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 April 29, 2024)
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
Secure .gov websites use HTTPS
A lock (
) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.
Combinatorial Methods for Explainable AI
Published
Author(s)
, , 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)
Pub Type
Conferences
Download Paper
Keywords
artificial intelligence, combinatorial testing, explainable AI, machine learning, t-way testing
Citation
Created October 23, 2020, Updated December 3, 2020