🤖 AI Summary
This study uncovers a paradox wherein intensified AI regulation weakens organizations’ substantive control over their AI systems, proposing the “governance inversion” hypothesis. Drawing on institutional theory, organizational governance, and AI accountability literature, it develops a conceptual model that identifies four interrelated mechanisms—fragmentation of authority, expansion of symbolic governance, externalization of control, and paralysis of authority—through which regulatory intensification fosters formalistic compliance at the expense of operational control. By introducing the novel concept of “governance inversion,” this work extends institutional decoupling theory and challenges the prevailing assumption that more regulation inherently yields stronger control. It offers critical theoretical insights for rethinking the design and implementation of AI governance frameworks in practice.
📝 Abstract
This paper introduces the Governance Inversion Hypothesis (GIH) to explain a growing paradox in artificial intelligence (AI) governance: under conditions of increasing regulatory expansion and technological complexity, organisations may become more formally governed while simultaneously experiencing a decline in operational control over AI systems. Existing AI governance frameworks generally assume that stronger regulation improves accountability, oversight, and organisational control. This paper challenges that assumption by arguing that governance formalisation itself may contribute to the erosion of control in AI-intensive environments. Drawing on institutional theory, organisational governance research, accountability scholarship, and emerging AI governance literature, the paper develops a conceptual framework explaining how regulatory expansion may weaken operational authority through four interconnected mechanisms: authority fragmentation, symbolic governance expansion, externalisation of control, and authority paralysis. As governance systems become increasingly layered and procedurally dense, organisations may struggle to maintain coherent authority, technical visibility, escalation capability, and meaningful intervention power over opaque and externally mediated AI infrastructures. The paper extends institutional decoupling theory by introducing governance inversion as a structural condition in which governance expansion may actively undermine operational coherence rather than strengthen it. It concludes that the central risk in AI governance may not be the absence of governance structures, but the emergence of institutions that appear increasingly governed while progressively losing the capacity to govern effectively.