  Recently, uncertainty-aware deep learning methods for multiclass labeling
problems have been developed that provide calibrated class prediction
probabilities and out-of-distribution (OOD) indicators, letting machine
learning (ML) consumers and engineers gauge a model's confidence in its
predictions. However, this extra neural network prediction information is
challenging to scalably convey visually for arbitrary data sources under
multiple uncertainty contexts. To address these challenges, we present
ScatterUQ, an interactive system that provides targeted visualizations to allow
users to better understand model performance in context-driven uncertainty
settings. ScatterUQ leverages recent advances in distance-aware neural
networks, together with dimensionality reduction techniques, to construct
robust, 2-D scatter plots explaining why a model predicts a test example to be
(1) in-distribution and of a particular class, (2) in-distribution but unsure
of the class, and (3) out-of-distribution. ML consumers and engineers can
visually compare the salient features of test samples with training examples
through the use of a ``hover callback'' to understand model uncertainty
performance and decide follow up courses of action. We demonstrate the
effectiveness of ScatterUQ to explain model uncertainty for a multiclass image
classification on a distance-aware neural network trained on Fashion-MNIST and
tested on Fashion-MNIST (in distribution) and MNIST digits (out of
distribution), as well as a deep learning model for a cyber dataset. We
quantitatively evaluate dimensionality reduction techniques to optimize our
contextually driven UQ visualizations. Our results indicate that the ScatterUQ
system should scale to arbitrary, multiclass datasets. Our code is available at
https://github.com/mit-ll-responsible-ai/equine-webapp
