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David R. Kuhn, Raghu N. Kacker, Yu Lei, Dimitris Simos
Abstract
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
Porto
Conference Title
IEEE International Conference on Software Testing, Verification and Validation (ICST)
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], https://doi.org/10.1109/ICSTW50294.2020.00037
(Accessed October 9, 2025)