# Neutrality Bites: Gender Representation in AI-Generated Animal Stories

arXiv: https://arxiv.org/abs/2606.07969 · HTML: https://mlsanity.com/paper/2606.07969/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 46 references that are in the map.
- First posted on arXiv 6 Jun 2026.

## Builds on (most cited first, 20 of 20)

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- [CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities](https://mlsanity.com/paper/2201.06796/) (2022)
- [Gender bias and stereotypes in Large Language Models](https://mlsanity.com/paper/2308.14921/) (2023)
- [Theories of "Gender" in NLP Bias Research](https://mlsanity.com/paper/2205.02526/) (2022)
- [MISGENDERED: Limits of Large Language Models in Understanding Pronouns](https://mlsanity.com/paper/2306.03950/) (2023)
- [F-coref: Fast, Accurate and Easy to Use Coreference Resolution](https://mlsanity.com/paper/2209.04280/) (2022)
- [GenderAlign: An Alignment Dataset for Mitigating Gender Bias in Large Language Models](https://mlsanity.com/paper/2406.13925/) (2024)
- [Biased Tales: Cultural and Topic Bias in Generating Children's Stories](https://mlsanity.com/paper/2509.07908/) (2025)
- [Gender-tuning: Empowering Fine-tuning for Debiasing Pre-trained Language Models](https://mlsanity.com/paper/2307.10522/) (2023)
- [Measuring Bias or Measuring the Task: Understanding the Brittle Nature of LLM Gender Biases](https://mlsanity.com/paper/2509.04373/) (2025)
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- [Measuring Bias in a Ranked List using Term-based Representations](https://mlsanity.com/paper/2403.05975/) (2024)
- [On How Users Edit Computer-Generated Visual Stories](https://mlsanity.com/paper/1902.08327/) (2019)
- ["I Recall the Past": Exploring How People Collaborate with Generative AI to Create Cultural Heritage Narratives](https://mlsanity.com/paper/2501.00359/) (2024)
- [Fire Dragon and Unicorn Princess; Gender Stereotypes and Children's Products in Search Engine Responses](https://mlsanity.com/paper/2206.13747/) (2022)
- [Transforming Dutch: Debiasing Dutch Coreference Resolution Systems for Non-binary Pronouns](https://mlsanity.com/paper/2405.00134/) (2024)
- [Analyzing values about gendered language reform in LLMs' revisions](https://mlsanity.com/paper/2505.21378/) (2025)
- [Mitigation of Gender and Ethnicity Bias in AI-Generated Stories through Model Explanations](https://mlsanity.com/paper/2509.04515/) (2025)

## Abstract (arXiv)

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.