SOURCE-LINKED INTELLIGENCE
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 communication remains an open empirical question. To study this, we introduce GAPA (Gender Associations of Physical Attributes), a dataset of 316 common physical attributes drawn from diverse sources, paired with 14,706 gender-association ratings from 304 US-based annotators. Re
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-14T21:29:15.000Z
First collected: 2026-09-20T09:01:24.920Z. This is not the publication date.