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Social and Legal Studios

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Social and Legal Studios

Vol. 9, No. 2, 2026

Social and Legal Studios

ISSN 2617-4162

e-ISSN 2617-4170

Publisher: Lviv State University of Internal Affairs

Received 24.12.2025, Revised 20.03.2026, Accepted 27.05.2026 Published 01.06.2026
Pages 67-78
https://doi.org/10.32518/sals2.2026.67
Vol. 9, No. 2, 2026
Article

Artificial intelligence algorithms in legal practice: Legal boundaries, socio-legal implications and liability


Abstract

The relevance of this study stems from the rapid digitalisation of legal practice and the growing role of artificial intelligence algorithms in legal decision-making processes, which gives rise to new legal and ethical challenges. The aim of the study was to examine the intersection of legal, ethical and institutional aspects of the application of artificial intelligence in the legal sphere, as well as to define the limits of permissible use of algorithmic systems and approaches to the allocation of liability. The study employed systematic, comparative legal and formal legal methods, as well as an analysis of judicial practice. The risks of automated decision-making were examined: algorithmic bias, the “black box” problem, the reproduction of discrimination and threats to judicial independence. These risks are confirmed by empirical research in the field of algorithmic justice and an analysis of the practice of using risk prediction systems in criminal proceedings (e.g., COMPAS in the US). The regulation of AI is analysed using the example of the EU Artificial Intelligence Act and the GDPR, as well as the ECHR’s approaches to ensuring transparency and procedural safeguards. Approaches to the allocation of responsibility between developers, suppliers, users and the state are summarised, and a model of algorithmic transparency with human oversight and the parties’ right to be informed is proposed. International experience from the US, France, Singapore and China is examined, as well as the prospects for implementing AI in Ukraine within the framework of the EU-ITC and case law analysis systems. Particular attention is paid to the concept of distributed algorithmic liability and the application of the analogy of liability for a source of increased danger to high-risk AI systems. A model of algorithmic transparency is proposed, incorporating the parties’ right to be notified of AI use, algorithmic expertise, and the court’s obligation to justify its consideration of AI recommendations. The principle of “human error” is outlined as a guarantee of judicial discretion and the independence of the judge. The results of the study can be used to improve national legislation in the field of digital justice and to implement standards of algorithmic transparency


Keywords: algorithmic accountability; ethics; discrimination; digital justice; EU Artificial Intelligence Act; General Data Protection Regulation


Suggested citation

Lukashchuk, Yu., & Tsmots, U. (2026). Artificial intelligence algorithms in legal practice: Legal boundaries, socio-legal implications and liability. Social and Legal Studios, 9(2), 67-78. https://doi.org/10.32518/sals2.2026.67
References
  1. Angwin, J., Larson, J., Mattu, S., & Kirchner, L. (2016). Machine bias. ProPublica. Retrieved from https://www.propublica.org/ article/machine-bias-risk-assessments-in-criminal-sentencing.
  2. Baranov, O.A. (2023). Definition of the term “artificial intelligence”. Information and Law, 1(44), 32-49. doi: 10.37750/26166798.2023.1(44).287537.
  3. Barfield, W. (Ed.). (2020). The Cambridge handbook of the law of algorithms. Cambridge: Cambridge University Press.doi: 10.1017/9781108680844.
  4. Barocas, S., & Selbst, A.D. (2016). Big data’s disparate impact. California Law Review, 104(3), 671-732.
  5. Calo, R. (2015). Robotics and the lessons of cyberlaw. California Law Review, 103(3), 513-563.
  6. Cath, C., Wachter, S., Mittelstadt, B., Taddeo, M., & Floridi, L. (2018). Artificial intelligence and the “good society”. Science and Engineering Ethics, 24(2), 505-528. doi: 10.1007/s11948-017-9901-7.
  7. Citron, D.K. (2008). Technological due process. Washington University Law Review, 85(6), 1249-1313.
  8. Davenport, T.H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108-116.
  9. Dressel, J., & Farid, H. (2018). The accuracy, fairness, and limits of predicting recidivism. Science Advances, 4(1), article number eaao5580. doi: 10.1126/sciadv.aao5580.
  10. Edwards, L. (2022). Regulating AI in Europe: Four problems and four solutions. Retrieved from https://www.adalovelaceinstitute. org/report/regulating-ai-in-europe/.
  11. European Commission High-Level Expert Group on AI. (2019). Ethics guidelines for trustworthy AI. Retrieved from https:// digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai.
  12. European Convention on Human Rights. (1950, November). Retrieved from https://www.echr.coe.int/en/web/echr/europeanconvention-on-human-rights.
  13. Floridi, L., et al. (2018). AI4People ‒ an ethical framework. Minds and Machines, 28(4), 689-707. doi: 10.1007/s11023-0189482-5.
  14. Goddard, K., Roudsari, A., & Wyatt, J. C. (2012). Automation bias in clinical decision support systems. Artificial Intelligence in Medicine, 57(3), 193-200. doi: 10.1016/j.artmed.2012.01.001.
  15. Hildebrandt, M. (2020). Law for computer scientists and other folk. Oxford: Oxford University Press. doi: 10.1093/ oso/9780198860877.001.0001.
  16. Judgment of the European Court of Human Rights in the Case No. 47143/06 “Zakharov v. Russia”. (2015, December). Retrieved from https://hudoc.echr.coe.int/eng?i=001-155159
  17. Judgment of the European Court of Human Rights in the Case No. 58170/13, 62322/14, 24960/15 “Big Brother Watch and Others v. the United Kingdom”. (2021, May). Retrieved from https://hudoc.echr.coe.int/eng?i=001-210077.
  18. Logg, J.M., Minson, J.A., & Moore, D.A. (2019). Algorithm appreciation. Organizational Behavior and Human Decision Processes, 151, 90-103. doi: 10.1016/j.obhdp.2018.12.005.
  19. McGinnis, J.O., & Pearce, R.G. (2014). The great disruption. Fordham Law Review, 82(6), 3041-3066.
  20. O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. New York: Crown Publishers. doi: 10.5860/crl.78.3.403.
  21. OECD. (2020). OECD principles on AI. Retrieved from https://www.oecd.org/en/topics/ai-principles.html.
  22. Pagallo, U. (2013). The laws of robots. New York: Springer. doi: 10.1007/978-94-007-6564-1
  23. Parasuraman, R., & Manzey, D.H. (2010). Complacency and bias in automation. Human Factors, 52(3), 381-410. doi: 10.1177/0018720810376055.
  24. Parasuraman, R., & Riley, V. (1997). Humans and automation. Human Factors, 39(2), 230-253. doi: 10.1518/001872097778543886.
  25. Pasquale, F. (2015). The black box society. Cambridge: Harvard University Press.
  26. Raji, I.D., et al. (2020). Closing the AI accountability gap. arXiv. doi: 10.48550/arXiv.2001.00973. 
  27. Regulation of European Union No. 2016/679 “General Data Protection”. (2016, April). Retrieved from https://eur-lex.europa. eu/eli/reg/2016/679/oj.
  28. Regulation of European Union No. 2024/1689 “Artificial Intelligence Act”. (2024, June). Retrieved from https://eur-lex.europa.eu/eli/reg/2024/1689/oj.
  29. Remus, D., & Levy, F. (2017). Can robots be lawyers? Georgetown Journal of Legal Ethics, 30, 501-558.
  30. Sourdin, T. (2018). Judge v robot? University of New South Wales Law Journal, 41(4), 1114-1133.
  31. Susskind, R. (2019). Online courts and the future of justice. Oxford: Oxford University Press. doi: 10.1093/ oso/9780198838364.001.0001.
  32. Veale, M., & Borgesius, F.Z. (2021). Demystifying the AI Act. Computer Law Review International, 22(4), 97-112.
  33. Wachter, S., Mittelstadt, B., & Floridi, L. (2017a). Transparent AI. Science Robotics. doi: 10.1126/scirobotics.aan6080.
  34. Wachter, S., Mittelstadt, B., & Floridi, L. (2017b). Why a right to explanation does not exist in GDPR. International Data Privacy Law, 7(2), 76-99. doi: 10.1093/idpl/ipx005.
  35. Yeung, K., & Ulbricht, L. (2022). Understanding algorithmic regulation. Regulation & Governance, 16(1), 3-22. doi: 10.1111/ rego.12437.