  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.
