When Cars Have Stereotypes: Auditing Demographic Bias in Objects from Text-to-Image Models

• URL: http://arxiv.org/abs/2508.03483v1
• Date: Tue, 05 Aug 2025 14:15:53 GMT
• Title: When Cars Have Stereotypes: Auditing Demographic Bias in Objects from Text-to-Image Models
• Authors: Dasol Choi Jihwan Lee, Minjae Lee, Minsuk Kahng,
• Abstract summary: We introduce SODA (Stereotyped Object Diagnostic Audit), a novel framework for measuring such biases.<n>Our approach compares visual attributes of objects generated with demographic cues to those from neutral prompts.<n>We uncover strong associations between specific demographic groups and visual attributes, such as recurring color patterns prompted by gender or ethnicity cues.
• Score: 4.240144901142787
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While prior research on text-to-image generation has predominantly focused on biases in human depictions, we investigate a more subtle yet pervasive phenomenon: demographic bias in generated objects (e.g., cars). We introduce SODA (Stereotyped Object Diagnostic Audit), a novel framework for systematically measuring such biases. Our approach compares visual attributes of objects generated with demographic cues (e.g., "for young people'') to those from neutral prompts, across 2,700 images produced by three state-of-the-art models (GPT Image-1, Imagen 4, and Stable Diffusion) in five object categories. Through a comprehensive analysis, we uncover strong associations between specific demographic groups and visual attributes, such as recurring color patterns prompted by gender or ethnicity cues. These patterns reflect and reinforce not only well-known stereotypes but also more subtle and unintuitive biases. We also observe that some models generate less diverse outputs, which in turn amplifies the visual disparities compared to neutral prompts. Our proposed auditing framework offers a practical approach for testing, revealing how stereotypes still remain embedded in today's generative models. We see this as an essential step toward more systematic and responsible AI development.

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