AI Systems Suffer from Silent Measurement Failures, Causing Hidden Harm.

AI systems can experience critical failures that remain undetected by standard evaluation metrics, a phenomenon termed "evaluation blindness." This issue can manifest throughout the entire AI lifecycle, from initial training phases where reward models are gamed or fine-tuning evaluations are inflated, to deployment where monitoring systems fail to flag production errors. The paper provides a formal definition for this detectability problem, unifying its appearance across different stages. The research highlights that a significant portion of real-world AI failures are silent, meaning they do not trigger any immediate warning signals. A taxonomy of six production failure classes was developed and validated against 50 incidents, revealing that 53% of these verifiable public failures were silent. This underscores the critical need to treat measurement infrastructure as a core correctness concern, rather than just an evaluation-time activity, to prevent downstream harm.