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Law and artificial intelligence: the experience of computational methodology for analyzing and assessing quantitative changes in legislation and law enforcement practice (on the example of the Article 20.4 of the Code of the Russian Federation on Administrative Offenses)

Trofimov Egor Viktorovich

ORCID: 0000-0003-4585-8820

Doctor of Law

Deputy Director for Science, St. Petersburg Institute (Branch) of the All-Russian State University of Justice

199178, Russia, g. Saint Petersburg, 10-ya liniya V.O., 19, lit. A, kab. 36

diterihs@mail.ru
Другие публикации этого автора
 

 
Metsker Oleg Gennad'evich

ORCID: 0000-0003-3427-7932

PhD in Technical Science

Researcher

199178, Russia, g. Saint Petersburg, 10-liniya V.O., 19 lit. A

olegmetsker@gmail.com
Другие публикации этого автора
 

 

DOI:

10.7256/2454-0706.2019.8.30306

Review date:

15-07-2019


Publish date:

22-07-2019


Abstract: The subject of this research is the changes that took place in law enforcement practice due to introduction in 2011 of the new revision of the Article 20.4 “On Violation of Fire Prevention Rules” of the Code of the Russian Federation on Administrative Offenses. The article presents the results of computational experiment conducted for the purpose of development and testing of high-performance software based on the intellectual analysis and computer-assisted learning that improves understanding of the new legal phenomena and processes associated with the impact of legislation upon law enforcement practice. For solving the research objective. For solving the research problem, the author uses the data of the State Information System “Justice” related to 56,500 orders on imposition of administrative punishment in accordance with the Article 20.4 of the Code of the Russian Federation on Administrative Offenses for the period of 2010-2017. The author extracts and factorizes the necessary data; JSON data was converted using the algorithm in MapReduce paradigm for the models of factorization and learning. As a result of computer-assisted learning, was obtained the “tree of decisions”. On the “tree of decisions” it is demonstrated that middle of 2011 marks qualitative improvement in judicial practice, which became more uniform and logical; as well as in the context of imposing administrative punishment, the court started using standard circumstances of the case. The more efficient revision of the Article 20.4 of the Code of the Russian Federation on Administrative Offenses allowed in a midterm period to enhance the rule of law in the area of satisfying formalized requirements to ensuring fire safety, by reducing the number of cases from 2012 to 2017 by more than 10 times. The author empirically substantiates the working version of the method of analysis and assessment of qualitative changes in legislation and law enforcement practice based on the computer-assisted learning technique in form of “tree of decisions”.


Keywords: computational methodology, computational experiment, big data, machine learning, intellectual analysis, administrative liability, digital state, artificial intelligence, law, fire safety
This article written in Russian. You can find full text of article in Russian here .

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