Year: 2025
Type: article
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models, like Midjourney, DALL-E, and Stable Diffusion, have been shown to reinforce harmful biases, often perpetuating outdated and discriminatory stereotypes.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.
Source: AI & Society
Authors: Juliana Shihadeh, Margareta Ackerman, David Loker
Institution: Santa Clara University
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
SDG: Reduced inequalities
Open Access status: hybrid
Estimated APC paid: $3,190
Funder: Santa Clara University
OpenAlex ID: W4417144697