Individual fairness testing (IFT) is a framework to find discriminatory instances within a given classifier. In this paper, we show our vision 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 dis- criminatory 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.