Scholars warn AI evals lose validity when models grade themselves

Commentators draw on Ken Thompson's 1984 trusting-trust compiler backdoor analogy to argue that AI evaluations are only meaningful when independent. If a model or its derivatives help generate the tests, evidence, or interpretation, a passing result is essentially invalidated.

2026-09-16 ~ 2026-09-16 · 2 related posts

• Thompson's Compiler Attack Explains Why AI Evals Fail When the Model Helps Build Them — vishalmisra · 2026-09-16
• When the Model Grades Itself, "It Passed" Means Less: On Independent AI Evaluation — vishalmisra · 2026-09-16