Explainable AI (XAI) is paramount in industry-grade AI; however existing methods fail to address this necessity, in part due to a lack of standardisation of explainability methods. The purpose of this paper is to offer a perspective on the current state of the area of explainability, and to provide novel definitions for Explainability and Interpretability to begin standardising this area of research. To do so, we provide an overview of the literature on explainability, and of the existing methods that are already implemented. Finally, we offer a tentative taxonomy of the different explainability methods, opening the door to future research.
@article{arxiv.2010.11273,
title = {The Need for Standardized Explainability},
author = {Othman Benchekroun and Adel Rahimi and Qini Zhang and Tetiana Kodliuk},
journal= {arXiv preprint arXiv:2010.11273},
year = {2020}
}
Comments
Accepted in 2nd ICML 2020 Workshop on Human in the Loop Learning