for fraud detection (n=17) [1,2,6,8,14,17,18,19,21,22,23,24,26,27,28,30,31], identifying and classifying detected fraud (n=8) [3,4,11,12,13,15,20,25], and investigating and analyzing fraudulent data (n=6) [5,7,9,10,16,29]. The most common algorithm used in the included studies was Convolutional Neural Network (CNN) (n=13), followed by Artificial Neural Network (ANN) (n=10).…”

“…Recently, deep learning approaches have made significant contributions to detecting fraudulent activities within healthcare networks. As such, fraud detection experts have recognized them as a solid, reliable, and promising anomaly detection technique [4]. While several studies on AI and fraud detection have been conducted, little research has summarized how novel AI approaches are utilized to mitigate fraud.…”

An Overview of Fairness in Clustering

“…M ACHINE Learning (ML) has been used to tackle many important problems, many of which can have significant societal implications. Some of these problems include predicting the likelihood of prisoner recidivism [1]- [5], disbursement of bank loans [6]- [8], shortlisting candidates for job applications [9]- [13], and college admissions [14]- [16]. Since ML models train on large datasets that have been found to contain biases against both individuals and minority groups, they can further amplify biases when used in highimpact applications.…”