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Title |
Differentiation of Legal Rules and Individualization of Court Decisions in Criminal, Administrative and Civil Cases: Identification and Assessment Methods |
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Author |
Egor Trofimov, Oleg Metsker, and Georgy Kopanitsa |
| Citation |
Vol. 22 No. 12 pp. 125-131 |
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Abstract |
The diversity and complexity of criminal, administrative and civil cases resolved by the courts makes it difficult to develop universal automated tools for the analysis and evaluation of justice. However, big data generated in the scope of justice gives hope that this problem will be resolved as soon as possible. The big data applying makes it possible to identify typical options for resolving cases, form detailed rules for the individualization of a court decision, and correlate these rules with an abstract provisions of law. This approach allows us to somewhat overcome the contradiction between the abstract and the concrete in law, to automate the analysis of justice and to model e-justice for scientific and practical purposes. The article presents the results of using dimension reduction, SHAP value, and p-value to identify, analyze and evaluate the individualization of justice and the differentiation of legal regulation. Processing and analysis of arrays of court decisions by computational methods make it possible to identify the typical views of courts on questions of fact and questions of law. This knowledge, obtained automatically, is promising for the scientific study of justice issues, the improvement of the prescriptions of the law and the probabilistic prediction of a court decision with a known set of facts. |
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Keywords |
artificial intelligence, digital state, indicator, law, machine learning, data mining, lawmaking, e-justice, big data. |
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