  As black-box models increasingly power high-stakes applications, a variety of
data-driven explanation methods have been introduced. Meanwhile, machine
learning models are constantly challenged by distributional shifts. A question
naturally arises: Are data-driven explanations robust against
out-of-distribution data? Our empirical results show that even though predict
correctly, the model might still yield unreliable explanations under
distributional shifts. How to develop robust explanations against
out-of-distribution data? To address this problem, we propose an end-to-end
model-agnostic learning framework Distributionally Robust Explanations (DRE).
The key idea is, inspired by self-supervised learning, to fully utilizes the
inter-distribution information to provide supervisory signals for the learning
of explanations without human annotation. Can robust explanations benefit the
model's generalization capability? We conduct extensive experiments on a wide
range of tasks and data types, including classification and regression on image
and scientific tabular data. Our results demonstrate that the proposed method
significantly improves the model's performance in terms of explanation and
prediction robustness against distributional shifts.
