We introduce explanatory guided learning (XGL), a novel interactive learning
strategy in which a machine guides a human supervisor toward selecting
informative examples for a classifier. The guidance is provided by means of
global explanations, which summarize the classifier's behavior on different
regions of the instance space and expose its flaws. Compared to other
explanatory interactive learning strategies, which are machine-initiated and
rely on local explanations, XGL is designed to be robust against cases in which
the explanations supplied by the machine oversell the classifier's quality.
Moreover, XGL leverages global explanations to open up the black-box of
human-initiated interaction, enabling supervisors to select informative
examples that challenge the learned model. By drawing a link to interactive
machine teaching, we show theoretically that global explanations are a viable
approach for guiding supervisors. Our simulations show that explanatory guided
learning avoids overselling the model's quality and performs comparably or
better than machine- and human-initiated interactive learning strategies in
terms of model quality.

提出了一种称为解释性引导学习（XGL）的新型交互式学习策略，其中机器通过全局解释来引导人类监督员选择具有信息量的分类器示例，这种方法有效避免了机器供给的局部解释可能过高估计分类器质量的问题。