NOTICE: Due to a lapse in annual appropriations, most of this website is not being updated. Learn more.

Form submissions will still be accepted but will not receive responses at this time. Sections of this site for programs using non-appropriated funds (such as NVLAP) or those that are excepted from the shutdown (such as CHIPS and NVD) will continue to be updated.

U.S. flag

An official website of the United States government

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.

PUBLICATIONS

Combinatorial Methods for Explainable AI

Published

Author(s)

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)
Pub Type
Conferences

Download Paper

https://doi.org/10.1109/ICSTW50294.2020.00037
Local Download

Keywords

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

Citation

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 16, 2025)

Issues

If you have any questions about this publication or are having problems accessing it, please contact [email protected].

Created October 23, 2020, Updated December 3, 2020
Was this page helpful?