Generative AI creates and spreads biases in beauty and race
Generative AI Produces and Transmits Beauty Bias
Computers and SocietyComputer Vision and Pattern Recognition
Summary
This paper looks at how AI programs that create images of faces might copy and spread unfair ideas about attractiveness linked to gender and race. The authors checked many AI tools and found that these tools often make faces that look more attractive than real photos, especially favoring lighter skin tones. When people saw AI images of very attractive faces, they judged real people as less trustworthy and less likely to be hired. The study shows that AI can continue and even change how people view beauty and trust.
What this means in practice
- •For ai ethics teams: Detect and monitor biases in AI-generated faces to improve fairness and reduce harmful stereotypes in AI systems.
- •For human resources software developers: Adjust candidate evaluation tools to account for effects of AI-generated image bias on perceived trustworthiness and hireability.
Authors
AJ Alvero, Deepak Varuvel Dennison, Alexandra Crespin, Heeyon Kim, Alejandra Regla-Vargas, Avani Akerkar, Jasmine McNealy, René Kizilcec
Abstract
Societies reward people judged to be attractive by standards intertwined with gender and race. In this paper, we examine if and how generative AI may contribute to these dynamics. We audited 27 text-to-image generators released between 2022 and 2026 using simple prompts that never mention attractiveness or beauty, analyzing 51,300 generated faces and 160,196 photographs of real people across primary and supplementary comparisons. We used three automated measures and collected ratings from 550 human raters on a subset of images. Nearly all generators produced faces scoring above photographed faces under automated measures, and human raters judged the outputs of all 23 generators as significantly more attractive than photographed faces, even after correction for multiple comparisons. When focusing our analysis on gender, we find that 37% of female-prompted generated faces outscored the median professional fashion model. Across racial, ethnic, and national categories, groups associated with darker skin tones tended to receive lower attractiveness scores than groups associated with lighter skin tones. In a preregistered hiring simulation experiment (N = 570), exposure to AI-generated faces selected for high human-rated attractiveness lowered hireability and trustworthiness ratings and reduced interview advancement for an identical test set of candidates paired with photographs of real people; exposure to retouched photographs produced no such effect. Generative AI can reproduce racial hierarchies and gendered patterns of beauty and alter the context in which real people are judged.