Search-Based Fairness Testing: An Overview

Authors

Hussaini Mamman, Universiti Teknologi PetronasFollow
Shuib Basri, Universiti Teknologi PetronasFollow
Abdullateef Balogun, Universiti Teknologi PetronasFollow
Abdullahi Abubakar Imam, Universiti Brunei DarussalamFollow
Ganesh Kumar, Universiti Teknologi PetronasFollow
Luiz Fernando Capretz, University of Western OntarioFollow

Document Type

Article

Publication Date

10-30-2023

First Page

89

Last Page

94

Abstract

Artificial Intelligence (AI) has demonstrated remarkable capabilities in domains such as recruitment, finance, healthcare, and the judiciary. However, biases in AI systems raise ethical and societal concerns, emphasizing the need for effective fairness testing methods. This paper reviews current research on fairness testing, particularly its application through search-based testing. Our analysis highlights progress and identifies areas of improvement in addressing AI systems’ biases. Future research should focus on leveraging established search-based testing methodologies for fairness testing.

Citation of this paper:

1. Mamman H., Basri S., Balogun A.O., Iman A.A., Kumar G., Capretz L.F., Search-Based Fairness Testing: Overview, IEEE International Conference on Computing (ICOCO 2023), Langkawi Island, Malaysia, pp. 89-94, October 2023.