TY  - CONF
ID  - scholars18950
SP  - 89
TI  - Search-Based Fairness Testing: An Overview
KW  - 'current; Artificial intelligence systems; Ethical concerns; Fairness; Fairness testing; Search-based; Search-based fairness testing; Search-based testing; Societal concerns; Testing method
N1  - 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
N2  - 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.
AV  - none
EP  - 94
A1  - Mamman, H.
A1  - Basri, S.
A1  - Balogun, A.O.
A1  - Imam, A.A.
A1  - Kumar, G.
A1  - Capretz, L.F.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184850232&doi=10.1109%2fICOCO59262.2023.10397906&partnerID=40&md5=e62d07415803b54dc633df6f60f9ad7a
PB  - Institute of Electrical and Electronics Engineers Inc.
SN  - 9798350302684
Y1  - 2023///
ER  -