A Survey of Explainable AI and Proposal for a Discipline of Explanation Engineering
This is an incremental survey that synthesizes existing XAI knowledge and introduces a framework for practitioners to enhance AI transparency.
The paper surveys Explainable AI (XAI), analyzing definitions, methods, and applications in domains like finance and healthcare, and proposes a new discipline called Explanation Engineering for systematic design of explainability in AI systems.
In this survey paper, we deep dive into the field of Explainable Artificial Intelligence (XAI). After introducing the scope of this paper, we start by discussing what an "explanation" really is. We then move on to discuss some of the existing approaches to XAI and build a taxonomy of the most popular methods. Next, we also look at a few applications of these and other XAI techniques in four primary domains: finance, autonomous driving, healthcare and manufacturing. We end by introducing a promising discipline, "Explanation Engineering," which includes a systematic approach for designing explainability into AI systems.