  Machine learning methods must be trusted to make appropriate decisions in
real-world environments, even when faced with out-of-distribution (OOD)
samples. Many current approaches simply aim to detect OOD examples and alert
the user when an unrecognized input is given. However, when the OOD sample
significantly overlaps with the training data, a binary anomaly detection is
not interpretable or explainable, and provides little information to the user.
We propose a new model for OOD detection that makes predictions at varying
levels of granularity as the inputs become more ambiguous, the model
predictions become coarser and more conservative. Consider an animal classifier
that encounters an unknown bird species and a car. Both cases are OOD, but the
user gains more information if the classifier recognizes that its uncertainty
over the particular species is too large and predicts bird instead of detecting
it as OOD. Furthermore, we diagnose the classifiers performance at each level
of the hierarchy improving the explainability and interpretability of the
models predictions. We demonstrate the effectiveness of hierarchical
classifiers for both fine- and coarse-grained OOD tasks.
