  Counterfactual explanations (CEs) are a practical tool for demonstrating why
machine learning classifiers make particular decisions. For CEs to be useful,
it is important that they are easy for users to interpret. Existing methods for
generating interpretable CEs rely on auxiliary generative models, which may not
be suitable for complex datasets, and incur engineering overhead. We introduce
a simple and fast method for generating interpretable CEs in a white-box
setting without an auxiliary model, by using the predictive uncertainty of the
classifier. Our experiments show that our proposed algorithm generates more
interpretable CEs, according to IM1 scores, than existing methods.
Additionally, our approach allows us to estimate the uncertainty of a CE, which
may be important in safety-critical applications, such as those in the medical
domain.
