  Neural networks (NNs) have shown high predictive performance, however, with
shortcomings. Firstly, the reasons behind the classifications are not fully
understood. Several explanation methods have been developed, but they do not
provide mechanisms for users to interact with the explanations. Explanations
are social, meaning they are a transfer of knowledge through interactions.
Nonetheless, current explanation methods contribute only to one-way
communication. Secondly, NNs tend to be overconfident, providing unreasonable
uncertainty estimates on out-of-distribution observations. We overcome these
difficulties by training a Bayesian convolutional neural network (CNN) that
uses explanation feedback. After training, the model presents explanations of
training sample classifications to an annotator. Based on the provided
information, the annotator can accept or reject the explanations by providing
feedback. Our proposed method utilizes this feedback for fine-tuning to correct
the model such that the explanations and classifications improve. We use
existing CNN architectures to demonstrate the method's effectiveness on one toy
dataset (decoy MNIST) and two real-world datasets (Dogs vs. Cats and ISIC skin
cancer). The experiments indicate that few annotated explanations and
fine-tuning epochs are needed to improve the model and predictive performance,
making the model more trustworthy and understandable.
