  An active learning (AL) algorithm seeks to construct an effective classifier
with a minimal number of labeled examples in a bootstrapping manner. While
standard AL heuristics, such as selecting those points for annotation for which
a classification model yields least confident predictions, there has been no
empirical investigation to see if these heuristics lead to models that are more
interpretable to humans. In the era of data-driven learning, this is an
important research direction to pursue. This paper describes our
work-in-progress towards developing an AL selection function that in addition
to model effectiveness also seeks to improve on the interpretability of a model
during the bootstrapping steps. Concretely speaking, our proposed selection
function trains an `explainer' model in addition to the classifier model, and
favours those instances where a different part of the data is used, on an
average, to explain the predicted class. Initial experiments exhibited
encouraging trends in showing that such a heuristic can lead to developing more
effective and more explainable end-to-end data-driven classifiers.
