Safety for Whom? Refusing the Right Subset of a Topic, Not the Whole Topic
Regulators and companies increasingly disagree on whether blocking narrow use cases counts as genuine safety, or merely shifts risks elsewhere.
Most AI safety work treats harmful content as entire topics to refuse, like all political content or all weapons discussions. However, real-world deployments need finer-grained control, refusing only harmful subsets within a topic while still answering legitimate questions about the same subject. This research studies how to train models to refuse specific dangerous requests, such as political manipulation, while continuing to answer factual political questions. The work reveals that standard safety training methods often create a problematic tradeoff, where reducing harmful responses unintentionally increases refusals of safe prompts, a problem that can only be detected by measuring both sides of the safety boundary.
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Nvidia CEO Jensen Huang resigns amid spy, affair scandals
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Also reported by TechCrunch and Wired AI
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Also reported by TechCrunch, Wired AI and Data Center Knowledge