Global explanations for discovering bias in data

In the paper, we propose attention-based summarized post-hoc explanations for detection and identification of bias in data. We propose a global explanation and introduce a step-by-step framework on how to detect and test bias. Then, the bias is evaluated with a proposed counterfactual approach to bias insertion. Because removing the unwanted bias is often a complicated and tremendous task, we automatically insert it, instead. We validate our results on the example of the skin lesion dataset. Using the method, we successfully identified and confirmed part of the possible bias-causing artifacts in dermoscopy images. We confirmed that the commonplace black frames in the training dataset images have a strong influence on the Convolutional Neural Network's prediction. After artificially adding a black frame to all images, around 22% of them changed the prediction from benign to malignant. We have shown that bias detection is an important step of making more robust models, and we discuss how to improve them