How Humans and LLMs Read Gender into "Gender-Neutral" Physical Descriptions

Yingjia Wan, Lin Lin, Elisa Kreiss

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 seemingl…