Document Type
Article
Publication Date
12-2025
Publisher
Springer Nature
Abstract
Text-to-image models, like Midjourney, DALL-E, and Stable Diffusion, have been shown to reinforce harmful biases, often perpetuating outdated and discriminatory stereotypes. In this study, we delve into a particular bias largely overlooked in generative image research: Brilliance Bias. By age 6, many children begin to internalize the damaging notion that intellectual brilliance is a male trait—a belief that persists into adulthood. Our findings demonstrate that popular image AI models possess this bias, further entrenching the misguided notion that exceptional intelligence is inherently male. This study calls for addressing brilliance bias in AI to ensure a more realistic representation of intellectual capabilities, helping shape a future where talent and brilliance are more broadly recognized.
Recommended Citation
Shihadeh, J., Ackerman, M., & Loker, D. (2026). What does genius look like? Investigating brilliance bias in AI-generated images. AI & SOCIETY, 41(4), 3121–3142. https://doi.org/10.1007/s00146-025-02752-6
Comments
Open access to this article is funded by Santa Clara University Library.
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