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 constitutes an "explanation." This has caused a disconnect between the explanations that systems produce in service of explainable Artificial Intelligence (XAI) and those explanations that users and other audiences actually need, which should be defined by the full spectrum of functional roles, audiences, and capabilities for explanation. In this paper, we explore the features of explanations and how to use those features in evaluating their utility. We focus on the requirements for explanations defined by their functional role, the knowledge states of users who are trying to understand them, and the availability of the information needed to generate them. Further, we discuss the risk of XAI enabling trust in systems without establishing their trustworthiness and define a critical next step for the field of XAI to establish metrics to guide and ground the utility of system-generated explanations.
翻译:人们广泛同意,人工智能系统,特别是那些使用机器学习(ML)的系统,应该能够“解释”它们的行为。不幸的是,对于什么是“解释”的问题,几乎没有一致的意见。这造成了系统为可解释的人工智能(XAI)服务而提供的解释与用户和其他受众实际需要的解释之间的脱节,这些解释应该由各种功能作用、受众和解释能力来界定。在本文件中,我们探讨了解释的特点以及如何使用这些特征来评价它们的效用。我们侧重于由它们的职能作用界定的解释要求、试图理解它们的用户的知识状况以及产生这些解释所需的信息的可得性。此外,我们讨论了XAI在建立对系统的信任而不确立其可信任性的情况下,为XAI领域制定指导并确立系统生成的解释的效用的衡量标准而下一个关键步骤的风险。