[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-156323-105":59,"doc-detail-156323-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","neutrality-bites-gender-representation-in-ai-generated-animal-stories","Neutrality Bites - Gender Representation in AI-Generated Animal Stories","","Gender bias in AI-generated stories remains widely documented, yet it is unclear whether neutrality-focused interventions yield genuinely fairer representations. This study tests six leading large language models on a controlled corpus of 23.8K English narratives about talking animals with unspecified character gender, varying story settings and model temperatures. Models often avoid gendering or use gender-neutral pronouns, but assigned genders show strong masculine skew: feminine characters appear only 2.2% versus 40.6% masculine. The results argue that “neutrality bites” may erase marginalized perspectives, motivating alternatives beyond neutrality.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/neutrality-bites-gender-representation-in-ai-generated-animal-stories/156323/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/neutrality-bites-gender-representation-in-ai-generated-animal-stories/156323.png","ImageObject",300,407,{"name":92,"@type":93},"wps_ap_test_251126_0180","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-17","2026-08-28",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",12,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"How do the authors test gender representation in AI-generated animal stories?","Question",{"text":113,"@type":114},"They prompt six leading large language models to complete English stories about multiple anthropomorphic animal characters whose gender is unstated, while iterating over narrative settings and model temperatures.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What patterns do the models show when assigning gender?",{"text":118,"@type":114},"Across 23.8K stories, models frequently avoid gendering or use gender-neutral language; when gender is assigned, the results show a significant masculine bias.",{"name":120,"@type":111,"acceptedAnswer":121},"What is the main implication of the study’s “neutrality bites” finding?",{"text":122,"@type":114},"The findings suggest neutrality-focused strategies may reduce bias on the surface while erasing marginalized identities and perspectives, so more equitable alternatives should be pursued.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},156323,1787955979,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},8796095027276,"https://avatar.qwps.com/avatar/d3BzX2FwX3Rlc3RfMjUxMTI2XzAxODA=","arXiv :2606 .07969v 1 [ cs .CL] 6 Jun 2026  \nNeutrality Bites: Gender Representation in AI-Generated Animal Stories  \nIMANI FINKLEY, University of Washington, USA YUANXI LI, University of Washington, USA MELANIE WALSH, University of Washington, USA  \nGender 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.  \nCCS Concepts: • Social and professional topics → Gender; • Information systems → Content analysis and feature selection; • Applied computing → Arts and humanities; • Computing methodologies → Natural language generation.  \nAdditional Key Words and Phrases: fiction, gender neutrality, gender bias, large language models  \nACM Reference Format:  \nImani Finkley, Yuanxi Li, and Melanie Walsh. 2026. Neutrality Bites: Gender Representation in AI-Generated Animal Stories. In The 2026 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’26), June 25–28, 2026, Montreal, QC, Canada. ACM, New York, NY, USA, 18 pages. [https://doi.org/10.1145/3805689.3812287](https://doi.org/10.1145/3805689.3812287)  \n1 Introduction  \nGender bias in AI-generated outputs has been a widespread problem [5, 27, 41] . These systems frequently overrepresent cisgender men and masculine perspectives while rendering women, non-binary people, and femininity either less visible, or reductively and offensively portrayed [7, 24] . This is concerning not only for algorithmic decision-making contexts like hiring, but also for storytelling and fiction, where AI models are increasingly used. For example, Google’s Gemini “Storybook” tool [17], which creates 10-page illustrated narratives based on prompts, is now being used by parents to read stories to their children [35] .  \nAuthors’ Contact Information: Imani Finkley, [ifinkley@uw.edu](ifinkley@uw.edu), University of Washington, Seattle, Washington, USA; Yuanxi Li, yuanxili@uw. edu, University of Washington, Seattle, Washington, USA; Melanie Walsh, melwalsh@uw.edu, University of Washington, Seattle, Washington, USA.  \nThis work is licensed under a Creative Commons Attribution 4 .0 International License. FAccT ’26, Montreal, QC, Canada  \n© 2026 Copyright held by the owner/author(s) .  \nACM ISBN 979-8-4007-2596-8/2026/06  \n[https://doi.org/10.1145/3805689.3812287](https://doi.org/10.1145/3805689.3812287)  \nFAccT ’26, June 25–28, 2026, Montreal, QC, Canada Imani Finkley, Yuanxi Li, and Melanie Walsh  \nFig. 1. Example prompt and response from GPT-4o with the following parameters: bear (subject), farm (setting), and 1.0 (temperature). We label this story Masculine due to the pronouns (bold green) used to refer to ","cbCaiauVmrwcDPrQ","https://ap.wps.com/l/cbCaiauVmrwcDPrQ","pdf",1527354,18,"English","# Introduction\n## Gender bias in AI-generated outputs\n## Research motivation and fairness concerns\n## Why talking-animal stories\n# Method and evaluation (implied)","[{\"question\":\"How do the authors test gender representation in AI-generated animal stories?\",\"answer\":\"They prompt six leading large language models to complete English stories about multiple anthropomorphic animal characters whose gender is unstated, while iterating over narrative settings and model temperatures.\"},{\"question\":\"What patterns do the models show when assigning gender?\",\"answer\":\"Across 23.8K stories, models frequently avoid gendering or use gender-neutral language; when gender is assigned, the results show a significant masculine bias.\"},{\"question\":\"What is the main implication of the study’s “neutrality bites” finding?\",\"answer\":\"The findings suggest neutrality-focused strategies may reduce bias on the surface while erasing marginalized identities and perspectives, so more equitable alternatives should be pursued.\"}]","Neutrality Bites - Gender Representation in AI-Generated Animal Stories | PDF",45]