  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.
