  State-of-the-art deep neural networks (DNNs) are highly effective at tackling
many real-world tasks. However, their wide adoption in mission-critical
contexts is hampered by two major weaknesses - their susceptibility to
adversarial attacks and their opaqueness. The former raises concerns about the
security and generalization of DNNs in real-world conditions, whereas the
latter impedes users' trust in their output. In this research, we (1) examine
the effect of adversarial robustness on interpretability and (2) present a
novel approach for improving the interpretability of DNNs that is based on
regularization of neural activation sensitivity. We evaluate the
interpretability of models trained using our method to that of standard models
and models trained using state-of-the-art adversarial robustness techniques.
Our results show that adversarially robust models are superior to standard
models and that models trained using our proposed method are even better than
adversarially robust models in terms of interpretability.
