DARE: Towards Robust Text Explanations in Biomedical and Healthcare
  Applications
Adam Ivankay Mattia Rigotti Pascal Frossard
Along with the successful deployment of deep neural networks in several
application domains, the need to unravel the black-box nature of these networks
has seen a significant increase recently. Several methods have been introduced
to provide insight into the inference process of deep neural networks. However,
most of these explainability methods have been shown to be brittle in the face
of adversarial perturbations of their inputs in the image and generic textual
domain. In this work we show that this phenomenon extends to specific and
important high stakes domains like biomedical datasets. In particular, we
observe that the robustness of explanations should be characterized in terms of
the accuracy of the explanation in linking a model's inputs and its decisions -
faithfulness - and its relevance from the perspective of domain experts -
plausibility. This is crucial to prevent explanations that are inaccurate but
still look convincing in the context of the domain at hand. To this end, we
show how to adapt current attribution robustness estimation methods to a given
domain, so as to take into account domain-specific plausibility. This results
in our DomainAdaptiveAREstimator (DARE) attribution robustness estimator,
allowing us to properly characterize the domain-specific robustness of faithful
explanations. Next, we provide two methods, adversarial training and FAR
training, to mitigate the brittleness characterized by DARE, allowing us to
train networks that display robust attributions. Finally, we empirically
validate our methods with extensive experiments on three established biomedical
benchmarks.