Generative AI Produces and Transmits Beauty Bias

arXiv:2610.22553v1 Announce Type: new 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 an

Generative AI perpetuates beauty bias, reinforcing racial and gender hierarchies.

The study finds generative AI systematically produces faces that are rated as more attractive, favoring lighter skin tones and reinforcing existing racial and gender biases. This results in negative impacts on real people’s hireability and trustworthiness, indicating AI contributes to social discrimination and harms human flourishing.

This is a well-powered empirical study with a preregistered experiment showing real behavioral harm from AI-generated beauty bias, including racial skew and hiring discrimination effects. The evidence is strong and directly tied to societal implications, though the scope is specific to beauty/hiring judgments rather than broader existential stakes.

The research provides strong evidence that generative AI significantly amplifies societal beauty biases, including those intertwined with gender and race. It directly links exposure to AI-generated images to negative real-world outcomes, such as reduced hireability for actual job candidates. This suggests a material and concerning impact on human flourishing through the perpetuation of harmful stereotypes and biases.

The study shows that generative AI amplifies existing beauty biases and negatively impacts real people's perceptions. The hiring simulation experiment demonstrates a clear negative societal implication.

SOURCE     arXiv cs.CY (Computers and Society) (tier 1)
   ↓
DOCUMENT   7105d098-299c-4062-93bd-3ada2decf33e
           https://arxiv.org/abs/2610.22553
   ↓
EVIDENCE   2 extracted excerpts
   ↓
MODEL RUN  5 runs, methodology 1.0.1
   ↓
SCORE      -69  (Strongly adverse)
   ↓
CONFIDENCE 87%