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Combinatorial Methods for Explainable AI



David R. Kuhn, Raghu N. Kacker, Yu Lei, Dimitris Simos


This paper introduces an approach to producing explanations or justifications of decisions made by artificial intelligence and machine learning (AI/ML) systems, using methods derived from fault location in combinatorial testing. We use a conceptually simple scheme to make it easy to justify classification decisions: identifying combinations of features that are present in members of the identified class and absent or rare in non-members. The method has been implemented in a prototype tool, and examples of its application are given.
Conference Dates
October 24-28, 2020
Conference Location
Conference Title
IEEE International Conference on Software Testing, Verification and Validation (ICST)


artificial intelligence, combinatorial testing, explainable AI, machine learning, t-way testing


Kuhn, D. , Kacker, R. , Lei, Y. and Simos, D. (2020), Combinatorial Methods for Explainable AI, IEEE International Conference on Software Testing, Verification and Validation (ICST), Porto, -1, [online], (Accessed July 15, 2024)


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Created October 23, 2020, Updated December 3, 2020