Post-Deployment Accountability in AI Governance: A Cross-Regulatory Empirical Analysis of AI Incidents
Ummara Mumtaz, Summaya Mumtaz
Why It Matters
What makes this one worth your time
Understanding and improving AI governance is crucial for ensuring accountability and compliance in high-stakes AI deployments, which is essential for both developers and regulators.
The paper identifies governance gaps in AI incident accountability and proposes a proactive compliance framework.
Summary
The paper conducts an empirical analysis of 480 AI incidents to evaluate their compliance with major governance frameworks and identifies gaps in post-deployment accountability. It proposes a new governance framework, the Proactive AI Governance Compliance Framework (PAGCF), aimed at ensuring pre-deployment compliance through a structured lifecycle methodology.
Key contributions
- Empirical analysis of 480 AI incidents against major governance frameworks.
- Identification of governance gaps in post-deployment accountability.
- Proposal of the Proactive AI Governance Compliance Framework (PAGCF) for pre-deployment compliance.
Notable insights
- The study uses a cross-regulatory approach to identify governance gaps in AI incident accountability.
- The proposed framework emphasizes a shift from reactive to proactive governance, focusing on pre-deployment compliance.
Possible limitations
- Not stated in the abstract
Abstract
arXiv:2605.16281v2 Announce Type: replace-cross Abstract: Post-deployment accountability has become central to AI governance, yet little empirical evidence shows whether monitoring, incident reporting, and impact assessment obligations are visible when AI systems fail. This study analyzes real-world AI incidents from the AI Incident Database (2020--2026) and codes them against nine post-deployment provisions from the EU AI Act, the NIST AI Risk Management Framework, and the GDPR. The findings show substantial accountability gaps: 77.1\% of incidents lack evidence of EU AI Act post-market monitoring, and 99.6\% lack documented Data-Protection Impact Assessment evidence. Governance gaps are also systemic, with 9.8\% of incidents simultaneously non-compliant under two or more regimes. Incidents detected through internal monitoring show much higher compliance than externally detected incidents (87.5\% vs 5.3\% under the EU AI Act; 95.8\% vs 58.1\% under NIST), suggesting that monitoring capacity is a key condition for effective post-deployment governance. Building on these findings, the paper proposes the Proactive AI Governance Compliance Framework (PAGCF), a four-phase lifecycle for pre-deployment assessment, continuous monitoring, incident preparedness, and cross-framework verification.