A core assumption of explainable AI systems is that explanations change what users know, thereby enabling them to act within their complex socio-technical environments. Despite the centrality of action, explanations are often organized and evaluated based on technical aspects. Prior work varies widely in the connections it traces between information provided in explanations and resulting user actions. An important first step in centering action in evaluations is understanding what the XAI community collectively recognizes as the range of information that explanations can present and what actions are associated with them. In this paper, we present our framework, which maps prior work on information presented in explanations and user action, and we discuss the gaps we uncovered about the information presented to users.

翻译：可解释AI系统的一个核心假设是，解释能够改变用户的知识状态，从而使其能够在复杂的社会技术环境中采取行动。尽管行动至关重要，但解释往往基于技术层面进行组织和评估。现有研究在解释提供的信息与用户后续行为之间建立的关联程度差异很大。将行动置于评价中心的首要步骤，是理解可解释AI（XAI）社群共同认可的解释所能呈现的信息范围，以及与之相关的行动类型。本文提出了一个框架，系统梳理了关于解释中包含的信息与用户行为的相关研究，并探讨了我们在用户可获得信息方面发现的空白。