How Humans and LLMs Read Gender into “Gender-Neutral” Physical Descriptions

Abstract

When foundation models describe people, a growing body of work in AI fairness, accessibility, and ethics recommends avoiding inferred identity labels in favor of seemingly ''objective'' physical descriptions (e.g., ''short hair,'' ''a defined jawline''). Yet whether such descriptive language actually 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 and domains, paired with 14,706 gender-association ratings from 304 US-based annotators. Results of human annotations show that physical descriptions carry automatic, structured, and graded gender associations among human readers: 53\% of attributes are significantly more associated with one gender than the others, with more consistent and distinctive associations for women and men than for non-binary identities. Next, we evaluate 16 large language models (LLMs) across model families, sizes, and post-training variants against human ratings. Models partially recover human associations but show systematic alignment biases, including compressed rating distributions, weaker alignment for associations with the men category, and asymmetric abstention disproportionately targeting non-binary categories, which escalated among instruction-tuned and proprietary models. Unlike existing gender-bias benchmarks, GAPA evaluates model alignment with human judgments of gender associations, revealing an underexplored dimension of model behavior: models that appear strong on conventional bias evaluations, particularly instruction-tuned models, can still show substantial misalignment with human associations. Finally, we release a proxy predictor model for predicting humans' gender associations of descriptive language at scale and demonstrate its utility through a sociolinguistic analysis of character descriptions in LitBank. Together, our findings challenge the assumption that replacing explicit gender labels with physical descriptions necessarily yields gender-neutral language, showing that such descriptions can retain systematic gender associations in human interpretation and introduce distinct challenges for model--human alignment, which may cause the current description-based practice to be structurally biased against more vulnerable groups like non-binary people. The dataset and code are available https://github.com/Yingjia-Wan/GAPA, and the predictor at https://huggingface.co/alisa-yingjia-wan/gapa-predictor-olmo2-7b.