Neutrality Bites: Gender Representation in AI-Generated Animal Stories

Large language models frequently sidestep assigning gender to animal characters in stories, but when they do, a pronounced masculine bias emerges, according to a new study that examined 23.8K AI-generated narratives [1]. The research, posted on arXiv, prompted six leading LLMs to complete English-language stories about seven anthropomorphic animal characters with unstated genders across varied settings and model temperatures [1]. On average, 19% of the stories avoided gendering the animal entirely, while 38.2% used gender-neutral language such as “it” or “its” [1]. When gender was assigned, however, the imbalance was stark: 40.6% of stories featured masculine characters, compared with just 2.2% that featured feminine characters [1]. The authors describe this pattern with the phrase “neutrality bites,” arguing that models prioritizing neutrality to address social bias may inadvertently erase marginalized perspectives and identities [1]. Even the model with the highest feminine representation, Claude Sonnet 4.5, assigned feminine gender in only 3.8% of its stories, against 34.3% that were masculine [3]. Feminine characters appeared most often in stories about cats, at 53.2%, an animal stereotypically associated with femininity [3]. Compared with a human study that used a nearly identical story prompt, the LLMs were six times less likely to imagine a feminine character and 1.2 times more likely to use neutral references or the animal’s name [3]. The findings add to a growing body of work on narrative bias in AI. A separate study that examined stories from ChatGPT, Gemini, and Claude found that implicit biases persisted even when explicit gender distributions appeared balanced, and that simply altering character gender counts was insufficient to overcome narrative stereotyping [5]. That analysis, which used Propp’s character classifications and Freytag’s narrative structure, noted that ChatGPT showed a more unbalanced and stereotypical distribution, while Claude’s plots were less prone to gender-based narrative bias but tended toward repetitive crime-genre conventions [5]. The new animal-story study concludes that strategies beyond neutrality are needed — approaches that more equally distribute social possibilities across imagined subjects rather than defaulting to masculine framings or erasing gender altogether [1].