  Explainable AI (XAI) has been investigated for decades and, together with AI
itself, has witnessed unprecedented growth in recent years. Among various
approaches to XAI, argumentative models have been advocated in both the AI and
social science literature, as their dialectical nature appears to match some
basic desirable features of the explanation activity. In this survey we
overview XAI approaches built using methods from the field of computational
argumentation, leveraging its wide array of reasoning abstractions and
explanation delivery methods. We overview the literature focusing on different
types of explanation (intrinsic and post-hoc), different models with which
argumentation-based explanations are deployed, different forms of delivery, and
different argumentation frameworks they use. We also lay out a roadmap for
future work.
