Classification Algorithm for Skin Color (CASCo): A new tool to measure skin color in social science research

Abstract

AbstractObjectiveA growing body of literature reveals that skin color has significant effects on people's income, health, education, and employment. However, the ways in which skin color has been measured in empirical research have been criticized for being inaccurate, if not subjective and biased.ObjectiveIntroduce an objective, automatic, accessible and customizable Classification Algorithm for Skin Color (CASCo).MethodsWe review the methods traditionally used to measure skin color (verbal scales, visual aids or color palettes, photo elicitation, spectrometers and image‐based algorithms), noting their shortcomings. We highlight the need for a different tool to measure skin colorResultsWe present CASCo, a (social researcher‐friendly) Python library that uses face detection, skin segmentation and k‐means clustering algorithms to determine the skin tone category of portraits.ConclusionAfter assessing the merits and shortcomings of all the methods available, we argue CASCo is well equipped to overcome most challenges and objections posed against its alternatives. While acknowledging its limitations, we contend that CASCo should complement researchers. toolkit in this area.