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Regulation Modelling and Analysis Using Machine Learning During the Covid-19 Pandemic in Russia
Egor Trofimov1, Oleg Metsker2, Georgy Kopanitsa3
1The All-Russian State University of Justice, Moscow, Russia.
Studies in Health Technology and Informatics
|November 4, 2021
Summary
This study assessed the effectiveness of Russian criminal sanctions during the COVID-19 pandemic using machine learning. Random forest and XGBoost models analyzed fine amounts and sentence remission, providing insights into judicial system adjustments.
Area of Science:
- Criminology
- Public Health
- Computer Science
Background:
- The COVID-19 pandemic necessitated emergency measures globally, impacting public health and judicial systems.
- Russia implemented specific criminal sanctions in response to the pandemic outbreak.
- Understanding the efficiency of these sanctions is crucial for judicial system adaptation.
Purpose of the Study:
- To evaluate the efficiency of Russian criminal sanctions introduced during the COVID-19 pandemic.
- To apply machine learning methods for analyzing judicial decision-making in criminal cases.
- To identify key factors influencing fine amounts and sentence remission.
Main Methods:
- Development of a regression model for fine amounts using Random Forest and XGBoost.
- Calculation of feature importance parameters for regression models.
- Development of classification models for penalty remission and sentence determination using Gradient Boosting.
Main Results:
- Machine learning models were successfully developed to predict fine amounts and sentence outcomes.
- Feature importance analysis provided insights into factors influencing judicial decisions.
- The study demonstrated the utility of machine learning in analyzing the impact of emergency measures on the judicial system.
Conclusions:
- Machine learning methods can effectively assess the efficiency of criminal sanctions during public health crises.
- The findings offer valuable data for understanding the Russian judicial system's response to the COVID-19 pandemic.
- Further research can explore the broader application of these methods in judicial analysis.
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