Big Data and predictive justice: social aspects digital transformation of law in Russia

The potential of using Big Data and AI technologies in the legal sector as a factor in ensuring Russia’s national security is studied. The relevance of the research topic is due to the rapid growth in the volume of legally significant information, the integration of state information systems into the Data Economics National Project, as well as the need to counter external destructive information and psychological effects. The purpose of the study is to substantiate theoretical and methodological approaches to the use of Big Data and AI technologies for the transition to a predictive model of law enforcement that ensures the realization of Russia’s national interests in a digital society.

The content of the Big Data concept in the Russian legal system is clarified, based on national standards and strategic documents. The key methods of legal information analysis are classified, from classical statistics and correlation analysis to ML and NLP methods. The risks and potential of implementing predictive analytics to ensure national security, information independence, and the effectiveness of justice are identified. The hypothesis of the study is that the integration of Big Data and AI technologies into Russia’s legal sector creates the technological prerequisites for the transition from a reactive to a predictive model of law enforcement, but the implementation of this transition requires a balance between technological efficiency and ethical and legal constraints, including personal data protection, explainability of algorithmic solutions, and judicial discretion preservation.

The author pays special attention to the social risks of digital transformation such as algorithmic bias, the opacity of the “black boxes” of neural networks, as well as the need to preserve citizens’ trust in justice. The research paper concludes that the integration of Big Data, AI, and law is a fundamental vector of the legal system development, and Russia is able to form its own standards for the digital transformation of law, stren gthening humanitarian and technological sovereignty.

1. Babiychuk, V. V. (2024). The role of artificial intelligence in the resocialization of offenders and reducing the risk of reoffending. In: Penal enforcement system: history and modernity: Proceedings of the Interuniversity Scientific and Practical Conference with International Participation, Pskov, April 18–19, 2024 . Pskov: University of the Federal Penitentiary Service of Russia. (In Russian).

2. Bonner, B., Walsham, G., Kimble, C., & McInerney, C. (2009). Restoring balance: How history tilts the scales against privacy. An Actor-Network Theory Investigation. Information and Organization, 19 (2), 84–102. https://doi.10.1016/j.infoandorg.2009.01.001

3. Citron, D. K., Pasquale, F. (2014). The Scored Society: Due Process for Automated Predictions. Washington Law Review, 89 .

4. Laney, D. (2025). 3-D Data Management: Controlling Data Volume, Velocity and Variety. Open Journal of Applied Sciences, 15 (7).

5. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., et al. (2011). Big Data: The next frontier for innovation, competition, and productivity . McKinsey Global Institute.

6. Rai, A. K., Malviya, A. K. (2014). Investigating the inclinations of research and practices in Hadoop: A systematic review. In: 2014 International Conference on Confluence: The Next Generation Information Technology Summit. IEEE. https://doi.10.1109/CONFLUENCE.2014.6949381

7. Skiba, A. P. (2025). Improving the efficiency of execution of criminal penalties: expanding the scope of information technologies. Penal Enforcement Law, 20 (2), 198–208. (In Russian). https://doi.org/10.33463/2687-122X.2025.20(1-4).2.198–208

8. Sun, Y. (2023). Historical experience and prospects of creating «smart courts» in the PRC: problems of applying artificial intelligence technology. History of State and Law, 5 , 44–47. (In Russian). https://doi.org/10.18572/1812-3805-2023-5-44-47

9. Tagaeva, S. N., Gatiyatullina, E. M. (2024). Big data and personal data: legal nature and regulatory issues. Digital Law, 5 (2), 40–52. (In Russian). https://doi.org/10.38044/2686-9136-2024-5-2-40-52

10. Tikhomirov, Yu. A., Kashanin, A. V., Churakov, V. D., Osipova, P. M., Pogrebnoy, E. O., Rofin, M. P., et al. (2021). Interpretation and application of big data in jurisprudence and legal practice. Moscow: Yustitsinform. (In Russian).

11. Tyunina, A. M., Kondusova, V. V., Kukueva, D. S., & Amelin, P. P. (2025). Application of Explainable AI (XAI) methods for interpreting deep neural network decisions. In: Issues and prospects of modeling systems and processes: Proceedings of the All-Russian Scientific and Practical Conference, Voronezh, October 14, 2025. Voronezh: Voronezh State University of Forestry and Technologies. (In Russian). https://doi.org/10.58168/PPSPM2025_185-191