Who Defines Fairness? Target-Based Prompting for Demographic Representation in Generative Models
Text-to-image(T2I) models like Stable Diffusion and DALL-E have made generative AI widely accessible, yet recent studies reveal that these systems often replicate societal biases, particularly in how they depict demographic groups across professions. Prompts such as 'doctor' or 'CEO' frequently yield lighter-skinned outputs, while lower-status roles like 'janitor' show more diversity, reinforcing stereotypes. Existing mitigation methods typically require retraining or curated datasets, making them inaccessible to most users. We propose a lightweight, inference-time framework that mitigates representational bias through prompt-level intervention without modifying the underlying model. Inst...
| Authors | Marzia Binta Nizam, James Davis |
|---|---|
| Primary category | cs.AI |
| Published | 22 April 2026 |
| Last updated at source | 22 April 2026 |
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