Test-time augmentation to improve XAI. Recently, Crabbé and van der Schaar independently analysed the invariance and equivariance of interpretability methods. They used the bag-of-words NLP model in combination with text permutation to assess the robustness of a number of feature importance methods, including IG and gradient SHAP . Bag-of-words models differ from our models in that text data in bag-of-words is inherently invariant to text permutation, whereas different text representations in our approach are learned to be invariant given the same underlying molecule. Crabbé and van der Schaar postulated that "...Any interpretability method can be made invariant ..., one should increase the number of samples N inv until the desired invariance is achieved. In this way, the method is made robust without increasing the number of calls more than necessary." . This is consistent with our findings using test-time augmentation to improve XAI robustness, where we see greater in-between model and in-between method consistency when using values averaged over multiple N inv samples (i.e., test-time augmentations). This was even true in a model that was not inherently invariant.