超越肤色深浅：表观肤色的多维度测量方法
Beyond Skin Tone: A Multidimensional Measure of Apparent Skin Color
AI总结：
针对现有Fitzpatrick肤色量表仅衡量肤色深浅的局限，该研究引入色相角维度构建多维度肤色测量方法，可揭示更多肤色相关偏见，为计算机视觉公平性评估提供更全面的指标。
摘要：
本文致力于在计算机视觉领域测量表观肤色，突破单一肤色深浅维度的限制。Buolamwini与Gebru在其开创性论文《Gender Shades（性别阴影）》中证实，性别分类系统会对深肤色女性存在偏见。此后，公平性领域的研究者与从业者普遍采用Fitzpatrick皮肤类型分类法，作为评估计算机视觉系统肤色偏见的通用指标。该方法虽有效，但Fitzpatrick量表仅聚焦从浅到深的肤色深浅维度。为实现更全面的肤色测量，本文引入从红到黄的色相角维度。将色相维度应用于图像后，可揭示计算机视觉数据集与模型中存在的其他肤色相关偏见。本文最后建议在公平性评估中采用同时涵盖肤色深浅与色相的多维度肤色量表。
英文摘要：
This paper strives to measure apparent skin color in computer vision, beyond a unidimensional scale on skin tone. In their seminal paper Gender Shades, Buolamwini and Gebru have shown how gender classification systems can be biased against women with darker skin tones. Subsequently, fairness researchers and practitioners have adopted the Fitzpatrick skin type classification as a common measure to assess skin color bias in computer vision systems. While effective, the Fitzpatrick scale only focuses on the skin tone ranging from light to dark. Towards a more comprehensive measure of skin color, we introduce the hue angle ranging from red to yellow. When applied to images, the hue dimension reveals additional biases related to skin color in both computer vision datasets and models. We then recommend multidimensional skin color scales, relying on both skin tone and hue, for fairness assessments.
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