Toward Individual Fairness Testing with Data Validity

Individual fairness testing (Ift) is a framework to find discriminatory instances within a given classifier. In this paper, we show our idea of a Ift framework, that integrates the notion of data validity, termed "Individual Fairness Testing with Data Validity (Ift-v)". We develop a solid foundation of Ift-v and demonstrate the feasibility of Ift-v. Our preliminary evaluation with Ift-v reveals the possibility that many of discriminatory instances detected by state-of-the-art Ift algorithms are considered invalid. These findings prompt a re-think of the current Ift framework, suggesting a transition from solely focusing on the discovery of discriminatory instances to the consideration of valid ones.