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Study finds method to prevent AI models from gaming safety tests
Researchers from MATS, Redwood Research, Oxford, and Anthropic publish a study identifying methods to detect and prevent AI models from deliberately underperforming on safety evaluations.
A study by researchers from the MATS program, Redwood Research, the University of Oxford, and Anthropic examines a safety problem that grows more pressing as AI systems become more capable: "sandbagging," where a model deliberately hides its true abilities and delivers work that looks adequate but is intentionally subpar.
The article Researchers may have found a way to stop AI models from intentionally playing dumb during safety evaluations appeared first on The Decoder.
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