  While deep learning based approaches have demonstrated expert-level
performance in dermatological diagnosis tasks, they have also been shown to
exhibit biases toward certain demographic attributes, particularly skin types
(e.g., light versus dark), a fairness concern that must be addressed. We
propose CIRCLe, a skin color invariant deep representation learning method for
improving fairness in skin lesion classification. CIRCLe is trained to classify
images by utilizing a regularization loss that encourages images with the same
diagnosis but different skin types to have similar latent representations.
Through extensive evaluation and ablation studies, we demonstrate CIRCLe's
superior performance over the state-of-the-art when evaluated on 16k+ images
spanning 6 Fitzpatrick skin types and 114 diseases, using classification
accuracy, equal opportunity difference (for light versus dark groups), and
normalized accuracy range, a new measure we propose to assess fairness on
multiple skin type groups.
