Associative Model of Support for Judicial Decision-Making
DOI:
https://doi.org/10.35774/10.35774/app2023.03.056Keywords:
decision-making, criminal justice, computational criminology, criminal offenses, security, criminal recidivism, suspended convictions, early dismissals, predictive justice, data science, associative modelAbstract
Despite numerous efforts aimed at ensuring the protection of the accused, and despite the understanding of the effectiveness of preventive measures in contrast to punitive sanctions, modern industrialized society in most cases decides to apply imprisonment to persons convicted of crimes. Convicted persons face increasing legal and social prejudice, alienation, and marginalization, including the loss of voting privileges. In addition, keeping a significant number of prisoners in penitentiary institutions causes an additional significant burden on the economies of the countries of the world. In addition, keeping a significant number of prisoners in penitentiary institutions causes an additional significant burden on the economies of the countries of the world. Today, effective solutions are needed in the criminal justice system, which will not be excessive in relation to the convicts and will be sufficient to ensure social order. Scientific methods and modern information technologies are increasingly used in predictive justice to support judicial decision-making.
The search for effective decision-making strategies that can help reduce the number of prisoners and state costs for their maintenance in penitentiary institutions and at the same time ensure the personal safety of citizens and the safety of society in general are becoming more and more relevant. The application of data science to support effective judicial decision-making is a prerequisite.
The work uses the analysis of associative rules to identify non-obvious relationships between data on previous criminal offenses of convicts. A model of associative rules was built to identify non-obvious interesting connections between individual statistical and dynamic characteristics of convicts and the fact of their criminal recidivism. Associative rules (regularities) were revealed, which are a combination of individual characteristics of convicts who commit repeated criminal offenses. The obtained results give grounds for asserting that convictions are the main conditions (antecedents), that cause the risk of recidivism (consequent).
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