  The practical impact of deep learning on complex supervised learning problems
has been significant, so much so that almost every Artificial Intelligence
problem, or at least a portion thereof, has been somehow recast as a deep
learning problem. The applications appeal is significant, but this appeal is
increasingly challenged by what some call the challenge of explainability, or
more generally the more traditional challenge of debuggability: if the outcomes
of a deep learning process produce unexpected results (e.g., less than expected
performance of a classifier), then there is little available in the way of
theories or tools to help investigate the potential causes of such unexpected
behavior, especially when this behavior could impact people's lives. We
describe a preliminary framework to help address this issue, which we call
"deep visual explanation" (DVE). "Deep," because it is the development and
performance of deep neural network models that we want to understand. "Visual,"
because we believe that the most rapid insight into a complex multi-dimensional
model is provided by appropriate visualization techniques, and "Explanation,"
because in the spectrum from instrumentation by inserting print statements to
the abductive inference of explanatory hypotheses, we believe that the key to
understanding deep learning relies on the identification and exposure of
hypotheses about the performance behavior of a learned deep model. In the
exposition of our preliminary framework, we use relatively straightforward
image classification examples and a variety of choices on initial configuration
of a deep model building scenario. By careful but not complicated
instrumentation, we expose classification outcomes of deep models using
visualization, and also show initial results for one potential application of
interpretability.
