Smart governance and Bureaucratic Accountability: Assessing the Legal Risks of AI-Assisted Public Administration Systems
DOI:
https://doi.org/10.51903/7zfjzs51Keywords:
Artificial Intelligence, Smart governance, Bureaucratic Accountability,, Legal Risk, Public AdministrationAbstract
The increasing adoption of Artificial Intelligence (AI) in public administration has transformed government operations by improving efficiency, service delivery, and data-driven decision-making. However, the growing reliance on AI systems also raises concerns regarding accountability, transparency, and legal risks associated with automated administrative decisions. This study aims to examine the relationship between AI implementation, smart governance, bureaucratic accountability, and legal risk within public administration systems. A mixed-method policy evaluation approach with a sequential explanatory design was employed. Quantitative data were collected from 224 respondents representing government officials, information technology officers, legal experts, and academics involved in digital governance initiatives. The quantitative analysis was conducted using Partial Least Squares–Structural Equation Modeling (PLS-SEM), while qualitative findings were analyzed through thematic analysis to provide contextual interpretation. The results indicate that AI implementation significantly enhances smart governance (β = 0.782, p < 0.001), which subsequently strengthens bureaucratic accountability (β = 0.827, p < 0.001). Furthermore, bureaucratic accountability significantly reduces legal risk (β = −0.641, p < 0.001). Mediation analysis reveals that the benefits of AI implementation in reducing legal risk are indirectly achieved through improvements in governance quality and accountability mechanisms. The study proposes the Smart governance–Accountability–Legal Risk (SGALR) Framework as an integrative model that links digital governance, public accountability, and AI-related legal risk. The findings highlight the importance of responsible AI governance, algorithmic transparency, regulatory compliance, and institutional oversight in ensuring the sustainable and accountable adoption of AI in public administration. This study contributes to the emerging literature on AI governance and provides practical policy recommendations for governments seeking to balance innovation with accountability and legal certainty.
References
Al-Dulaimi, A. O. M., & Mohammed, M. A. A. W. (2025). Legal responsibility for errors caused by Artificial Intelligence (AI) in the public sector. International Journal of Law and Management. https://doi.org/10.1108/IJLMA-08-2024-0295
Balasubramaniam, N., Kauppinen, M., Hiekkanen, K., & Kujala, S. (2022). Transparency and Explainability of AI Systems: Ethical Guidelines in Practice. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13216 LNCS, 3–18. https://doi.org/10.1007/978-3-030-98464-9_1
Bao, H., Liu, W., & Dai, Z. (2025). Artificial Intelligence vs. public administrators: Public trust, efficiency, and tolerance for errors. Technological Forecasting and Social Change, 215. https://doi.org/10.1016/j.techfore.2025.124102
Bracci, E. (2023). The loopholes of algorithmic public services: an “intelligent” accountability research agenda. Accounting, Auditing and Accountability Journal, 36(2), 739–763. https://doi.org/10.1108/AAAJ-06-2022-5856
Cossette-Lefebvre, H., & Maclure, J. (2023). AI’s fairness problem: understanding wrongful discrimination in the context of automated decision-making. AI and Ethics, 3(4), 1255–1269. https://doi.org/10.1007/s43681-022-00233-w
Cracel Viana, L., Moreira, J. A., & Alves, P. (2022). State audit of public–private partnerships: Effects on transparency, auditor’s roles, and impact on auditee’s reactions. Financial Accountability and Management, 38(4), 633–660. https://doi.org/10.1111/faam.12342
Dirgová Luptáková, I., Pospíchal, J., & Huraj, L. (2024). Beyond Code and Algorithms: Navigating Ethical Complexities in Artificial Intelligence. Lecture Notes in Networks and Systems, 934 LNNS, 316–332. https://doi.org/10.1007/978-3-031-54813-0_30
Fountain, J. E. (2022). The moon, the ghetto and Artificial Intelligence: Reducing systemic racism in computational algorithms. Government Information Quarterly, 39(2). https://doi.org/10.1016/j.giq.2021.101645
Gaie, C., & Mehta, M. (2024). Digital Transformation of Public Services: Introduction, Current Trends and Future Directions. Transforming Public Services, 1–28. https://doi.org/10.1007/978-3-031-55575-6_1
Kitsios, F., Kamariotou, M., & Mavromatis, A. (2023). Drivers and Outcomes of Digital Transformation: The Case of Public Sector Services. Information (Switzerland), 14(1). https://doi.org/10.3390/info14010043
Krafft, T. D., Zweig, K. A., & König, P. D. (2022). How to regulate algorithmic decision-making: A framework of regulatory requirements for different applications. Regulation and Governance, 16(1), 119–136. https://doi.org/10.1111/rego.12369
Kumar Das, D. (2024). Exploring the Symbiotic Relationship between Digital Transformation, Infrastructure, Service Delivery, and Governance for Smart Sustainable Cities. Smart Cities.
Latupeirissa, J. J. P., Dewi, N. L. Y., Prayana, I. K. R., Srikandi, M. B., Ramadiansyah, S. A., & Pramana, I. B. G. A. Y. (2024). Transforming Public Service Delivery: A Comprehensive Review of Digitization Initiatives. Sustainability (Switzerland) , 16(7). https://doi.org/10.3390/su16072818
Magrani, E., & da Silva, P. G. F. (2024). The Ethical and Legal Challenges of Recommender Systems Driven by Artificial Intelligence. Law, Governance and Technology Series, 58, 141–168. https://doi.org/10.1007/978-3-031-41264-6_8
Makanga, C. N., Orobia, L. K., Kaawaase, T. K., Nabeta, I. N., Mindra, R. K., & Munene, J. C. (2025). Oversight mechanisms and public accountability in an emerging economy. Cogent Business and Management, 12(1). https://doi.org/10.1080/23311975.2025.2507216
Memarian, B., & Doleck, T. (2023). Fairness, Accountability, Transparency, and Ethics (FATE) in Artificial Intelligence (AI) and higher education: A systematic review. Computers and Education: Artificial Intelligence, 5. https://doi.org/10.1016/j.caeai.2023.100152
Mora, J. (2025). Efficiency, Fairness, and Discrimination: Algorithms in Public Policy and Their Impact on Social Hierarchies. Palgrave Studies in Digital Inequalities, Part F742, 65–84. https://doi.org/10.1007/978-3-031-83479-0_4
Nastjuk, I., Trang, S., & Papageorgiou, E. I. (2022). Smart cities and smart governance models for future cities. Electronic Markets, 32(4), 1917–1924. https://doi.org/10.1007/s12525-022-00609-0
Natali, C., Marconi, L., Dias Duran, L. D., & Cabitza, F. (2025). AI-induced Deskilling in Medicine: A Mixed-Method Review and Research Agenda for Healthcare and Beyond. Artificial Intelligence Review, 58(11). https://doi.org/10.1007/s10462-025-11352-1
Newman, J., Mintrom, M., & O’Neill, D. (2022). Digital technologies, Artificial Intelligence, and bureaucratic transformation. Futures, 136. https://doi.org/10.1016/j.futures.2021.102886
O’Regan, B., King, R., & Smith, D. (2022). Combining accountability forms: transparency and “intelligent” accountability in a public service organization. Accounting, Auditing and Accountability Journal, 35(5), 1287–1315. https://doi.org/10.1108/AAAJ-03-2020-4473
Osborne, S. P., Cucciniello, M., Nasi, G., & Zhu, E. (2022). Digital transformation, Artificial Intelligence and effective public services: challenges and opportunities. Global Public Policy and Governance, 2(4), 377–380. https://doi.org/10.1007/s43508-022-00058-7
Overman, S., & Schillemans, T. (2022). Toward a Public Administration Theory of Felt Accountability. Public Administration Review, 82(1), 12–22. https://doi.org/10.1111/puar.13417
Radanliev, P. (2025). AI Ethics: Integrating Transparency, Fairness, and Privacy in AI Development. Applied Artificial Intelligence, 39(1). https://doi.org/10.1080/08839514.2025.2463722
Saheb, T., & Saheb, T. (2022). Topical Review of Artificial Intelligence National Policies: A Mixed Method Analysis. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4208097
Sarwar, M. I., Abbas, Q., Alyas, T., Alzahrani, A., Alghamdi, T., & Alsaawy, Y. (2023). Digital Transformation of Public Sector Governance With IT Service Management-A Pilot Study. IEEE Access, 11, 6490–6512. https://doi.org/10.1109/ACCESS.2023.3237550
Setijadi Prihatmanto, A., Andrian, R., Danar Sunindyo, W., & Sutriadi, R. (2024). Transforming Public Services: A Systematic Review of Smart Government Frameworks, Architectures, and Implementation Challenges. IEEE Access, 12, 135799–135810. https://doi.org/10.1109/ACCESS.2024.3450907
Sucupira Furtado, L., da Silva, T. L. C., Ferreira, M. G. F., de Macedo, J. A. F., & de Melo Lima Cavalcanti Moreira, J. K. (2023). A framework for Digital Transformation towards Smart Governance: using big data tools to target SDGs in Ceará, Brazil. Journal of Urban Management, 12(1), 74–87. https://doi.org/10.1016/j.jum.2023.01.003
Yigitcanlar, T., David, A., Li, W., Fookes, C., Bibri, S. E., & Ye, X. (2024). Unlocking Artificial Intelligence Adoption in Local Governments: Best Practice Lessons from Real-World Implementations. Smart Cities, 7(4), 1576–1625. https://doi.org/10.3390/smartcities7040064
Zharova, A. K. (2023). Achieving Algorithmic Transparency and Managing Risks of Data Security when Making Decisions without Human Interference: Legal Approaches. Journal of Digital Technologies and Law, 1(4), 973–993. https://doi.org/10.21202/jdtl.2023.42
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Althe Serafim Kriswandaru
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
6.png)
