AI生成人脸时存在外貌偏见，影响性别识别准确性。
When Algorithms Play Favorites: Lookism in the Generation and Perception of Faces
- T2I模型将颜值与智能、可信等无关特质关联
- 非白人女性不美面孔的性别分类错误率更高
- 揭示数字身份系统中的算法外貌歧视风险
本文研究了合成人脸与基于机器学习的性别分类算法如何受到算法外貌歧视（lookism）的影响，即依据外貌进行偏好对待。在13,200张合成人脸的实验中发现：(1) 文本到图像（T2I）系统倾向于将面部吸引力与无关的正面特质如智力与可信度相关联；(2) 性别分类模型在“非吸引力”面孔上的错误率更高，尤其在非白人女性中更为显著。这些结果凸显了数字身份系统中的公平性问题。
原文摘要 · Abstract (English)
This paper examines how synthetically generated faces and machine learning-based gender classification algorithms are affected by algorithmic lookism, the preferential treatment based on appearance. In experiments with 13,200 synthetically generated faces, we find that: (1) text-to-image (T2I) systems tend to associate facial attractiveness to unrelated positive traits like intelligence and trustworthiness; and (2) gender classification models exhibit higher error rates on "less-attractive" faces, especially among non-White women. These result raise fairness concerns regarding digital identity systems.
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