Mamman, H. and Basri, S. and Balogun, A.O. and Imam, A.A. and Kumar, G. and Capretz, L.F. (2023) Search-Based Fairness Testing: An Overview. In: UNSPECIFIED.
Full text not available from this repository.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, emphasising 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. © 2023 IEEE.
Item Type: | Conference or Workshop Item (UNSPECIFIED) |
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Additional Information: | cited By 0; Conference of 2023 IEEE International Conference on Computing, ICOCO 2023 ; Conference Date: 9 October 2023 Through 12 October 2023; Conference Code:196872 |
Uncontrolled Keywords: | 'current; Artificial intelligence systems; Ethical concerns; Fairness; Fairness testing; Search-based; Search-based fairness testing; Search-based testing; Societal concerns; Testing method |
Depositing User: | Mr Ahmad Suhairi UTP |
Date Deposited: | 04 Jun 2024 14:11 |
Last Modified: | 04 Jun 2024 14:11 |
URI: | https://khub.utp.edu.my/scholars/id/eprint/18950 |