  Recently, interpretable models called self-explaining models (SEMs) have been
proposed with the goal of providing interpretability robustness. We evaluate
the interpretability robustness of SEMs and show that explanations provided by
SEMs as currently proposed are not robust to adversarial inputs. Specifically,
we successfully created adversarial inputs that do not change the model outputs
but cause significant changes in the explanations. We find that even though
current SEMs use stable co-efficients for mapping explanations to output
labels, they do not consider the robustness of the first stage of the model
that creates interpretable basis concepts from the input, leading to non-robust
explanations. Our work makes a case for future work to start examining how to
generate interpretable basis concepts in a robust way.
