A bear is almost certainly a “he.” A bird is usually an “it.” And a female wolf? She practically doesn’t exist. When artificial intelligence writes children’s stories about animals, female characters almost entirely vanish.
A new University of Washington study testing leading AI models across nearly 24,000 story completions found that AI guardrails designed to reduce bias have accidentally erased female characters — defaulting overwhelmingly to male animals or ungendered “it” pronouns.
The work builds on earlier research led by Melanie Walsh, an assistant professor at the UW Information School, who last year analyzed 300 popular children’s picture books alongside journalists from The Pudding. That study revealed a distinct masculine bias in traditional publishing: out of 13 common animal tropes, most default to male — unless the character happens to be a cat, duck, or bird.
When Walsh’s team gave 1,300 human participants simple sentence completion prompts like, “And then the bear said, ‘I must go to the river.’ Upon arriving…,” human readers leaned even further into male pronouns for every single animal tested.
To test how modern AI models handle the exact same creative prompts — the kind consumer tools like Google’s Gemini Storybook use to generate kids’ tales — researchers ran variations of those sentence completion tasks across six leading AI models, including GPT-5.1, Gemini 2.5, Claude Sonnet 4.5, and Olmo 3 (an open source model from researchers at Seattle’s Allen Institute for Artificial Intelligence and the UW).
Across 23,800 AI responses, instead of matching human biases or balancing representation, the models took a sharp turn into extreme gender neutrality: 57% of generated characters were assigned neutral or ungendered pronouns like “it,” male characters made up 41%, and female characters dropped to a stark 2%.
The research team presented its findings on June 25 at the 2026 ACM Conference on Fairness, Accountability, and Transparency in Montréal. Led by UW Information School doctoral student Imani Finkley alongside Walsh and sociology doctoral student Yuanxi Li, the paper highlights how alignment guardrails designed to eliminate gender bias can backfire.
“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,” Walsh told UW News. “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 gap between individual models was stark.
While Olmo 3 leaned heavily into neutral framing (85% of responses), Google’s Gemini 2.5 and OpenAI’s GPT-5.1 produced masculine characters in 63% and 65% of stories, respectively. Anthropic’s Claude Sonnet 4.5 generated the highest proportion of female characters, though that figure still maxed out at just 4%.
Specific animals also triggered distinct patterns: cats were assigned female pronouns 7% of the time — the highest of any creature — while birds defaulted to neutral language in 96% of responses.
The push toward neutrality did not translate into inclusive human language, according to the UW. Across thousands of generations, singular “they/them” pronouns appeared only twice — compared to roughly 3% in human-written responses. Instead, models defaulted to “it/its” or avoided pronouns altogether.
“The neutrality of these AI models didn’t just erase female characters,” Finkley said. “It was all non-masculine identities.”
The researchers told UW News they view the experiment as a diagnostic tool — a kind of “Bechdel test” for evaluating how AI models handle gender representation in storytelling. Moving forward, the team may expand beyond English-language prompts and analyze other narrative patterns, such as the recurring character tropes that surfaced throughout the generated text.
“There’s this weird phenomenon where people forget to worry about human social biases when they’re imagining animal stories,” Finkley said. “AI is replicating that tendency and reshaping it.”
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