How Humans and LLMs Read Gender into"Gender-Neutral"Physical Descriptions
When foundation models describe people, recent work in AI fairness, accessibility, and ethics recommends avoiding inferred identity labels (e.g.,"she","his") in favor of seemingly"objective"physical descriptions (e.g.,"short hair","a defined jawline"). Yet whether such descriptive language achieves gender-neutral commu...
Yingjia Wan, Lin L. Lin, Elisa Kreiss
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