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There has recently been a surge of work in explanatory artificialintelligence (XAI). This research area tackles the important problem thatcomplex machines and algorithms often cannot provide insights into theirbehavior and thought processes. XAI allows users and parts of the internalsystem to be more transparent, providing explanations of their decisions insome level of detail. These explanations are important to ensure algorithmicfairness, identify potential bias/problems in the training data, and to ensurethat the algorithms perform as expected. However, explanations produced bythese systems is neither standardized nor systematically assessed. In an effortto create best practices and identify open challenges, we provide ourdefinition of explainability and show how it can be used to classify existingliterature. We discuss why current approaches to explanatory methods especiallyfor deep neural networks are insufficient. Finally, based on our survey, weconclude with suggested future research directions for explanatory artificialintelligence.

Explaining Explanations: An Overview of Interpretability of Machine Learning