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There is broad agreement that Artificial Intelligence (AI) systems,particularly those using Machine Learning (ML), should be able to "explain"their behavior. Unfortunately, there is little agreement as to what constitutesan "explanation." This has caused a disconnect between the explanations thatsystems produce in service of explainable Artificial Intelligence (XAI) andthose explanations that users and other audiences actually need, which shouldbe defined by the full spectrum of functional roles, audiences, andcapabilities for explanation. In this paper, we explore the features ofexplanations and how to use those features in evaluating their utility. Wefocus on the requirements for explanations defined by their functional role,the knowledge states of users who are trying to understand them, and theavailability of the information needed to generate them. Further, we discussthe risk of XAI enabling trust in systems without establishing theirtrustworthiness and define a critical next step for the field of XAI toestablish metrics to guide and ground the utility of system-generatedexplanations.

"Explanation" is Not a Technical Term: The Problem of Ambiguity in XAI