  Understanding decisions made by neural networks is key for the deployment of
intelligent systems in real world applications. However, the opaque decision
making process of these systems is a disadvantage where interpretability is
essential. Many feature-based explanation techniques have been introduced over
the last few years in the field of machine learning to better understand
decisions made by neural networks and have become an important component to
verify their reasoning capabilities. However, existing methods do not allow
statements to be made about the uncertainty regarding a feature's relevance for
the prediction. In this paper, we introduce Monte Carlo Relevance Propagation
(MCRP) for feature relevance uncertainty estimation. A simple but powerful
method based on Monte Carlo estimation of the feature relevance distribution to
compute feature relevance uncertainty scores that allow a deeper understanding
of a neural network's perception and reasoning.
