Neutrality Bites: Gender Representation in AI-Generated Animal Stories

2026-06-06

AI Summary

This study investigates how large language models implicitly assign gender when generating anthropomorphic animal stories without explicit gender specifications, revealing that ostensibly neutral strategies may mask a systemic erasure of marginalized gender identities. By prompting six mainstream models to produce 23.8K stories and combining controlled experiments with large-scale textual analysis, the research finds that models avoid gender assignment in 19% of cases and employ gender-neutral pronouns in 38.2%. Among instances with explicit gender assignment, male characters constitute 40.6% while female characters account for only 2.2%. Challenging the assumption that neutrality equates to fairness, this work argues for moving beyond passive neutrality toward actively equitable representation of gendered social possibilities, offering a novel intervention pathway to mitigate implicit bias in generative AI.

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.

Problem

• gender bias
• AI-generated stories
• neutrality
• large language models
• gender representation

Innovation

• gender bias
• large language models
• gender neutrality
• anthropomorphic narratives
• algorithmic fairness