How Humans and LLMs Read Gender into Gender-Neutral Physical Descriptions
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Computer Science > Computation and Language
arXiv:2609.16366 (cs)
[Submitted on 14 Sep 2026]
Title:How Humans and LLMs Read Gender into Gender-Neutral Physical Descriptions
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Abstract: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. Results show that physical descriptions carry structured and graded gender associations among readers, with more consistent and distinctive associations for women and men than for non-binary identities. Next, we evaluate 16 LLMs across model families, sizes, and post-training variants against human ratings. The models partially recover human associations but exhibit systematic alignment biases, including compressed rating distributions, weaker alignment for associations with men, and asymmetric abstention that disproportionately targets the non-binary category. Finally, we release the best-performing proxy model trained to predict humans' gender associations of descriptive language and demonstrate its utility through a sociolinguistic analysis of character descriptions in LitBank. Together, our findings provide the first empirical evidence that seemingly "objective" physical descriptions can retain systematic gender associations in human interpretation, and uncover systematic patterns of model-human misalignment. This challenges the assumption that replacing explicit gender labels with physical descriptions necessarily yields gender-neutral communication, and highlights downstream challenges in using such descriptions to communicate subjective identity categories in human-AI interaction.
| Comments: | The dataset and code are available at this https URL, and the predictor model is released at this https URL |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC) |
| Cite as: | arXiv:2609.16366 [cs.CL] |
| (or arXiv:2609.16366v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.16366
arXiv-issued DOI via DataCite (pending registration)
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| Journal reference: | In Proceedings of Third Conference on Language Modeling (COLM), 2026 |
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