'Person' == Light-skinned, Western Man, and Sexualization of Women of Color: Stereotypes in Stable Diffusion
Sourojit Ghosh, Aylin Caliskan

TL;DR
This study analyzes stereotypes in Stable Diffusion, revealing biases towards Western men, continental stereotypes, and oversexualization of women of color, highlighting concerning societal implications of AI-generated images.
Contribution
It provides a systematic examination of gender and nationality stereotypes in Stable Diffusion, including the novel use of CLIP similarity and NSFW detection to quantify biases.
Findings
Stable diffusion favors images of European/North American men for 'a person'
Continental stereotypes lead to misrepresentation of Indigenous Oceanic peoples
Women of color, especially Latin American, Mexican, Indian, and Egyptian, are oversexualized
Abstract
We study stereotypes embedded within one of the most popular text-to-image generators: Stable Diffusion. We examine what stereotypes of gender and nationality/continental identity does Stable Diffusion display in the absence of such information i.e. what gender and nationality/continental identity is assigned to `a person', or to `a person from Asia'. Using vision-language model CLIP's cosine similarity to compare images generated by CLIP-based Stable Diffusion v2.1 verified by manual examination, we chronicle results from 136 prompts (50 results/prompt) of front-facing images of persons from 6 different continents, 27 nationalities and 3 genders. We observe how Stable Diffusion outputs of `a person' without any additional gender/nationality information correspond closest to images of men and least with persons of nonbinary gender, and to persons from Europe/North America over…
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Taxonomy
TopicsMedia, Gender, and Advertising · Media Studies and Communication · Social and Cultural Dynamics
Methods7 Fastest Ways to Call American Airlines Reservations Number (USA Guide) · Diffusion
