Previous research in Explainable Artificial Intelligence (XAI) suggests that a main aim of explainability approaches is to satisfy specific interests, goals, expectations, needs, and demands regarding artificial systems (we call these “stakeholders' desiderata”) in a variety of contexts. However, the literature on XAI is vast, spreads out across multiple largely disconnected disciplines, and it often remains unclear how explainability approaches are supposed to achieve the goal of satisfying stakeholders' desiderata. This paper discusses the main classes of stakeholders calling for explainability of artificial systems and reviews their desiderata. We provide a model that explicitly spells out the main concepts and relations necessary to consider and investigate when evaluating, adjusting, choosing, and developing explainability approaches that aim to satisfy stakeholders' desiderata. This model can serve researchers from the variety of different disciplines involved in XAI as a common ground. It emphasizes where there is interdisciplinary potential in the evaluation and the development of explainability approaches.

先前在可解释人工智能（XAI）中的研究表明，可解释性方法的主要目的是在各种情况下满足有关人工智能系统（我们称为“利益相关者的需求”）的特定利益，目标，期望，需求和要求。但是，有关XAI的文献非常广泛，分布在多个相互独立的学科上，并且通常仍不清楚如何可解释性方法应该达到满足利益相关者需求的目标。本文讨论了要求人工系统具有可解释性的主要利益相关者类别，并回顾了他们的愿望。我们提供了一个模型，明确阐明了在评估，调整，选择和开发旨在满足利益相关者需求的可解释性方法时要考虑和调查的主要概念和关系。该模型可以为XAI所涉及的不同学科的研究人员提供服务，这是一个共同点。它强调了在评估和发展可解释性方法方面具有跨学科潜力的地方。