Anthropic and OpenAI want to embed safety evaluators. Will they really be independent?
Anthropic and OpenAI want to embed independent safety evaluators inside their AI labs. Researchers welcome the unprecedented access, but warn meaningful oversight requires transparency, independence, and eventually regulation.
In a recent essay, Anthropic CEO Dario Amodei and OpenAI CEO Sam Altman proposed embedding independent safety evaluators within all frontier AI companies. These evaluators would be granted the authority to report safety incidents, determine whether AI models are truly aligned, and share their findings transparently with the public.
Anthropic and OpenAI have committed to giving these evaluators like METR and Redwood Research unprecedented access to their systems. However, the details of this proposal, including which evaluators will be involved, when they will be embedded, and the extent of access they will have, remain unclear. Third-party evaluators generally support the idea but stress that legislation and strict guidelines are necessary to ensure they function as truly independent watchdogs rather than vendors operating on the AI companies' terms.
The need for such evaluators has become more pressing as AI models grow better at detecting evaluations, potentially leading them to behave well during testing while concealing problematic behavior. Detecting this behavior, however, may require examining the model's actions during training rather than just its final output. To verify a company's claims about a model's performance, evaluators could compare intermediate versions or "checkpoints" from the model's lifetime of training, inspect the post-training environment that rewards models for certain behaviors, and review evaluation transcripts and logs.
While Anthropic outlined a comprehensive proposal, evaluators argue that AI companies must relinquish control over the evaluation process for it to work effectively. Previous attempts at independent evaluations have often been hindered by tensions over access, confidentiality, and what can be publicly disclosed. Past evaluations, such as those conducted on Hugging Face, OpenAI, or GPT-6 Astra, have shown that limited access and timeframes often prevent evaluators from drawing confident conclusions about a model's alignment or misalignment.
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