  We argue that robustness of explanations---i.e., that similar inputs should
give rise to similar explanations---is a key desideratum for interpretability.
We introduce metrics to quantify robustness and demonstrate that current
methods do not perform well according to these metrics. Finally, we propose
ways that robustness can be enforced on existing interpretability approaches.
