@inproceedings{warren-etal-2025-decoding,
title = "Decoding Fatphobia: Examining Anti-Fat and Pro-Thin Bias in {AI}-Generated Images",
author = "Warren, Jane and
Weiss, Gary M. and
Martinez, Fernando and
Guo, Annika and
Zhao, Yijun",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-naacl.266/",
doi = "10.18653/v1/2025.findings-naacl.266",
pages = "4724--4736",
ISBN = "979-8-89176-195-7",
abstract = "Existing studies have shown that AI-generated images tend to reinforce social biases, including those related to race and gender. However, no studies have investigated weight bias, or fatphobia, in AI-generated images. This study utilizes DALL-E 3 to determine the extent to which anti-fat and pro-thin biases are present in AI-generated images, and examines stereotypical associations between moral character and body weight. Four-thousand images are generated using twenty pairs of positive and negative textual prompts. These images are then manually labeled with weight information and analyzed to determine the extent to which they reflect fatphobia. The findings and their impact are discussed and related to existing research on weight bias."
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<abstract>Existing studies have shown that AI-generated images tend to reinforce social biases, including those related to race and gender. However, no studies have investigated weight bias, or fatphobia, in AI-generated images. This study utilizes DALL-E 3 to determine the extent to which anti-fat and pro-thin biases are present in AI-generated images, and examines stereotypical associations between moral character and body weight. Four-thousand images are generated using twenty pairs of positive and negative textual prompts. These images are then manually labeled with weight information and analyzed to determine the extent to which they reflect fatphobia. The findings and their impact are discussed and related to existing research on weight bias.</abstract>
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%0 Conference Proceedings
%T Decoding Fatphobia: Examining Anti-Fat and Pro-Thin Bias in AI-Generated Images
%A Warren, Jane
%A Weiss, Gary M.
%A Martinez, Fernando
%A Guo, Annika
%A Zhao, Yijun
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Findings of the Association for Computational Linguistics: NAACL 2025
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-195-7
%F warren-etal-2025-decoding
%X Existing studies have shown that AI-generated images tend to reinforce social biases, including those related to race and gender. However, no studies have investigated weight bias, or fatphobia, in AI-generated images. This study utilizes DALL-E 3 to determine the extent to which anti-fat and pro-thin biases are present in AI-generated images, and examines stereotypical associations between moral character and body weight. Four-thousand images are generated using twenty pairs of positive and negative textual prompts. These images are then manually labeled with weight information and analyzed to determine the extent to which they reflect fatphobia. The findings and their impact are discussed and related to existing research on weight bias.
%R 10.18653/v1/2025.findings-naacl.266
%U https://aclanthology.org/2025.findings-naacl.266/
%U https://doi.org/10.18653/v1/2025.findings-naacl.266
%P 4724-4736
Markdown (Informal)
[Decoding Fatphobia: Examining Anti-Fat and Pro-Thin Bias in AI-Generated Images](https://aclanthology.org/2025.findings-naacl.266/) (Warren et al., Findings 2025)
ACL