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Data-driven models are now deployed in a plethora of real-world applications- including automated diagnosis - but models learned from data risk learningbiases from that same data. When models learn spurious correlations not foundin real-world situations, their deployment for critical tasks, such as medicaldecisions, can be catastrophic. In this work we address this issue forskin-lesion classification models, with two objectives: finding out what arethe spurious correlations exploited by biased networks, and debiasing themodels by removing such spurious correlations from them. We perform asystematic integrated analysis of 7 visual artifacts (which are possiblesources of biases exploitable by networks), employ a state-of-the-art techniqueto prevent the models from learning spurious correlations, and propose datasetsto test models for the presence of bias. We find out that, despite interestingresults that point to promising future research, current debiasing methods arenot ready to solve the bias issue for skin-lesion models.

Debiasing Skin Lesion Datasets and Models? Not So Fast