Last year, , a �������� assistant professor in the Information School, wrote 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 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 , 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 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: , , , , and (an open source model from researchers at the 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’ “,” 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 , 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 , 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.”

, 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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Seattle Public Library, or SPL, is the only U.S. library system that makes its anonymized, granular checkout data public. Want to find out how many times people borrowed the e-book version of Toni Morrison’s “Beloved” in May 2018? That data is available.��

The hitch is that the library’s data set contains nearly 50 million rows, and a single title can appear variously. Morrison’s “Beloved,” for instance, is listed as “Beloved,” “Beloved (unabridged),” “Beloved : a novel / by Toni Morrison” and so on.��

To track trends in the catalogue over the last 20 years, �������� researchers analyzed the checkout data of the 93 authors included in the post-1945 volume of “The Norton Anthology of American Literature.” It’s assigned in U.S. English classes more than virtually any other anthology, so what’s thought of as the contemporary American — the books and writers we’ve deemed culturally important.��

The team found that among these vaunted writers — including Morrison, Viet Thanh Nguyen, David Foster Wallace and Joan Didion — science fiction was particularly popular. Ursula K. Le Guin and Octavia E. Butler topped the list.��

The team Nov. 21 in Computational Humanities Research 2025, and created .��

“It’s kind of mind-boggling and ironic that in this age of abundant data, we have so little data about what people are reading,” said senior author , a UW assistant professor in the Information School. “, particularly for researchers, so I’ve been obsessed with SPL’s data for years now. But extracting insights from it is actually a really hard computational and bibliographic modeling problem.”

To organize the data, the team used computational methods, such as stripping away subtitles and standardizing punctuation. They also manually identified things like translations of a work.��

“We worked with the Norton anthology in part because it’s a small enough scale for us to handle,” said lead author , a UW doctoral student in the Information School. “It allows us to have a ground truth to work off of. We can still put a human eye on things.” 

In all the team looked at 1,603 works by the 93 authors, which were checked out a total of 980,620 times since 2005.

The 10 top authors were:

1. Ursula K. Le Guin
2. Octavia E. Butler
3. Louise Erdrich
4. N.K. Jemisin
5. Toni Morrison
6. Kurt Vonnegut
7. George Saunders
8. Philip K. Dick
9. Sherman Alexie
10. James Baldwin

The 10 top books were: 

1. “Parable of the Sower” by Octavia E. Butler
2. “Lincoln in the Bardo” by George Saunders
3. “The Fifth Season” by N.K. Jemisin
4. “The Sympathizer” by Viet Thanh Nguyen
5. “Kindred” by Octavia E. Butler
6. “Beloved” by Toni Morrison
7. “The Left Hand of Darkness” by Ursula K. Le Guin
8. “The Absolutely True Diary of a Part-Time Indian” by Sherman Alexie
9. “The Year of Magical Thinking” by Joan Didion
10. “The Sentence” by Louise Erdrich

Researchers noted several trends that may have driven checkouts. In general, books with genre and sci-fi elements were some of the most popular.��

“I found the prevalence of sci-fi books and writers really interesting,” Gupta said. “These are recent additions to the anthology, since sci-fi and genre fiction haven’t always been seen as important literature. So while it’s a bit unsurprising, it’s also striking to see that despite comprising a small portion of the anthology, these are the authors people are actually reading the most.”

News events also drove spikes in readership, such as film adaptations of James Baldwin’s “If Beale Street Could Talk” and Don DeLillo’s “White Noise,” or the deaths of authors such as Didion, Wallace, Morrison and Philip Roth.��

The top book, “Parable of the Sower,” saw a huge spike in readership in 2024 — the year the futuristic novel is set, and the year SPL selected the novel for its program.��

“We’ve deemed these canonical authors important enough to continue reading, to continue teaching, to continue studying and talking about, so it’s fascinating to see who we’re actually reading and when,” Walsh said. “I find it very beautiful that after years of these big debates about diversifying the canon, the works that people are turning to the most are by women and Black and Native writers, who previously were not even included in these anthologies.”

Co-authors include Daniella Maor, Karalee Harris, Emily Backstrom and Hongyuan Dong, all students at the UW. This research was supported in part by the .

For more information, contact Walsh at melwalsh@uw.edu and Gupta at ngupta1@uw.edu.

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