  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 by biased networks, and debiasing the
models by removing such spurious correlations from them. We perform a
systematic integrated analysis of 7 visual artifacts (which are possible
sources of biases exploitable by networks), employ a state-of-the-art technique
to prevent the models from learning spurious correlations, and propose datasets
to test models for the presence of bias. We find out that, despite interesting
results that point to promising future research, current debiasing methods are
not ready to solve the bias issue for skin-lesion models.
