  With the rise of deep neural networks, the challenge of explaining the
predictions of these networks has become increasingly recognized. While many
methods for explaining the decisions of deep neural networks exist, there is
currently no consensus on how to evaluate them. On the other hand, robustness
is a popular topic for deep learning research; however, it is hardly talked
about in explainability until very recently. In this tutorial paper, we start
by presenting gradient-based interpretability methods. These techniques use
gradient signals to assign the burden of the decision on the input features.
Later, we discuss how gradient-based methods can be evaluated for their
robustness and the role that adversarial robustness plays in having meaningful
explanations. We also discuss the limitations of gradient-based methods.
Finally, we present the best practices and attributes that should be examined
before choosing an explainability method. We conclude with the future
directions for research in the area at the convergence of robustness and
explainability.
