The field of explainable artificial intelligence emerged in response to the growing need for more transparent and reliable models. However, using raw features to provide explanations has been disputed in several works lately, advocating for more user-understandable explanations. To address this issue, a wide range of papers proposing Concept-based eXplainable Artificial Intelligence (C-XAI) methods have arisen in recent years. Nevertheless, a unified categorization and precise field definition are still missing. This paper fills the gap by offering a thorough review of C-XAI approaches. We define and identify different concepts and explanation types. We provide a taxonomy identifying nine categories and propose guidelines for selecting a suitable category based on the development context. Additionally, we report common evaluation strategies including metrics, human evaluations and dataset employed, aiming to assist the development of future methods. We believe this survey will serve researchers, practitioners, and domain experts in comprehending and advancing this innovative field.

翻译：可解释人工智能领域应运而生，旨在满足对模型透明性与可靠性日益增长的需求。然而，近期多项研究对使用原始特征进行解释的方法提出质疑，主张采用更易于用户理解的解释形式。为应对这一挑战，近年来涌现了大量关于概念驱动可解释人工智能（C-XAI）方法的研究成果。尽管如此，该领域仍缺乏统一的分类框架和精确的定义。本文通过全面综述C-XAI方法填补了这一空白。我们界定了不同概念与解释类型，提出了包含九大分类的体系结构，并依据开发情境给出了选择适当类别的方法指南。此外，我们系统梳理了常用评估策略（包括评价指标、人工评估及采用的数据集），旨在为未来方法的发展提供支持。本综述将帮助研究者、实践者及领域专家深入理解并推动这一创新领域的发展。