A Review of Taxonomies of Explainable Artificial Intelligence (XAI) Methods

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Speith, Timo:
A Review of Taxonomies of Explainable Artificial Intelligence (XAI) Methods.
In: Isbell, Charles ; Lazar, Seth ; Oh, Alice ; Xiang, Alice (Hrsg.): 2022 5th ACM Conference on Fairness, Accountability, and Transparency. - New York, NY, USA : Association for Computing Machinery , 2022 . - S. 2239-2250
ISBN 978-1-4503-9352-2
DOI: https://doi.org/10.1145/3531146.3534639

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Abstract

The recent surge in publications related to explainable artificial intelligence (XAI) has led to an almost insurmountable wall if one wants to get started or stay up to date with XAI. For this reason, articles and reviews that present taxonomies of XAI methods seem to be a welcomed way to get an overview of the field. Building on this idea, there is currently a trend of producing such taxonomies, leading to several competing approaches to construct them. In this paper, we will review recent approaches to constructing taxonomies of XAI methods and discuss general challenges concerning them as well as their individual advantages and limitations. Our review is intended to help scholars be aware of challenges current taxonomies face. As we will argue, when charting the field of XAI, it may not be sufficient to rely on one of the approaches we found. To amend this problem, we will propose and discuss three possible solutions: a new taxonomy that incorporates the reviewed ones, a database of XAI methods, and a decision tree to help choose fitting methods.