StylisticBias: 少数人类视觉线索驱动多模态大语言模型中的大部分社会偏见
StylisticBias: A Few Human Visual Cues Drive Most Social Biases in MLLMs
AI总结：
提出StylisticBias基准，通过控制单一视觉属性变化，发现年龄和体型主导身份层面偏见，而时尚风格等约15个属性解释近80%的偏见变化，偏见集中于少数视觉线索。
摘要：
多模态大语言模型（MLLMs）越来越多地部署在个人和社会影响重大的场景中，但影响这些模型判断人物的视觉线索仍知之甚少。先前的工作通常比较不同的（群体）个体，难以将外貌效应与身份差异分离。我们引入StylisticBias，一个用于评估MLLMs中属性级社会偏见的受控基准。我们生成500张逼真的基础人脸，每张脸创建约50个单一属性变体，产生约25K张图像。这种设计保持身份不变，每次改变一个视觉属性，使我们能够测量特定线索如何改变模型判断。我们在25个二元社会判断场景中评估了六个MLLMs。我们发现年龄和体型主导身份层面的效应，而时尚风格和其他视觉线索驱动最大的属性级变化。我们进一步发现，约15个属性解释了近80%的总变异，表明偏见集中在少数视觉线索上。在与外貌语义对齐的判断中，尤其是社会经济和风格相关判断，敏感性最强。我们发布StylisticBias作为多模态模型细粒度偏见评估的基准。代码和数据集：此https URL和此https URL。
英文摘要：
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 model judgments. We evaluate six MLLMs across 25 binary social judgment scenarios. We find that age and body type dominate identity-level effects, while fashion style and other visual cues drive the largest attribute-level shifts. We further find that about 15 attributes account for nearly 80\% of the total variation, showing that bias is concentrated in a small set of visual cues. Sensitivity is strongest in judgments that are semantically aligned with appearance, especially socioeconomic and style-related judgments. We release StylisticBias as a benchmark for fine-grained bias evaluation in multimodal models. Code and dataset: https://github.com/timo-cavelius/StylisticBias and https://hf.co/datasets/shaghayegh/stylistic-bias-dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
