一句话：提出结合数据有效性的个体公平性测试框架 Ift-v，并初步表明现有方法找到的歧视实例许多可能无效。
个体公平性测试（IFT）旨在分类器中寻找歧视性实例，但现有框架通常只关注能否找到实例，而忽略这些实例是否满足数据有效性。本文将数据有效性概念引入 IFT，提出 Ift-v 框架，并为其建立初步基础、论证可行性。作者进一步使用 Ift-v 进行初步评估，结果显示当前最先进 IFT 算法检测出的许多歧视性实例可能被认为是无效的。这些发现促使重新思考当前 IFT 框架：应从仅关注发现歧视性实例，转向同时考虑有效歧视性实例。
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.