Melanie Walsh, Yuanxi Li, Imani Finkley
We lifted 1 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| imanif/animal-stories | canonical | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| build_prompt_grid | Ran | imanif/animal-stories/generation.py pointer only (licence: NONE) · get_code("8f3c05b050ea7a18") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Gender bias in AI-generated stories is a well-documented problem. While much attention has been paid to reducing or mitigating this bias, it is not always clear whether interventions produce genuinely fairer results. To investigate this issue, we examine how large language models (LLMs) handle gender assignment in a narrative context that is popular, highly ambiguous, and also known to closely reproduce human stereotypes: stories about talking animals. We prompt six leading LLMs to complete an English-language story about seven different anthropomorphic animal characters whose gender is unstated. We additionally iterate with four different narrative settings and a range of model temperatures. Across the 23.8K stories, we find that models frequently avoid gendering the animal character in the story (19% on average) or use gender-neutral language like "it" or "its" (38.2% on average). However, when gender is assigned, there is a significant masculine bias. Feminine animal characters are virtually absent, present in just 2.2% of stories vs. 40.6% that feature masculine characters. Our findings point to a broader argument: neutrality bites. In other words, models that prioritize neutrality to address social bias may actually contribute to the erasure of marginalized perspectives and identities. We suggest that alternative strategies beyond neutrality need to be pursued, such as ones that more equally distribute social possibilities across imagined subjects. CCS Concepts: • Social and professional topics → Gender; • Information systems → Content analysis and feature selection; • Applied computing → Arts and humanities; • Computing methodologies → Natural language generation.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2606.07969")
get_code_for_paper("2606.07969")
have("2606.07969")
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