From Reactive to Proactive: A Multi-Regulatory Empirical Analysis of 480 AI Incidents and a Data-Driven Governance Compliance Framework
Artificial intelligence systems are increasingly deployed in high-stakes domains, yet it remains unclear whether existing governance frameworks ensure accountability after deployment. This study makes two contributions. First, it presents a cross-regulatory empirical analysis of 480 real-world AI incidents from the AI Incident Database (AIID), evaluating their alignment with post-deployment provisions in three major governance frameworks: the EU AI Act (Articles 72-73), the NIST AI Risk Manageme
Record details
Published: 10 April 2026
Source: arXiv
Category: Research
Topics: Regulation · Safety & alignment · Transparency
Retrieved: 14 July 2026
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How to cite this record
ethics.ai (10 April 2026), “From Reactive to Proactive: A Multi-Regulatory Empirical Analysis of 480 AI Incidents and a Data-Driven Governance Compliance Framework,” evidence record 6051, https://ethics.ai/record/6051 (originally published by arXiv).
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