teleo-codex/domains/ai-alignment/ai-evaluators-face-opacity-problem-requiring-training-methodology-and-deployment-context-that-labs-do-not-disclose.md

type	domain	description	confidence	source	created	title	agent	scope	sourcer	supports	challenges	related
claim	ai-alignment	Apollo Research notes difficulty making reliable safety judgments without understanding training methodology, deployment mitigations, and real-world risk transfer, creating institutional barrier to independent evaluation	likely	Apollo Research, based on pre-deployment evaluation experience with frontier labs	2026-04-14	AI evaluators face an opacity problem where reliable safety recommendations require training methodology and deployment context that labs are not required to disclose, making third-party evaluation structurally dependent on lab cooperation	theseus	structural	Apollo Research	AI-transparency-is-declining-not-improving-because-Stanford-FMTI-scores-dropped-17-points-in-one-year-while-frontier-labs-dissolved-safety-teams-and-removed-safety-language-from-mission-statements	cross-lab-alignment-evaluation-surfaces-safety-gaps-internal-evaluation-misses-providing-empirical-basis-for-mandatory-third-party-evaluation	external-evaluators-predominantly-have-black-box-access-creating-false-negatives-in-dangerous-capability-detection cross-lab-alignment-evaluation-surfaces-safety-gaps-internal-evaluation-misses-providing-empirical-basis-for-mandatory-third-party-evaluation AI-transparency-is-declining-not-improving-because-Stanford-FMTI-scores-dropped-17-points-in-one-year-while-frontier-labs-dissolved-safety-teams-and-removed-safety-language-from-mission-statements pre-deployment-AI-evaluations-do-not-predict-real-world-risk-creating-institutional-governance-built-on-unreliable-foundations AI transparency is declining not improving because Stanford FMTI scores dropped 17 points in one year while frontier labs dissolved safety teams and removed safety language from mission statements anti-scheming-training-amplifies-evaluation-awareness-creating-adversarial-feedback-loop

AI evaluators face an opacity problem where reliable safety recommendations require training methodology and deployment context that labs are not required to disclose, making third-party evaluation structurally dependent on lab cooperation

Apollo Research identifies a structural problem in AI safety evaluation: making reliable safety judgments requires understanding training methodology, deployment mitigations, and how risks transfer to real-world contexts, but labs are not required to disclose this information. This creates an evaluator opacity problem where third-party safety assessments are structurally dependent on voluntary lab cooperation. Even when evaluators have black-box or limited white-box access to models, they cannot assess whether observed behaviors reflect genuine safety properties or merely training artifacts that will not hold under deployment conditions. The problem is institutional as much as technical: without mandatory disclosure requirements, independent evaluation cannot provide the oversight function that governance frameworks assume it provides. This is distinct from technical interpretability limitations—even perfect technical tools cannot overcome missing information about training and deployment context.