Search-Based Fairness Testing: An Overview

Hussaini Mamman, Shuib Basri, Abdullateef Oluwaqbemiga Balogun, Abdullahi Abubakar Imam, Ganesh Kumar +1 more
11/10/2023
cs.AI

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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.

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Cite this paper

@article{mamman2023searchbased,
  title  = {Search-Based Fairness Testing: An Overview},
  author = {Hussaini Mamman and Shuib Basri and Abdullateef Oluwaqbemiga Balogun and Abdullahi Abubakar Imam and Ganesh Kumar and Luiz Fernando Capretz},
  year   = {2023},
  eprint = {2311.06175},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2311.06175v1}
}

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