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State-of-the-art machine learning models often learn spurious correlationsembedded in the training data. This poses risks when deploying these models forhigh-stake decision-making, such as in medical applications like skin cancerdetection. To tackle this problem, we propose Reveal to Revise (R2R), aframework entailing the entire eXplainable Artificial Intelligence (XAI) lifecycle, enabling practitioners to iteratively identify, mitigate, and(re-)evaluate spurious model behavior with a minimal amount of humaninteraction. In the first step (1), R2R reveals model weaknesses by findingoutliers in attributions or through inspection of latent concepts learned bythe model. Secondly (2), the responsible artifacts are detected and spatiallylocalized in the input data, which is then leveraged to (3) revise the modelbehavior. Concretely, we apply the methods of RRR, CDEP and ClArC for modelcorrection, and (4) (re-)evaluate the model's performance and remainingsensitivity towards the artifact. Using two medical benchmark datasets forMelanoma detection and bone age estimation, we apply our R2R framework to VGG,ResNet and EfficientNet architectures and thereby reveal and correct realdataset-intrinsic artifacts, as well as synthetic variants in a controlledsetting. Completing the XAI life cycle, we demonstrate multiple R2R iterationsto mitigate different biases. Code is available onhttps://github.com/maxdreyer/Reveal2Revise.

Reveal to Revise: An Explainable AI Life Cycle for Iterative Bias Correction of Deep Models