Recent advancements in AI have coincided with ever-increasing efforts in the research community to investigate, classify and evaluate various methods aimed at making AI models explainable. However, most of existing attempts present a method-centric view of eXplainable AI (XAI) which is typically meaningful only for domain experts. There is an apparent lack of a robust qualitative and quantitative performance framework that evaluates the suitability of explanations for different types of users. We survey relevant efforts, and then, propose a unified, inclusive and user-centred taxonomy for XAI based on the principles of General System's Theory, which serves us as a basis for evaluating the appropriateness of XAI approaches for all user types, including both developers and end users.
@article{arxiv.2303.02810,
title = {A System's Approach Taxonomy for User-Centred XAI: A Survey},
author = {Ehsan Emamirad and Pouya Ghiasnezhad Omran and Armin Haller and Shirley Gregor},
journal= {arXiv preprint arXiv:2303.02810},
year = {2023}
}