Proxy-Based Evaluation and the Limits of Assurance in AI Governance

Artificial intelligence (AI) governance frameworks increasingly rely on proxy-based evaluation mechanisms, including performance metrics, benchmarks, transparency documentation, audit procedures, and robustness tools, to operationalize accountability and trust. While these proxies enable scalable oversight, they can also be mistaken for evidence that the underlying governance goal has been achieved. This Policy Letter synthesizes recent literature from AI governance, auditing, robustness, and deployment studies to examine how proxy satisfaction can diverge from operational performance in deployed systems. To make this mismatch concrete, the paper develops explicit proxy-goal mappings and discusses two illustrative examples: transparency documentation as a proxy for accountable understanding, and pre-deployment validation as a proxy for sustained operational performance. The analysis shows that even formally satisfied evaluation mechanisms can still leave performance degradation, hidden harms, distribution shifts, and deployment-time failure modes insufficiently examined. Rather than proposing a new metric or regulatory framework, the paper offers a governance-focused interpretation of the existing literature that clarifies why assurance claims should distinguish between proxy and deployment evidence.