Extracting Explanations, Justification, and Uncertainty from Black-Box
  Deep Neural Networks
Paul Ardis Arjuna Flenner
Deep Neural Networks (DNNs) do not inherently compute or exhibit
empirically-justified task confidence. In mission critical applications, it is
important to both understand associated DNN reasoning and its supporting
evidence. In this paper, we propose a novel Bayesian approach to extract
explanations, justifications, and uncertainty estimates from DNNs. Our approach
is efficient both in terms of memory and computation, and can be applied to any
black box DNN without any retraining, including applications to anomaly
detection and out-of-distribution detection tasks. We validate our approach on
the CIFAR-10 dataset, and show that it can significantly improve the
interpretability and reliability of DNNs.