VAK Russia 5.1.4 UDC 343.9

METHODS FOR APPLYING ARTIFICIAL INTELLIGENCE TECHNOLOGIES IN CRIMINOLOGICAL ANALYSIS (USING THE EXAMPLE OF VIOLENT CRIME IN THE DOMESTIC SPHERE) Methods for applying artificial intelligence technologies in criminological analysis (using the example of violent crime in the domestic sphere)

Published in Vestnik of the St. Petersburg University of the Ministry of Internal Affairs of Russia · Volume 2026, Issue 3 · Pages 203–213 · Rubric: CRIMINAL LAW SCIENCES
Received: 30.03.2026 Accepted: 25.09.2026 Published: 30.09.2026 Language of publication: RUS
Introduction. Against the backdrop of the digitalisation of all spheres of activity and the growing volume of unstructured information, there is a need to update certain elements of the methodology of criminological science. New opportunities arising from the spread of artificial intelligence technologies create the conditions for improving the effectiveness of criminological analysis through the collection, processing and systematisation of large volumes of digital information. Artificial intelligence makes it possible to analyse a significantly larger volume of criminologically relevant information per unit of time. The particular potential of this tool lies in the analysis of types of crime characterised by a large volume of empirical data (for example, domestic violence). The aim of this study is to develop and test a methodology for using artificial intelligence technologies, using the criminological analysis of this type of crime as a case study. Methods. The study was conducted as a retrospective documentary analysis. OpenAI’s ChatGPT generative language model, based on the GPT-5.4 language model family, was used as a supporting tool for extracting, structuring and pre-processing data, with the results subsequently verified by the researcher. The empirical data comprised 523 court rulings from 2012 to 2025, selected from open sources and transcribed into a standardised table of criminologically significant characteristics. Results. Testing of the methodology demonstrated its feasibility and scientific value. Based on the dataset obtained, criminologically significant conclusions were drawn regarding violent crime in the domestic sphere. The effectiveness of using artificial intelligence as an auxiliary tool for extracting, structuring and performing initial processing of criminologically significant data has been confirmed. Conclusions have been drawn regarding its high heuristic and practical value for criminological analysis within a strictly defined research framework, whilst maintaining the researcher’s decisive role in interpreting the results.
digitalisation of criminology, domestic crime, criminological analysis, court rulings, artificial intelligence, ChatGPT
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1. Pavlichenko N. V. Pravovoe i organizacionnoe obespechenie vnedreniya tehnologiy iskusstvennogo intellekta v sistemu MVD Rossii / Iskusstvennyy intellekt na sluzhbe policii: sbornik statey Mezhdunarodnoy nauchno-prakticheskoy konferencii, g. Moskva, 27 maya 2021 g. / vstup. st.: V. D. Shulika, A. N. Konev, V. V. Ageev. Moskva : Akademiya upravleniya MVD Rossii, 2021. S. 13–19.

2. Pruss D. [et al.]. Prediction and Punishment: Critical Report on Carceral AI. SSRN, 2024. 36 p. URL: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5017321. http://dx.doi.org/10.2139/ssrn.5017321

3. McKay C. Predicting Risk in Criminal Procedure: Actuarial Tools, Algorithms, AI and Judicial Decision-Making // Current Issues in Criminal Justice. 2019. Vol. 32, № 5. P. 1–18. http://dx.doi.org/10.2139/ssrn.3494076

4. Il'nickiy A. S. Tehnologii iskusstvennogo intellekta v kriminologicheskoy nauke i preduprezhdenii prestupleniy // Vestnik Moskovskogo universiteta MVD Rossii. 2023. № 7. S. 105–111. https://doi.org/10.24412/2073-0454-2023-7-105-111

5. Serebrennikova A. V., Serebrennikova M. S. Criminological innovations in criminality prevention: status and perspectives // SHS Web of Conferences : IX Baltic Legal Forum “Law and Order in the Third Millennium”, Kaliningrad, December 11, 2020. Kaliningrad : EDPsciences, 2021. Vol. 108. P. 03002. https://doi.org/10.1051/shsconf/202110803002. EDN UAYBAJ

6. Hsieh T. C. [et al.]. Measuring the Unmeasurable –A Study of Domestic Violence Risk Prediction and Management // Journal of Technology in Human Services. 2018. Vol. 36, № 1. P. 56–68. https://doi.org/10.1080/15228835.2017.1417953

7. Rodríguez-Rodríguez I. [et al.]. Modeling and Forecasting Gender-Based Violence through Machine Learning Techniques // Applied Sciences. 2020. Vol. 22, № 10. P. 8244. https://doi.org/10.3390/app10228244

8. Wijenayake S., Graham T., Christen P. A Decision Tree Approach to Predicting Recidivism in Domestic Violence // Ganji M.[et al.] (eds.). Trends and Applications in Knowledge Discovery and Data Mining. Springer, Cham. 2018. Vol 11154. P. 3–15. https://doi.org/10.1007/978-3-030-04503-6_1

9. Berk R. A., Sorenson S. B., Barnes G. Forecasting Domestic Violence: A Machine Learning Approach to Help Inform Arraignment Decisions // Journal of Empirical Legal Studies. 2016. Vol. 13, is. 1. P. 94–115. https://doi.org/10.1111/jels.12098

10. Amrit C. [et al.]. Identifying child abuse through text mining and machine learning //Expert systems with applications. 2017. Vol. 88. P. 402–418. https://doi.org/10.1016/j.eswa.2017.06.035

11. Schrading N. [et al.]. An analysis of domestic abuse discourse on reddit // Conference: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, Portugal, September 17–21, 2015. P. 2577–2583. https://doi.org/10.18653/v1/D15-1309

12. Subramani S. [et al.]. Domestic violence crisis identification from facebook posts based on deep learning // IEEE access. 2018. Vol. 6. P. 54075–54085. https://doi.org/10.1109/ACCESS.2018.2871446

13. Subramani S. [et al.]. Deep Learning for Multi-Class Identification from Domestic Violence Online Posts // IEEE access. 2019. Vol. 7. P. 46210–46224. https://doi.org/10.1109/ACCESS.2019.2908827

14. Subramani S., Vu H. Q., Wang H. Intent classification using feature sets for domestic violence discourse on social media // Conference: 4th Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE). 2017. P. 129–136. https://doi.org/https://doi.org/10.1109/APWConCSE.2017.00030

15. Xue J., Chen J., Gelles R. Using data mining techniques to examine domestic violence topics on Twitter // Violence and gender. 2019. Vol. 6. №. 2. P. 105–114. https://doi.org/10.1089/vio.2017.0066

16. Karystianis G. [et al.]. Prevalence of Mental Illnesses in Domestic Violence Police Records: Text Mining Study // Journal of Medical Internet Research. 2020. Vol. 22, № 12. P. e23725. https://doi.org/10.2196/23725

17. Liu M. [et al.]. Using Social Media to Explore the Consequences of Domestic Violence on Mental Health // Journal of Interpersonal Violence. 2018. Vol. 36, № 3-4. P. 1965–1985. https://doi.org/10.1177/0886260518757756

18. Nui V., Constantino R. E. The association between life satisfaction, emotional support, and perceived health among women who experienced intimate partner violence (IPV) –2007 behavioral risk factor surveillance system // BMC Public Health. 2021. Vol. 21, № 1. https://doi.org/10.1186/s12889-021-10665-4

19. Pashaev H. P. Prestupnost' v sfere semeyno-bytovyh otnosheniy v Respublike Altay i v otdel'nyh sub'ektah Rossiyskoy Federacii v Sibirskom federal'nom okruge: pokazateli, pravovye i organizacionnye mery protivodeystviya: monografiya. Gorno-Altaysk : Gorno-Altayskiy gosudarstvennyy universitet, 2017. 316 c.

20. Zolotuhin S. N. Kriminal'noe nasilie v sfere semeyno-bytovyh otnosheniy : monografiya. Chelyabinsk :Biblioteka A. Millera, 2019. 196 s.

21. Latysheva E. V. O prestupleniyah, vhodyaschih v ob'em nasil'stvennoy prestupnosti v semeyno-bytovoy sfere// Kriminalist'. 2025. № 4 (53). S. 78–85.

22. Schepel'kov V. F., Savin S. D., Timoshina E. M. Domashnee nasilie v Rossiyskoy Federacii (sostoyanie, problemy kriminologicheskoy ocenki) // Vserossiyskiy kriminologicheskiy zhurnal. 2024. T. 18, № 3. S. 232–244. https://doi.org/10.17150/2500-4255.2024.18(3).232-244

23. Kirillov M. A. Kriminologicheskaya harakteristika i preduprezhdenie prestupleniy, sovershaemyh v sostoyanii narkoticheskogo op'yaneniya v semeyno-bytovyh i dosugovyh otnosheniyah: monografiya. Nizhniy Novgorod : Nizhegorodskaya akademiya MVD Rossii, 2010. 298 s.

24. Varygin A. N. Osobennosti semeyno-bytovyh prestupleniy i ih preduprezhdeniya // Vestnik Kazanskogo yuridicheskogo instituta MVD Rossii. 2014. № 3 (17). S. 30–34.