  With the increased deployment of machine learning models in various
real-world applications, researchers and practitioners alike have emphasized
the need for explanations of model behaviour. To this end, two broad strategies
have been outlined in prior literature to explain models. Post hoc explanation
methods explain the behaviour of complex black-box models by identifying
features critical to model predictions; however, prior work has shown that
these explanations may not be faithful, in that they incorrectly attribute high
importance to features that are unimportant or non-discriminative for the
underlying task. Inherently interpretable models, on the other hand, circumvent
these issues by explicitly encoding explanations into model architecture,
meaning their explanations are naturally faithful, but they often exhibit poor
predictive performance due to their limited expressive power. In this work, we
identify a key reason for the lack of faithfulness of feature attributions: the
lack of robustness of the underlying black-box models, especially to the
erasure of unimportant distractor features in the input. To address this issue,
we propose Distractor Erasure Tuning (DiET), a method that adapts black-box
models to be robust to distractor erasure, thus providing discriminative and
faithful attributions. This strategy naturally combines the ease of use of post
hoc explanations with the faithfulness of inherently interpretable models. We
perform extensive experiments on semi-synthetic and real-world datasets and
show that DiET produces models that (1) closely approximate the original
black-box models they are intended to explain, and (2) yield explanations that
match approximate ground truths available by construction. Our code is made
public at https://github.com/AI4LIFE-GROUP/DiET.
