  The problem of explaining the behavior of deep neural networks has recently
gained a lot of attention. While several attribution methods have been
proposed, most come without strong theoretical foundations, which raises
questions about their reliability. On the other hand, the literature on
cooperative game theory suggests Shapley values as a unique way of assigning
relevance scores such that certain desirable properties are satisfied.
Unfortunately, the exact evaluation of Shapley values is prohibitively
expensive, exponential in the number of input features. In this work, by
leveraging recent results on uncertainty propagation, we propose a novel,
polynomial-time approximation of Shapley values in deep neural networks. We
show that our method produces significantly better approximations of Shapley
values than existing state-of-the-art attribution methods.
