Authors: Richard Ren, Steven Basart, Adam Khoja, Alice Gatti, Long Phan, Xuwang Yin, Mantas Mazeika, Alexander Pan, Gabriel Mukobi, Ryan H. Kim, Stephen Fitz, Dan Hendrycks
Abstract: As artificial intelligence systems grow more powerful, there has been
increasing interest in “AI safety” research to address emerging and future
risks. However, the field of AI safety remains poorly defined and
inconsistently measured, leading to confusion about how researchers can
contribute. This lack of clarity is compounded by the unclear relationship
between AI safety benchmarks and upstream general capabilities (e.g., general
knowledge and reasoning). To address these issues, we conduct a comprehensive
meta-analysis of AI safety benchmarks, empirically analyzing their correlation
with general capabilities across dozens of models and providing a survey of
existing directions in AI safety. Our findings reveal that many safety
benchmarks highly correlate with upstream model capabilities, potentially
enabling “safetywashing” — where capability improvements are misrepresented as
safety advancements. Based on these findings, we propose an empirical
foundation for developing more meaningful safety metrics and define AI safety
in a machine learning research context as a set of clearly delineated research
goals that are empirically separable from generic capabilities advancements. In
doing so, we aim to provide a more rigorous framework for AI safety research,
advancing the science of safety evaluations and clarifying the path towards
measurable progress.
Source: http://arxiv.org/abs/2407.21792v1
