What is it about?

This study aims to improve the predictive performance of existing risk assessment tools and the predictive validity of the original Ministry of Justice Case Assessment Tool (MJCA) concerning recidivism rates using machine learning (ML) and examine whether the tool’s predictive performance can be improved. With follow-up data on 5,942 individuals in Japanese Juvenile Assessment Centers, the study uses ML methods, such as the K-nearest neighbor algorithm, support vector machine, random forest, gradient boosting tree, and multilayer perceptron, to improve the MJCA’s prediction power. The results show that the predictive validity of the original MJCA significantly improves for three of the six ML methods; gradient boosting tree, random forest, and multilayer perceptron have the highest predictive validity.

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Why is it important?

The importance of this study lies in the fact that it indicates the benefits and limitations of machine learning through comparing and testing the performance of several types of machine learning on predicting recidivism in a large sample of cases. We concluded that ML could improve the predictive validity of recidivism rates. The improvement is less pronounced than the enormous impact that recent artificial intelligence methods have had on information processing. However, it is significant because recidivism risk assessment is important in determining the treatment for individuals who offend. ML is beneficial for risk assessment and must be used with a focus on these issues.


In recent years, advances in AI have been widely publicized, and I hope that we can make more and more use of this machine learning technique in the field of criminal psychology. I hope this article will help to accelerate this trend and help in preventing crime. Above all, I hope this article is thought-provoking.

Takemi Mori
Konan Women's University

Read the Original

This page is a summary of: The limited value of machine learning approach to improving predictive performance: The Ministry of Justice Case Assessment Tool., Psychology Public Policy and Law, May 2024, American Psychological Association (APA),
DOI: 10.1037/law0000421.
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