Argumentative XAI: A Survey

TL;DR

This survey reviews argumentation-based explainable AI methods, highlighting their reasoning frameworks, explanation types, and delivery methods, and discusses future research directions in this growing field.

Contribution

It provides a comprehensive overview of computational argumentation approaches in XAI, detailing various models, explanation types, and frameworks, and proposes a future research roadmap.

Findings

Argumentation models align well with explanation activities.

Various argumentation frameworks are used for explanations.

The survey identifies key challenges and future directions in argumentation-based XAI.

Abstract

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.