  Both uncertainty estimation and interpretability are important factors for
trustworthy machine learning systems. However, there is little work at the
intersection of these two areas. We address this gap by proposing a novel
method for interpreting uncertainty estimates from differentiable probabilistic
models, like Bayesian Neural Networks (BNNs). Our method, Counterfactual Latent
Uncertainty Explanations (CLUE), indicates how to change an input, while
keeping it on the data manifold, such that a BNN becomes more confident about
the input's prediction. We validate CLUE through 1) a novel framework for
evaluating counterfactual explanations of uncertainty, 2) a series of ablation
experiments, and 3) a user study. Our experiments show that CLUE outperforms
baselines and enables practitioners to better understand which input patterns
are responsible for predictive uncertainty.
