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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 learning biases from that same data. When models learn spurious correlations not found in real-world situations, their deployment for critical tasks, such as medical decisions, can be catastrophic. In this work we address this issue for skin-lesion classification models, with two objectives: finding out what are the spurious correlations exploited bydoi:10.1109/cvprw50498.2020.00378 dblp:conf/cvpr/BissotoVA20 fatcat:aklcaprjgrh6lcg4flwak3nwna