The AI Evaluability Gap: The Missing Layer for Managing Risk and Sustaining Value
Organizations deploying AI face two fundamental governance challenges: managing AI risk and sustaining AI value. Both depend on evidence whose sufficiency cannot be taken for granted. We call the shared underlying challenge the AI Evaluability Gap: the condition in which organizations lack sufficient evidence to support high-confidence governance decisions regarding either risk or value. We argue that this gap reflects a category error in current practice. Existing governance approaches focus primarily on properties of systems, such as safety, fairness, reliability, compliance, and value, while paying comparatively little attention to the evidentiary foundations required to justify decisi...
| Authors | Vishal Srivastava, Tanmay Sah |
|---|---|
| Primary category | cs.AI |
| Published | 19 June 2026 |
| Last updated at source | 19 June 2026 |
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