Smart governance and Bureaucratic Accountability: Assessing the Legal Risks of AI-Assisted Public Administration Systems - Althe Serafim Kriswandaru (Universitas Sains dan Teknologi Komputer, 2026)
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.