Digital Decision Support Technologies in Legal Practice: Psychological Profile and User Trust
EDN: IJDDMP
Abstract
Decision support systems (DSS) are a promising technology based on artificial intelligence. While such systems are currently used in a number of fields and industries, the legal field remains one of the most challenging to implement.
The purpose of this paper is to analyze the psychological aspects of user-AI interaction within these systems. Based on an analysis of existing models of human-technology interaction and the author's methodology, the study design is presented.
The results of a focus group with experts from executive and judicial authorities (N = 8) are presented: the role of DSS in legal work, and the benefits and concerns associated with using such systems. Parameters relevant for profiling and further adapting systems to specific users are highlighted.
The article also discusses the prospects and issues of implementing DSS in law enforcement practice.
About the Authors
A. Yu. Kuzmin
Saint Petersburg State University
Russian Federation
Russian Federation
Andrey Yu. Kuzmin, Department Assistant
Saint Petersburg
O. O. Gofman
Saint Petersburg State University
Russian Federation
Russian Federation
Olga O. Gofman, PhD, Assistant Professor
Saint Petersburg
S. V. Kovalchuk
ITMO University
Russian Federation
Russian Federation
Sergey V. Kovalchuk, PhD, Associate Professor
Saint Petersburg
References
1. Gofman O. O., Kuzmin A. Yu., Kovalchuk S. V. (2025). Symbiotic human — AI interaction in decision support systems // Organizational Psychology [Organizatsionnaia psikhologiia] 2025. Vol. 15, N 1. P. 297–321. DOI 10.17323/2312-5942-2025-15-1-297-321. (In Russ.).
2. Canalli R. L. Interpretable AI Models for Judicial Decision-Making: Beyond Explicability Towards Legal Due Process. e-Publica, 2024. N 11. P. 117–129.
3. Dafoe A. et al. Cooperative AI: machines must learn to find common ground // Nature. 2021. Vol. 593. N 7857. P. 33–36.
4. Davis F. D., Bagozzi R. P., Warshaw P. R. User acceptance of computer technology: A comparison of two theoretical models // Management science. 1989. Vol. 35. N 8. P. 982–1003.
5. Deeks A. The judicial demand for explainable artificial intelligence // Columbia Law Review. 2019. Vol. 119, N 7. P. 1829–1850.
6. Fragiadakis G. et al. Evaluating human-ai collaboration: A review and methodological framework // arXiv preprint arXiv: 2407.19098. 2024.
7. Gupta M., George J. F. Toward the development of a big data analytics capability // Information & Management. 2016. Vol. 53. Iss. 8. P. 1049–1064.
8. Kim T. et al. One AI does not fit all: A cluster analysis of the laypeople’s perception of AI roles // Proceedings of the 2023 CHI conference on human factors in computing systems. 2023. P. 1–20.
9. Kochenderfer M. J., Wheeler T. A., Wray K. H. Algorithms for decision making. MIT Press, 2022.
10. Lake B. M. et al. Building machines that learn and think like people // Behavioral and brain sciences. 2017. Vol. 40. P. e253.
11. Lee E. A. Cyber physical systems: Design challenges // 2008 11th IEEE international symposium on object and component-oriented real-time distributed computing (ISORC). IEEE, 2008. P. 363–369.
12. Maclure J. AI, explainability and public reason: The argument from the limitations of the human mind // Minds and Machines. 2021. Vol. 31. N 3. P. 421–438.
13. Malek M. A. Transparency in Predictive Algorithms: A Judicial Perspective. Advance. 2021. P. 1–13.
14. Shamim S., Zeng J., Shariq S. M., Khan Z. Role of big data management in enhancing big data decision-making capability and quality among Chinese firms: A dynamic capabilities view // Information & Management. 2019. Vol. 56. N 6. P. 103135.
15. Shelar A., Moharir M. Predicting Outcomes of Court Judgments — A Machine Learning Approach. In: Proceedings of the 2021 International Conference on Intelligent Technologies (CONIT), Hubli, India, 25–27 June 2021. P. 1–6.
16. Sreedharan S. et al. A Unifying Bayesian Formulation of Measures of Interpretability in HumanAI // arXiv preprint arXiv: 2104.10743. 2021.
17. Tabesh P., Mousavidin E., Hasani S. Implementing big data strategies: A managerial perspective // Business Horizons. 2019. Vol. 62. N. 3. P. 347–358.
18. Venkatesh V. et al. User acceptance of information technology: Toward a unified view // MIS quarterly. 2003. P. 425–478.
19. Venkatesh V., Davis F. D. A theoretical extension of the technology acceptance model: Four longitudinal field studies // Management science. 2000. Vol. 46. N 2. P. 186–204.
20. Xu Z. Human judges in the era of artificial intelligence: challenges and opportunities // Applied Artificial Intelligence. 2022. Vol. 36. N 1. P. 2013652.
Review
For citations:
Kuzmin A.Yu., Gofman O.O., Kovalchuk S.V. Digital Decision Support Technologies in Legal Practice: Psychological Profile and User Trust. Administrative Consulting. 2025;(5):106–114. (In Russ.) EDN: IJDDMP
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