ArticleScientific reports2025
AI-generated faces influence gender stereotypes and racial homogenization.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Information Source-Message Effect on Consumers' Intentions to Adhere to Food Safety Practices When Handling and Preparing Raw Chicken.Foods (Basel, Switzerland) · 2026Article
- FairGen: preference-aligned diffusion for demographically equitable medical image synthesis.NPJ digital medicine · 2026Article
- FaceScanPaliGemma multi-agent vision language models for facial attribute recognition.Scientific reports · 2026Article
- Demographic biases in AI-generated simulated patient cohorts: a comparative analysis against census benchmarks.Advances in simulation (London, England) · 2025Article
- Towards a World Wide Web powered by generative AI.Scientific reports · 2025Article
- Beyond the Algorithm: A Perspective on Tackling Bias and Cultural Sensitivity in AI-Guided Aesthetic Standards for Cosmetic Surgery in the Middle East and North Africa (MENA) Region.Clinical, cosmetic and investigational dermatology · 2025Article
- Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Text-to-image generative AI models such as Stable Diffusion are used daily by millions worldwide. However, the extent to which these models exhibit racial and gender stereotypes is not yet fully understood. Here, we document significant biases in Stable Diffusion across six races, two genders, 32 professions, and eight attributes. Additionally, we examine the degree to which Stable Diffusion depicts individuals of the same race as being similar to one another. This analysis reveals significant racial homogenization, e.g., depicting nearly all Middle Eastern men as bearded, brown-skinned, and wearing traditional attire. We then propose debiasing solutions that allow users to specify the desired distributions of race and gender when generating images while minimizing racial homogenization. Finally, using a preregistered survey experiment, we find evidence that being presented with inclusive AI-generated faces reduces people's racial and gender biases, while being presented with non-inclusive ones increases such biases, regardless of whether the images are labeled as AI-generated. Taken together, our findings emphasize the need to address biases and stereotypes in text-to-image models.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.