  Although interactive learning puts the user into the loop, the learner
remains mostly a black box for the user. Understanding the reasons behind
queries and predictions is important when assessing how the learner works and,
in turn, trust. Consequently, we propose the novel framework of explanatory
interactive learning: in each step, the learner explains its interactive query
to the user, and she queries of any active classifier for visualizing
explanations of the corresponding predictions. We demonstrate that this can
boost the predictive and explanatory powers of and the trust into the learned
model, using text (e.g. SVMs) and image classification (e.g. neural networks)
experiments as well as a user study.
