Why AI Systems Still Fail After Audit: The Governance Gap
This article discusses the limitations of AI governance frameworks that focus solely on assessment and auditing, without enforcing real-time control and accountability over system behavior.
Why it matters
This article highlights a critical limitation in current AI governance frameworks that focus too narrowly on assessment rather than real-time control and accountability.
Key Points
- 1Assessment alone does not create control over AI systems
- 2Detecting policy violations, data issues, and compliance gaps does not automatically change system behavior
- 3There is a structural gap between assessment and enforcement, leading to
- 4
- 5Effective governance requires decision boundaries, escalation triggers, stop authority, and accountability
Details
The article argues that modern AI governance is often framed as an assessment problem - identifying risks, mapping to regulations, and generating scores to create visibility. However, this approach does not actually create control over the system. Even if an AI system is known to be misaligned, it is often still allowed to continue operating. The author calls this the
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