CONCEPTUALIZATION OF THE SCIENTIFIC PARADIGM OF REGULATING ARTIFICIAL INTELLIGENCE IN PUBLIC ADMINISTRATION
Abstract
The article systematizes modern scientific approaches to the study of artificial intelligence in public administration. The main dimensions of the scientific paradigm are identified, namely theoretical and methodological, institutional and managerial, legal, ethical, socially participatory and applied. Based on the analysis of foreign and domestic research, the main scientific approaches to understanding artificial intelligence as a socio-technical phenomenon that functions at the intersection of technologies, institutions, law and society are systematized. Five key dimensions of the study are identified: theoretical and methodological, institutional and managerial, legal and ethical, socially participatory, and the Ukrainian context. It is proven that the implementation of AI transforms the logic of managerial decision-making, changes the nature of accountability, strengthens the role of data and necessitates the need to ensure transparency, fairness and control of algorithmic systems.
Particular attention is paid to the risk-based approach to regulation, the principle of algorithmic accountability, data management and public involvement in the processes of policy formation in the field of AI. It is substantiated that the effectiveness and legitimacy of the use of artificial intelligence in the public sector are determined not only by technological characteristics, but also by the level of institutional capacity of the state, the quality of regulatory and legal support and public trust. The generalization of theoretical approaches and international experience made it possible to propose a holistic model of institutional regulation of artificial intelligence in public administration, which combines regulatory, institutional, infrastructural, ethical and participatory components. The novelty of the study lies in the formation of an integrated approach to understanding AI as an institutional element of the public administration system. The practical significance of the results lies in the possibility of their use for the development of public policy, improvement of regulatory mechanisms and introduction of innovations in the activities of government bodies
References
2. Bostrom, N. (2014). Superintelligence: Paths, Dangers, Strategies. Oxford: Oxford University Press, URL: https://global.oup.com/academic/product/superintelligence-97801996781123 [in English]
3. Tegmark, M. (2017). Life 3.0: Being Human in the Age of Artificial Intelligence. New York: Knopf. URL: https://www.amazon.com/Life-3-0-Being-Artificial-Intelligence/dp/1101946598 [in English]
4. Floridi, L. (2023). The Ethics of Artificial Intelligence: Principles, Challenges, and Opportunities. Oxford : Oxford University Press. URL: https://global.oup.com/academic/product/the-ethics-of-artificial-intelligence-9780198883098 [in English]
5. Floridi, L., Cowls, J., Beltrametti, M. et al. (2018). AI People–An Ethical Framework for a Good AI Society. Minds and Machines. Vol. 28, 4, 689–707. URL: https://link.springer.com/article/10.1007/s11023-018-9482-5 [in English]
6. Goldsmith, S. & Crawford, S. (2014). The Responsive City: Engaging Communities Through Data-Smart Governance. San Francisco : Jossey-Bass. URL: https://surl.li/otvcyi [in English]
7. Goldsmith, S. & Yang, J. (2026). AI and the Transformation of Accountability and Discretion in Urban Governance. DOI: https://doi.org/10.1016/j.ugj.2026.02.005 [in English]
8. Sun, T., & Medaglia, R. (2019). Mapping the Challenges of AI in Public Administration. Government Information Quarterly. Vol. 36, 2, 368–383. https://doi.org/10.1016/j.giq.2018.09.008 [in English]
9. Veale, M. & Brass, I. (2019). Administration by Algorithm? Public Management Meets Public Sector Machine Learning. Algorithmic Regulation / ed. by K. Yeung, M. Lodge. Oxford: Oxford University Press, 121–149. URL: https://discovery.ucl.ac.uk/id/eprint/10072507/ [in English]
10. Janssen, M., Brous, P. & Estevez, E. (2020). Data Governance in the Public Sector // Government Information Quarterly. Vol. 37, 3. DOI: https://doi.org/10.1016/j.giq.2020.101493 [in English]
11. Parthasarathy, A., Phalnikar, A., Jauhar, A. et al. (2024). Participatory Approaches in AI Development and Governance. URL: https://vidhilegalpolicy.in/research/participatory-ai-approaches-in-ai-development-and-governance/ [in English]
12. Nkongolo, M. W. (2026). Pluralism in AI Governance: Toward Sociotechnical Alignment and Normative Coherence. URL: https://arxiv.org/abs/2602.15881 [in English]
13. Kovalenko, L. & Kovalenko, B. (2025) Rozvytok doktryny vprovadzhennia shtuchnoho intelektu v publichnomu upravlinni [Development of the doctrine of the introduction of artificial intelligence in public administration]. Metaverse Science, Society and Law. 2, 1. URL: https://metaversescience.com/. [in Ukrainian].
14. Hachkevych, A. (2025) Avtomatyzatsiia protsesiv u publichnomu upravlinni ta zabezpechennia zakonnosti pryiniattia rishen na osnovi ShI [Automation of processes in public administration and ensuring the legitimacy of decision-making based on AI]. Aktualni problemy pravoznavstva. 4, 57–62. URL: https://appj.wunu.edu.ua/index.php/appj/article/view/2215. [in Ukrainian].
15. Karpenko, Y. V. (2019) Etychni pryntsypy zastosuvannia shtuchnoho intelektu v publichnomu upravlinni [Ethical principles of the application of artificial intelligence in public administration]. Informatsiini tekhnolohii. 4(95). 93–97. URL: https://surl.li/kmuwhg [in Ukrainian].
16. Shevchenko, N., Shevchenko, O. & Drob, O. (2025) Zastosuvannia upravlinskykh teorii pry reformuvanni publichnoho upravlinnia v Ukraini [Application of management theories in public administration reform in Ukraine] // Visnyk Dniprovskoi akademii neperervnoi osvity. Seriia: Publichne upravlinnia ta administruvannia. 2, 59–67. DOI: https://doi.org/10.54891/2786-698X-2025-2-6. [in Ukrainian].
ISSN 
ISSN 
