StylisticBias: A Few Human Visual Cues Drive Most Social Biases in MLLMs
Multimodal large language models (MLLMs) are increasingly deployed in personally and societally consequential settings, yet the visual cues that shape how these models judge people remain poorly understood. Prior work often compares different (groups of) individuals, making it difficult to separate appearance effects from identity differences. We introduce StylisticBias, a controlled benchmark for evaluating attribute-level social bias in MLLMs. We generate 500 photorealistic base faces and create about 50 single-attribute variations per face, producing about 25K images. This design keeps identity fixed and changes one visual attribute at a time. It lets us measure how specific cues shift...
| Authors | Shaghayegh Kolli, Timo Cavelius, Nafiseh Nikeghbal et al. |
|---|---|
| Primary category | cs.CL |
| Published | 18 June 2026 |
| Last updated at source | 18 June 2026 |
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