  The notion of robustness in XAI refers to the observed variations in the
explanation of the prediction of a learned model with respect to changes in the
input leading to that prediction. Intuitively, if the input being explained is
modified slightly subtly enough so as to not change the prediction of the model
too much, then we would expect that the explanation provided for that new input
does not change much either. We argue that a combination through discriminative
averaging of ensembles weak learners explanations can improve the robustness of
explanations in ensemble methods.This approach has been implemented and tested
with post-hoc SHAP method and Random Forest ensemble with successful results.
The improvements obtained have been measured quantitatively and some insights
into the explicability robustness in ensemble methods are presented.
