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Policy
Embedded AI evaluators won’t be an easy solution, Hugging Face’s policy chief says
The idea of embedded evaluators, in which supposedly "neutral" third-party observers are placed inside AI organizations, has been popularized over the last several months, as AI labs grapple with a rash of safety incidents that have spurred concerns over the potential harms of their technology.
AI labs have staked their reputations on model benchmarking, the supposedly neutral and accurate system of evaluations that are designed to measure whether one AI product is better than another.
However, the methods behind these evaluations are complicated and still maturing, said Irene Solaiman, Hugging Face’s chief policy officer, during a discussion at TechCrunch Disrupt last week. Testing needs to come together into a more consistent science if the industry is to rely on them in the future.
With that in mind, can we trust the current tests that are being released?
“Trust is such a weighted term,” said Solaiman. “In terms of whether we can trust results, one big part [of that] is can you reproduce the results of an evaluation? Do you have the level of access that you need to the model itself, this is where open weights come in in a big way,” she added.
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