Kampel, L.
, Simos, D.
, Kuhn, D.
and Kacker, R.
(2021),
An exploration of combinatorial testing-based approaches to fault localization for explainable AI, Annals of Mathematics and Artificial Intelligence, [online], https://doi.org/10.1007/s10472-021-09772-0
(Accessed October 15, 2025)
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An exploration of combinatorial testing-based approaches to fault localization for explainable AI
Published
Author(s)
Ludwig Kampel, Dimitris Simos, ,
Abstract
We briefly review properties of explainable AI proposed by various researchers. We take a structural approach to the problem of explainable AI, examine the feasibility of these aspects and extend them where appropriate. Afterwards, we review combinatorial methods for explainable AI which are based on combinatorial testing-based approaches to fault localization. Last, we view the combinatorial methods for explainable AI through the lens provided by the properties of explainable AI that are elaborated in this work. We pose resulting research questions that need to be answered and point towards possible solutions, which involve a hypothesis about a potential parallel between software testing, human cognition and brain capacity.Citation
Annals of Mathematics and Artificial Intelligence
Volume
90
Pub Type
Journals
Download Paper
Keywords
AI, explainable AI, combinatorial testing, fault localization
Citation
Issues
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Created September 20, 2021, Updated August 7, 2024