AI RESEARCH
Safe for Whom? Rethinking How We Evaluate the Safety of LLMs for Real Users
arXiv CS.AI
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ArXi:2512.10687v2 Announce Type: replace Safety evaluations of large language models (LLMs) typically focus on universal risks like dangerous capabilities or undesirable propensities. However, millions use LLMs for personal advice on high-stakes topics like finance and health, where harms are context-dependent rather than universal. While frameworks like the OECD's AI classification recognize the need to assess individual risks, user-welfare safety evaluations remain underdeveloped.