Summary of Safetywashing: Do Ai Safety Benchmarks Actually Measure Safety Progress?, by Richard Ren et al.
Safetywashing: Do AI Safety Benchmarks Actually Measure Safety Progress?
by 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
First submitted to arxiv on: 31 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A comprehensive meta-analysis is conducted on AI safety benchmarks, analyzing their correlation with general capabilities across dozens of models. The study reveals that many safety benchmarks highly correlate with both upstream model capabilities and training compute, potentially enabling “safetywashing” where capability improvements are misrepresented as safety advancements. To address this issue, the researchers 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. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial intelligence (AI) is getting smarter, but we need to make sure it’s safe too! The problem is that there isn’t a clear way to measure AI safety. This makes it hard for researchers to know how to make progress on this issue. To help solve this problem, scientists looked at how different ways of measuring AI safety are related to each other and to the capabilities of the AI systems themselves. They found that many of these measures are connected in a way that could be misleading. For example, some measures might seem like they’re measuring AI safety, but really they’re just measuring how smart the AI is. This could lead people to think that making AI safer is just a matter of making it smarter. But the scientists say that’s not enough. We need to come up with new ways to measure AI safety that are more accurate and clear. |
Keywords
» Artificial intelligence » Machine learning
