2026-08-06 ワシントン大学（UW）
＜関連情報＞
- https://www.washington.edu/news/2026/08/06/ai-bias-kids-stories/
- https://dl.acm.org/doi/10.1145/3805689.3812287
中立性の弊害：AI生成動物物語におけるジェンダー表現 Neutrality Bites: Gender Representation in AI-Generated Animal Stories
Imani Finkley, Yuanxi Li, Melanie Walsh
FAccT ’26: The 2026 ACM Conference on Fairness, Accountability, and Transparency Published: 25 June 2026
DOI:https://doi.org/10.1145/3805689.3812287
Abstract
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.
