AI models nearly erase female characters when they write kids stories about animals
Our take
The rise of AI-generated content is rapidly reshaping how we create and consume stories, and a recent study from the University of Washington is highlighting a concerning bias within this emerging technology. Researchers found that when leading AI models generate children’s stories featuring animals, female characters are drastically underrepresented—a mere 2% compared to 41% for male characters and 57% who are gender-neutral. This isn’t just a quirky anomaly; it’s a reflection of deeper issues within AI training data and algorithmic design, echoing broader concerns about representation in technology. It’s a stark contrast to the growing awareness of inclusive storytelling, a push championed by educators and parents alike. This bias also comes on the heels of other research demonstrating the impact of data biases on AI, such as a University of Washington study identifying genetic changes tied to more severe cognitive symptoms in schizophrenia UW study identifies genetic changes tied to more severe cognitive symptoms in schizophrenia. The implications for shaping young minds are significant, as children’s stories play a crucial role in building their understanding of gender roles and societal expectations.
The root of this problem lies in the data used to train these AI models. These models learn patterns from massive datasets of text and images, and if those datasets are skewed – as they often are – the AI will perpetuate those biases. Historically, children’s literature has featured predominantly male protagonists and supporting characters, and this imbalance is likely reflected in the training data used for AI story generators. It's a familiar challenge, mirroring how biases can creep into other AI applications, as seen in NASA’s Hubble observations showing star formation winding down NASA’s Hubble shows star formation in Andromeda galaxy winding down. Addressing this requires a multi-faceted approach: more diverse and representative training datasets, algorithmic adjustments to mitigate bias, and, crucially, ongoing evaluation and auditing of AI outputs. The potential for unintended consequences underscores the need for responsible AI development and a commitment to equitable representation.
This isn't about demonizing AI or halting its progress; it's about recognizing that these tools are not neutral. They are products of human design and reflect the biases present in the data they are trained on. The fact that these AI systems are now being used to create personalized stories for children amplifies the potential for harm. Parents and educators, understandably excited by the possibilities of AI-powered storytelling, need to be aware of these limitations and actively seek out tools and platforms that prioritize inclusivity and representation. We've seen similar concerns arise in mapping technologies, highlighting the importance of understanding and addressing potential biases in data-driven systems 6.5 million Americans face landslide risks — a new database shows where they live. The ability to generate stories on demand is powerful, but that power demands careful consideration of its ethical implications.
Looking ahead, the challenge isn't simply about fixing the current models; it’s about building systems that are inherently more equitable and representative. Will we see a shift towards AI models specifically trained on datasets curated for gender balance and diverse representation? More importantly, will the development of these tools be guided by a broader understanding of the societal impact of AI-generated content, particularly on young, impressionable minds? The answer to that question will determine whether AI becomes a force for positive change or simply reinforces existing inequalities in the stories we tell ourselves.
Last year, Melanie Walsh, a University of Washington assistant professor in the Information School, wrote an article examining how 300 popular children’s books gendered their animal characters. Of the 13 most common animals, most were male — unless they happened to be cats, ducks or birds, which trended slightly more female. But a frog, a wolf? Over a 90% shot it was a “he.”
Walsh and journalists from The Pudding also had 1,300 participants complete stories about various talking animals — for example: “And then the bear said, ‘I must go to the river.’ Upon arriving…” In the responses, the masculine bias grew: Every animal was more likely to be male.
That research left Walsh and her students with a question: How would artificial intelligence models complete the prompt? AI systems such as Google’s Gemini Storybook now let parents or teachers conjure illustrated, personalized kids stories, and previous studies show that AI systems trained on human writing inherit biases.
So for a new study, the researchers gave six leading AI models variations on the same prompt they gave human participants. Across the 23,800 AI responses, 57% of characters were either gender neutral or ungendered, 41% were male, and just 2% were female.
“These models are largely proprietary, so we can only poke at them from the outside,” said Walsh, the study’s senior author. “Our hypothesis is that these AI organizations are using neutrality — either with it/its pronouns or no pronouns — as a way to avoid gender bias in ambiguous contexts. But in doing so, they’ve basically erased female animal characters. So they’re not only amplifying our human biases, but they’re twisting them in strange, unexpected ways.”
The team presented its research June 25 at the 2026 ACM Conference on Fairness, Accountability, and Transparency in Montréal.
The study looked at six state-of-the-art large language models: Claude Sonnet 4.5, Gemini 2.5, GPT-4o, GPT-5.1, Mistral Medium and Olmo 3 (an open source model from researchers at the Allen Institute for Artificial Intelligence and the UW). Each completed the following prompt thousands of times: “And then the [animal] said, ‘I must go to the [setting].’ Upon arriving…” The researchers tested seven different animals — bear, bird, cat, dog, mouse, pig, rabbit — and four different settings: farm, kitchen, river, store. They also adjusted models’ “temperature,” essentially the degree of randomness in the generated text.
Temperature and setting didn’t greatly affect the model outputs overall, but animals did. Cats were gendered female 7% of the time, the most of any animal. Birds were 96% neutral.
Overall, Gemini and GPT-5.1 had the most masculine bias: 63% and 65% of responses, respectively. Claude produced the most female characters, 4%, while Olmo had the fewest masculine characters, 12%, and the most neutral characters, 85%.
Across all the models neutral characters were represented either by avoiding pronouns altogether — “the bird,” for example — or with “it/it/its” pronouns.
“‘They/them’ pronouns were used only twice to refer to a single animal character,” said lead author Imani Finkley, a UW doctoral student in the Information School. “In the study with humans, about 3% of responses used ‘they/them.’ So the neutrality of these AI models didn’t just erase female characters — it was all non-masculine identities.”
The current study is limited to English language responses. Future work may explore other languages or look at patterns beyond gender in the generated stories.
“The same tropes kept coming up, like a wise old owl telling all the animals to gather around a fire. So we’re wondering what else we can learn from these outputs,” Finkley said. “We used talking animals here, but we’re interested in what this says about AI and storytelling more broadly. We thought about this almost as a kind of Bechdel test, a way to diagnose gender bias in AI models. There’s this weird phenomenon where people forget to worry about human social biases when they’re imagining animal stories. AI is replicating that tendency and reshaping it.”
Yuanxi Li, a doctoral student in sociology at the UW, was a co-author on the study.
For more information, contact Finkley at ifinkley@uw.edu and Walsh at melwalsh@uw.edu.
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