The Use of Artificial Intelligence in Advocacy

Authors

  • Zuzana Mlkvá Illýová Comenius University Bratislava, Faculty of Law, Institute of Clinical Legal Education
  • Igor Hron Comenius University Bratislava, Faculty of Law, Department of Legal History and Comparative Law

DOI:

https://doi.org/10.46282/bpf.2025.11

Keywords:

Artificial Intelligence, legal profession, legal technology, regulation

Abstract

The article examines the modern use of artificial intelligence (AI) tools in legal practice, focusing on both their benefits and risks. It analyzes the areas in which AI is already applied, including legal research, drafting of legal documents, due diligence, summarization of case files, litigation outcome prediction and administrative support for law firms, while also highlighting practical examples from international and domestic contexts. The article also addresses the risks associated with the use of AI, in particular issues of confidentiality and data protection, reliability of outputs and the need to preserve the human factor in legal services. It further presents the results of a survey conducted among Slovak lawyers, which explored the extent to which they use AI tools in practice and identified the most frequently used applications. The aim of the article is to provide a comprehensive overview of the ways in which AI can be used in legal practice, to analyze available tools and to assess the risks that the use of AI poses for the performance of the legal profession and the preservation of its core values. 

References

1. ALETRAS, Nicolaos – TSARAPATSANIS, Dimitrios – PREOŢIUC-PIETRO, Daniel – LAMPOS, Vasileios. Predicting judicial decisions of the European Court of Human Rights: a Natural Language Processing perspective. In: PeerJ Computer Science, roč. 2, č. 1 (2016), s. 2. DOI: https://doi.org/10.7717/peerj-cs.93

2. ALLEN, Kevin – BRIDEWELL, Will. Emerging AI & Law approaches to automating analysis and retrieval of electronically stored information in discovery proceedings. In: Artificial Intelligence and Law, roč. 18, č. 4 (2010), s. 312-313. DOI: https://doi.org/10.1007/s10506-010-9098-4

3. American Bar Association, Standing Committee on Ethics and Professional Responsibility. Formal Opinion 512: Generative Artificial Intelligence Tools. 2024. Dostupné na: https://www.americanbar.org/content/dam/aba/administrative/professional_responsibility/ethics-opinions/aba-formal-opinion-512.pdf.

4. BENGIO, Yoshua – DVIVAN, Réjean – VINCENT, Pascal. A Neural Probabilistic Language Model. In: Journal of Machine Learning Research, roč. 3 (2003), s. 1137.

5. CHARLOTIN, Damien. AI Hallucination Cases. Dostupné na: https://www.damiencharlotin.com/hallucinations/.

6. CHOI, Jonathan – MONAHAN, Amy – RACHLINSKI, Jeffrey J. Lawyering in the Age of Artificial Intelligence. In: Minnesota Law Review, roč. 109, č. 1 (2024), s. 150.

7. COSTANTINI, Stefania – DE GASPERIS, Giovanni – OLIVIERI, Raffaele. Digital forensics and investigations meet artificial intelligence. In: Annals of Mathematics and Artificial Intelligence, roč. 86, č. 1 (2019), s. 194. DOI: https://doi.org/10.1007/s10472-019-09632-y

8. Council of Bars and Law Societies of Europe. Guide on the use of Artificial Intelligence-based tools by lawyers and law firms in the EU. 2022. Dostupné na: https://www.ccbe.eu/fileadmin/speciality_distribution/public/documents/IT_LAW/ITL_Reports_studies/EN_ITL_20220331_Guide-AI4L.pdf.

9. COUTURE, Robert J. The Impact of Artificial Intelligence on Law Firms’ Business Models. In: Harvard Law School Center on the Legal Profession – Insights, 25.2.2025. Dostupné na: https://clp.law.harvard.edu/knowledge-hub/insights/the-impact-of-artificial-intelligence-on-law-law-firms-business-models/.

10. DE CASTRO, Leo – CHEN, Yuxi – WANG, Shenzhi – SONG, Dawn. Privacy-preserving large language model inference via GPU-accelerated fully homomorphic encryption. In: NeurIPS Safe Generative AI Workshop 2024, s. 5.

11. DEMIR, Mikail – YILMAZ, Abdullah – KAYA, Berk. LegalGuardian: A Privacy-Preserving Framework for Secure Integration of Large Language Models in Legal Practice. In: arXiv preprint arXiv:2501.10915 (2025), s. 6.

12. FAGAN, Frank. A View of How Language Models Will Transform Law. In: Tennessee Law Review, roč. 92, č. 1 (2024), s. 35-36.

13. FBE – Federation of European Bars. European Lawyers in the Era of Chat-GPT: Guidelines 2.0 on how European lawyers should act regarding AI (ChatGPT). 2023. Dostupné na: https://www.fbe.org/wp-content/uploads/2024/10/European-lawyers-in-the-era-of-ChatGPT-Guidelines-2.0-on-how-lawyers-should-take-advantage-of-the-opportunities-offered-by-large-language-models-and-generative-AI.pdf.

14. FINA, Siegfried – NG, Irene. Big Data & Litigation: Analyzing the Expectation of Lawyers to Provide Big Data Predictions when Advising Clients. In: Indian Journal of Law and Technology, roč. 13, č. 1 (2017), s. 3. DOI: https://doi.org/10.55496/MNPZ6794

15. HABERNAL, Ivan – RUPP, Constantin – HIRSCH, Tobias. Mining legal arguments in court decisions. In: Artificial Intelligence and Law, roč. 32 (2024), s. 582. DOI: https://doi.org/10.1007/s10506-023-09361-y

16. HACKER, Philipp – ENGEL, Andreas – MAUER, Marco. Regulating ChatGPT and other Large Generative AI Models. In: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency (FAccT '23). Association for Computing Machinery, 2023, s. 1112–1123. https://doi.org/10.1145/3593013.3594067. DOI: https://doi.org/10.1145/3593013.3594067

17. HRON, Igor – MLKVÁ ILLÝOVÁ, Zuzana. ECtHR: Kulák v. Slovakia (Application no. 57748/21, 3 April 2025): Exposing Structural Flaws in the Slovak Code of Criminal Procedure on Legal Professional Privilege. In: Bratislava Law Review, roč. 9, č. 1 (2025), s. 255-268. DOI: https://doi.org/10.46282/blr.2025.9.1.1020

18. JANG, Myeongjun – STIKKEL, Gábor. Leveraging Natural Language Processing and Large Language Models for Assisting Due Diligence in the Legal Domain. In: Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 6: Industry Track), 2024, s. 155. DOI: https://doi.org/10.18653/v1/2024.naacl-industry.14

19. JIANG, Minhao – WANG, Yiran – LI, Zhe. Does data contamination make a difference? Insights from intentionally contaminating pre-training data for language models. In: ICLR 2024 Workshop on Navigating and Addressing Data Problems for Foundation Models, 2024, s. 2.

20. LI, Haoran – CHEN, Xiang – WANG, Kai. Multi-step Jailbreaking Privacy Attacks on ChatGPT. In: Findings of the Association for Computational Linguistics: EMNLP 2023, s. 4144-4145. DOI: https://doi.org/10.18653/v1/2023.findings-emnlp.272

21. LYON, Christopher. Fake Cases, Real Consequences: Misuse of ChatGPT Leads to Sanctions. In: NYLitigator, roč. 28, č. 2 (2023), s. 12.

22. MA, Guangze. Informetric Analysis of Researches on Legal Issues Related to Artificial Intelligence. In: Asia Conference on Cognitive Engineering and Intelligent Interaction (CEII), 2023, s. 131. DOI: https://doi.org/10.1109/CEII60565.2023.00031

23. MEDVEDEVA, Masha – VOLS, Michel – WIELING, Martijn. Using machine learning to predict decisions of the European Court of Human Rights. In: Artificial Intelligence and Law, roč. 28, č. 2 (2020), s. 237-266. DOI: https://doi.org/10.1007/s10506-019-09255-y

24. MUNIR, Bakht – SHAH, Arsalan – KHAN, Imran et al. Evaluating AI in Legal Operations: A Comparative Analysis of Accuracy, Completeness, and Hallucinations in ChatGPT-4, Copilot, DeepSeek, Lexis+ AI, and Llama 3. In: International Journal of Legal Information, roč. 53, č. 1 (2025), s. 7-8. DOI: https://doi.org/10.2139/ssrn.5331771

25. PERLIN, Jonah. How the Billable Hours Can Survive Generative AI. In: Stetson Business Law Review, 20.6.2025, s. 3. Dostupné na: https://ssrn.com/abstract=5313848. DOI: https://doi.org/10.2139/ssrn.5313848

26. SCHWARCZ, Daniel – MANNINGS, Sam – KRAKOW, David et al. AI-Powered Lawyering: AI Reasoning Models, Retrieval Augmented Generation, and the Future of Legal Practice. Minnesota Legal Studies Research Paper No. 25-16, 2025, s. 30. DOI: https://doi.org/10.2139/ssrn.5162111

27. SIINO, Marco – AMARAL, Pedro – LOPEZ, Juan et al. Exploring LLMs Applications in Law: A Literature Review on Current Legal NLP Approaches. In: IEEE Access, roč. 13, č. 1 (2025), s. 18255. doi:10.1109/ACCESS.2025.3533217. DOI: https://doi.org/10.1109/ACCESS.2025.3533217

28. SRA – Solicitors Regulation Authority. SRA approves first AI-driven law firm – Garfield.Law. Tlačová správa 6.5.2025. Dostupné na: https://www.sra.org.uk/garfield-ai.

29. SURDEN, Harry. Machine Learning and Law. In: Washington Law Review, roč. 94, č. 1 (2019), s. 109-110.

30. TU, Sean – CYPHERT, Amy – PERL, Samuel. Artificial Intelligence: Legal Reasoning, Legal Research and Legal Writing. In: Minnesota Journal of Law, Science & Technology, roč. 25, č. 2 (2024), s. 122.

31. TYSS, Santosh – HADDAD, Rashid – GRABMAIR, Matthias. ECtHR-PCR: A Dataset for Precedent Understanding and Prior Case Retrieval in the European Court of Human Rights. In: Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), s. 5474.

32. TYSS, Santosh – NOLASCO, Isaac – GRABMAIR, Matthias. LeCoPCR: Legal Concept-guided Prior Case Retrieval for European Court of Human Rights cases. In: NAACL (Findings), 2025, s. 1658.

33. TYSS, Santosh – VARGAS, Alberto – GRABMAIR, Matthias et al. LexGenie: Automated Generation of Structured Reports for European Court of Human Rights Case Law. In: CoRR, 2025, s. 5.

34. XU, Huihui – ŠAVELKA, Jaromír – ASHLEY, Kevin. Using Argument Mining for Legal Text Summarization. In: VILLATA, Serena – HARAŠTA, Jakub – KŘEMEN, Petr (eds.). Legal Knowledge and Information Systems. Amsterdam: IOS Press, 2020, s. 185. DOI: https://doi.org/10.3233/FAIA200862

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Published

2025-12-31