  The paper proposes summarized attribution-based post-hoc explanations for the
detection and identification of bias in data. A global explanation is proposed,
and a step-by-step framework on how to detect and test bias is introduced.
Since removing unwanted bias is often a complicated and tremendous task, it is
automatically inserted, instead. Then, the bias is evaluated with the proposed
counterfactual approach. The obtained results are validated on a sample skin
lesion dataset. Using the proposed method, a number of possible bias causing
artifacts are successfully identified and confirmed in dermoscopy images. In
particular, it is confirmed that black frames have a strong influence on
Convolutional Neural Network's prediction: 22% of them changed the prediction
from benign to malignant.
