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Embedded AI evaluators won’t be an easy solution, Hugging Face’s policy chief says
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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.
Key facts
- 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
- 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
- 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 that in mind, can they trust the current tests that are being released
Summary
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. With that in mind, can they trust the current tests that are being released? “Trust is such a weighted term,” said Solaiman. Reproducibility in model evaluations has long remained one of the stickier aspects of benchmarking, and remains a topic of significant debate in the industry.