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Beyond Benchmarks: On The False Promise of AI Regulation
arXiv:2501.15693v2 Announce Type: replace
Abstract: The performance of AI models on safety benchmarks does not indicate their real-world performance after deployment. This opaqueness of AI models impedes existing regulatory frameworks constituted on benchmark performance, leaving them incapable of mitigating ongoing real-world harm. The problem stems from a fundamental challenge in AI interpretability, which seems to be overlooked by regulators and decision makers. We propose a simple, realistic and readily usable regulatory framework which does not rely on benchmarks, and call for interdisciplinary collaboration to find new ways to address this crucial problem.
Abstract: The performance of AI models on safety benchmarks does not indicate their real-world performance after deployment. This opaqueness of AI models impedes existing regulatory frameworks constituted on benchmark performance, leaving them incapable of mitigating ongoing real-world harm. The problem stems from a fundamental challenge in AI interpretability, which seems to be overlooked by regulators and decision makers. We propose a simple, realistic and readily usable regulatory framework which does not rely on benchmarks, and call for interdisciplinary collaboration to find new ways to address this crucial problem.
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