  Techniques for understanding the functioning of complex machine learning
models are becoming increasingly popular, not only to improve the validation
process, but also to extract new insights about the data via exploratory
analysis. Though a large class of such tools currently exists, most assume that
predictions are point estimates and use a sensitivity analysis of these
estimates to interpret the model. Using lightweight probabilistic networks we
show how including prediction uncertainties in the sensitivity analysis leads
to: (i) more robust and generalizable models; and (ii) a new approach for model
interpretation through uncertainty decomposition. In particular, we introduce a
new regularization that takes both the mean and variance of a prediction into
account and demonstrate that the resulting networks provide improved
generalization to unseen data. Furthermore, we propose a new technique to
explain prediction uncertainties through uncertainties in the input domain,
thus providing new ways to validate and interpret deep learning models.
