Simulation of a Judicial Process using Machine Learning to Analyze Administrative Prejudice and Indicate the Quality of Justice

In many countries, it is customary to divide public offenses into different types. For example, in common law countries the doctrine of dividing such offenses into and ""malum prohibitum"" is common. In this article, based on a computer analysis of a large volume of empirical dat...

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Autores principales:	Oleg Metsker, David Paskoshev, Egor Trofimov, Georgy Kopanitsa
Formato:	article
Lenguaje:	EN
Publicado:	FRUCT 2021
Materias:	text mining machine learning law simulation Telecommunication TK5101-6720
Acceso en línea:	https://doaj.org/article/38c70329fb1e4cb0ba6d212c16b520d0
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id	oai:doaj.org-article:38c70329fb1e4cb0ba6d212c16b520d0
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spelling	oai:doaj.org-article:38c70329fb1e4cb0ba6d212c16b520d02021-11-20T15:59:33ZSimulation of a Judicial Process using Machine Learning to Analyze Administrative Prejudice and Indicate the Quality of Justice2305-72542343-073710.23919/FRUCT53335.2021.9599971https://doaj.org/article/38c70329fb1e4cb0ba6d212c16b520d02021-10-01T00:00:00Zhttps://www.fruct.org/publications/fruct30/files/Pas.pdfhttps://doaj.org/toc/2305-7254https://doaj.org/toc/2343-0737In many countries, it is customary to divide public offenses into different types. For example, in common law countries the doctrine of dividing such offenses into and ""malum prohibitum"" is common. In this article, based on a computer analysis of a large volume of empirical date, we test the following hypotheses, which have essential legal significance. The first hypothesis: judicial decisions rendered under the criminal procedure should have a more developed semantics than judicial decisions rendered under the administrative offence procedure. The second hypothesis: in the case of administrative prejudice, the decision trees built on the materials of criminal proceedings should have a fundamental similarity with the decision trees built on the materials of proceedings on cases of administrative offenses. The study was carried out based on non-political administrative prejudice, which has an important social significance, namely on the example of the failure to pay for the support of children or disabled parents (Article 5.35 of the Administrative Offenses Code of the Russian Federation, Article 157 of the Criminal Code). The mentioned articles of the law are in force in the unchanged version since 15.07.2016, but even before that there was a stable judicial practice on similar cases. The study proved that both hypotheses: judicial decisions rendered under the criminal procedure have a more developed semantics than judicial decisions rendered under the administrative offence procedure. The second hypothesis: in the case of administrative prejudice, the decision trees built on the materials of criminal proceedings demonstrated a fundamental similarity with the decision trees built on the materials of proceedings on cases of administrative offenses.Oleg MetskerDavid PaskoshevEgor TrofimovGeorgy KopanitsaFRUCTarticletext miningmachine learninglawsimulationTelecommunicationTK5101-6720ENProceedings of the XXth Conference of Open Innovations Association FRUCT, Vol 30, Iss 1, Pp 165-170 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	text mining machine learning law simulation Telecommunication TK5101-6720
spellingShingle	text mining machine learning law simulation Telecommunication TK5101-6720 Oleg Metsker David Paskoshev Egor Trofimov Georgy Kopanitsa Simulation of a Judicial Process using Machine Learning to Analyze Administrative Prejudice and Indicate the Quality of Justice
description	In many countries, it is customary to divide public offenses into different types. For example, in common law countries the doctrine of dividing such offenses into and ""malum prohibitum"" is common. In this article, based on a computer analysis of a large volume of empirical date, we test the following hypotheses, which have essential legal significance. The first hypothesis: judicial decisions rendered under the criminal procedure should have a more developed semantics than judicial decisions rendered under the administrative offence procedure. The second hypothesis: in the case of administrative prejudice, the decision trees built on the materials of criminal proceedings should have a fundamental similarity with the decision trees built on the materials of proceedings on cases of administrative offenses. The study was carried out based on non-political administrative prejudice, which has an important social significance, namely on the example of the failure to pay for the support of children or disabled parents (Article 5.35 of the Administrative Offenses Code of the Russian Federation, Article 157 of the Criminal Code). The mentioned articles of the law are in force in the unchanged version since 15.07.2016, but even before that there was a stable judicial practice on similar cases. The study proved that both hypotheses: judicial decisions rendered under the criminal procedure have a more developed semantics than judicial decisions rendered under the administrative offence procedure. The second hypothesis: in the case of administrative prejudice, the decision trees built on the materials of criminal proceedings demonstrated a fundamental similarity with the decision trees built on the materials of proceedings on cases of administrative offenses.
format	article
author	Oleg Metsker David Paskoshev Egor Trofimov Georgy Kopanitsa
author_facet	Oleg Metsker David Paskoshev Egor Trofimov Georgy Kopanitsa
author_sort	Oleg Metsker
title	Simulation of a Judicial Process using Machine Learning to Analyze Administrative Prejudice and Indicate the Quality of Justice
title_short	Simulation of a Judicial Process using Machine Learning to Analyze Administrative Prejudice and Indicate the Quality of Justice
title_full	Simulation of a Judicial Process using Machine Learning to Analyze Administrative Prejudice and Indicate the Quality of Justice
title_fullStr	Simulation of a Judicial Process using Machine Learning to Analyze Administrative Prejudice and Indicate the Quality of Justice
title_full_unstemmed	Simulation of a Judicial Process using Machine Learning to Analyze Administrative Prejudice and Indicate the Quality of Justice
title_sort	simulation of a judicial process using machine learning to analyze administrative prejudice and indicate the quality of justice
publisher	FRUCT
publishDate	2021
url	https://doaj.org/article/38c70329fb1e4cb0ba6d212c16b520d0
work_keys_str_mv	AT olegmetsker simulationofajudicialprocessusingmachinelearningtoanalyzeadministrativeprejudiceandindicatethequalityofjustice AT davidpaskoshev simulationofajudicialprocessusingmachinelearningtoanalyzeadministrativeprejudiceandindicatethequalityofjustice AT egortrofimov simulationofajudicialprocessusingmachinelearningtoanalyzeadministrativeprejudiceandindicatethequalityofjustice AT georgykopanitsa simulationofajudicialprocessusingmachinelearningtoanalyzeadministrativeprejudiceandindicatethequalityofjustice
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Simulation of a Judicial Process using Machine Learning to Analyze Administrative Prejudice and Indicate the Quality of Justice

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