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.
October 24-28, 2020
IEEE International Conference on Software Testing, Verification and Validation (ICST)
, Kacker, R.
, Lei, Y.
and Simos, D.
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 July 31, 2021)