Decentralization and Accountability in Artificial Intelligence Governance
Keywords:
Artificial Intelligence Governance; Accountability; Decentralization; Public Administration.Abstract
The expansion of artificial intelligence (AI) in public governance has intensified debates on accountability, particularly regarding the balance between proactive and reactive mechanisms. Existing frameworks conceptualize accountability as a structured relation of answerability, yet they often assume consolidated institutional capacity and regulatory coherence. This study aims to reinterpret proactive and reactive accountability in AI governance through the lens of Indonesia’s decentralized administrative system. The research employs a qualitative case study design based exclusively on secondary data, including scholarly literature, regulatory documents, and policy reports. Using an analytical framework derived from accountability theory and systems governance perspectives, the study examines how administrative capacity, coordination dynamics, and governance missions condition accountability goals. The analysis focuses on institutional architecture, resource distribution, and inter-organizational oversight structures. Findings indicate that proactive accountability is constrained by uneven bureaucratic capacity and regulatory fragmentation, while reactive enforcement mechanisms gain structural prominence. The study concludes that accountability goals in decentralized AI governance are institutionally conditioned rather than purely normative policy choices. This research contributes to the field by integrating public administration and systems governance theory into AI accountability scholarship, offering a context-sensitive framework applicable to emerging digital states.
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