Siberian Federal University

Issue

Journal of Siberian Federal University. Humanities & Social Sciences. 2026 19 (4)

Authors

Sudakova, Tatiana M.

Contact information

Sudakova, Tatiana M.: Baikal State University (Irkutsk, Russian Federation); ; ORCID: 0000-0002-2613-1207

Keywords

algorithmic criminology; computational criminology; predictive analytics; machine learning in criminology

Abstract

The problem of methodology is currently an actively developed aspect of the study of modern crime features and trends in modern criminology and is directly related to the assessment of the role and significance of its computational, technological, and digital nature. In the context of the digital transformation of society, the principles and methods of traditional classical criminology, based on linear causality, retrospective statistics and ideas about the sustainability of social patterns, are largely losing their explanatory and predictive potential. The methodological approaches of criminology should act as a systematic set of those strategic initiatives and principles that make it possible to form adequate ways of measuring and evaluating modern crime in order to determine the actual degree of its social danger. The use of algorithms for predictive purposes is the essence and technological basis of algorithmic criminology. Its development is associated with many critical assessments of both the methods themselves and their evaluation and recognition. Today, the creation of an accurate predictive model is one of the key components of predictive policing as the main mechanism of algorithmic criminology, which has been seriously developed in the theory of foreign criminology and is actively being implemented in the practice of law enforcement agencies and has received as serious criticism of the effectiveness and transparency of the models used, as well as an assessment of the ethical and legal principles of their application. The development of algorithmic criminology today is aimed at creating an accurate and reliable algorithmic model as an important stage in the effective fight against cybercrime, followed by an analysis of evidence of its effectiveness and the proportionality of risks and benefits. Recent research shows that criminology needs a deeper epistemological understanding of the scientific value of data-driven tools in order to further a serious discussion about their use

Pages

869–879

EDN

FOZWLD

Paper at repository of SibFU

https://elib.sfu-kras.ru/handle/2311/158273

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).