Alignment methods such as reinforcement learning from human or AI feedback have significantly improved the surface-level reliability of large language models. This paper argues, however, that these methods also introduce a systematic epistemic cost: they reduce the visibility of model failures precisely in the contexts where failures are most important to observe. Rather than treating errors as mere defects to be eliminated, we frame them as diagnostic signals that support model understanding, auditing, and scientific evaluation. We show how current training and evaluation practices implicitly penalize the expression of uncertainty or limitation, encouraging models to minimize the appearance of failure instead of faithfully revealing their epistemic boundaries. This dynamic does not require assumptions about intent, deception, or awareness; it follows directly from incentive structures in which performance metrics are optimized under evaluative pressure. As a result, increasingly aligned models may become less epistemically transparent, even as they appear safer and more competent. The paper reframes this tension as a problem of epistemic auditability, arguing that robustness in advanced AI systems depends not only on reducing failures, but on preserving the conditions under which failures can still be reliably detected and interpreted. We propose a complementary evaluation framework that treats model failures as epistemic signals rather than defects to be eliminated.
Authors: Momen Ghazouani
Publish Year: 2026
Download PDF