Search-Based Fairness Testing: An Overview
This is an incremental overview paper that addresses the problem of AI bias for stakeholders in ethical and societal contexts.
The paper reviews existing research on fairness testing for AI systems, focusing on the application of search-based testing methods to address biases in domains like recruitment and healthcare, and identifies areas for future improvement.
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.