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.

该研究旨在衡量计算机视觉中明显的肤色，超越肤色一维刻板印象的范围。通过引入色调角度从红到黄的范围作为肤色的补充维度，该研究揭示了计算机视觉系统中与肤色相关的偏见，并推荐使用多维肤色尺度，以评估公平性。