Prediction Machine Learning Models on Propensity Convicts to Criminal Recidivism

Abstract

Increasing internal state security requires an understanding of the factors that influence the commission of repetitive crimes (recidivism) since the crime is not caused by public danger but by the criminal person. Against the background of informatization of the information activities of law enforcement agencies, there is no doubt about the expediency of using artificial intelligence algorithms and blockchain technology to predict and prevent crimes. The prediction machine-learning models for identifying significant factors (individual characteristics of convicts), which affect the propensity to commit criminal recidivism, were applied in this article. For predicting the probability of propensity for criminal recidivism of customers of Ukrainian penitentiary institutions, a Decision Tree model was built to suggest the probability of repeated criminal offenses by convicts. It was established that the number of convictions to the actual punishment and suspended convictions is the main factors that determine the propensity of customers of penitentiary institutions to commit criminal recidivism in the future. Decision Tree models for the classification of convicts prone or not prone to recidivism were built. They can be used to predict new cases for decision-making support in criminal justice. In our further research, the possibility of using the technology of distributed registers/blockchain in predictive criminology will be analyzed.

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