Recent advancements in AI have coincided with ever-increasing efforts in the research community to investigate, classify and evaluate various methods aimed at making AI models explainable. However, most of existing attempts present a method-centric view of eXplainable AI (XAI) which is typically meaningful only for domain experts. There is an apparent lack of a robust qualitative and quantitative performance framework that evaluates the suitability of explanations for different types of users. We survey relevant efforts, and then, propose a unified, inclusive and user-centred taxonomy for XAI based on the principles of General System's Theory, which serves us as a basis for evaluating the appropriateness of XAI approaches for all user types, including both developers and end users.

翻译：近年来，人工智能的进步与研究界在调查、分类和评估各种旨在使AI模型可解释的方法方面不断加大努力相吻合。然而，现有的大多数尝试都呈现出一种以方法为中心的可解释人工智能（XAI）视角，通常仅对领域专家有意义。目前明显缺乏一个稳健的定性和定量性能框架来评估不同用户类型对解释的适用性。我们综述了相关研究，然后基于一般系统论原理，提出了一种统一、包容且以用户为中心的XAI分类法，这为我们评估所有用户类型（包括开发者和最终用户）的XAI方法适用性提供了基础。