  Robustness has become an important consideration in deep learning. With the
help of explainable AI, mismatches between an explained model's decision
strategy and the user's domain knowledge (e.g. Clever Hans effects) have been
identified as a starting point for improving faulty models. However, it is less
clear what to do when the user and the explanation agree. In this paper, we
demonstrate that acceptance of explanations by the user is not a guarantee for
a machine learning model to be robust against Clever Hans effects, which may
remain undetected. Such hidden flaws of the model can nevertheless be
mitigated, and we demonstrate this by contributing a new method,
Explanation-Guided Exposure Minimization (EGEM), that preemptively prunes
variations in the ML model that have not been the subject of positive
explanation feedback. Experiments demonstrate that our approach leads to models
that strongly reduce their reliance on hidden Clever Hans strategies, and
consequently achieve higher accuracy on new data.
