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Addressing fairness in lesion classification from dermatological images iscrucial due to variations in how skin diseases manifest across skin tones.However, the absence of skin tone labels in public datasets hinders building afair classifier. To date, such skin tone labels have been estimated prior tofairness analysis in independent studies using the Individual Typology Angle(ITA). Briefly, ITA calculates an angle based on pixels extracted from skinimages taking into account the lightness and yellow-blue tints. These anglesare then categorised into skin tones that are subsequently used to analysefairness in skin cancer classification. In this work, we review and comparefour ITA-based approaches of skin tone classification on the ISIC18 dataset, acommon benchmark for assessing skin cancer classification fairness in theliterature. Our analyses reveal a high disagreement among previously publishedstudies demonstrating the risks of ITA-based skin tone estimation methods.Moreover, we investigate the causes of such large discrepancy among theseapproaches and find that the lack of diversity in the ISIC18 dataset limits itsuse as a testbed for fairness analysis. Finally, we recommend further researchon robust ITA estimation and diverse dataset acquisition with skin toneannotation to facilitate conclusive fairness assessments of artificialintelligence tools in dermatology. Our code is available athttps://github.com/tkalbl/RevisitingSkinToneFairness.

Revisiting Skin Tone Fairness in Dermatological Lesion Classification