Fairness testing aims at mitigating unintended discrimination in the decision-making process of data-driven AI systems. Individual discrimination may occur when an AI model makes different decisions for two distinct individuals who are distinguishable solely according to protected attributes, such as age and race. Such instances reveal biased AI behaviour, and are called Individual Discriminatory Instances (IDIs). In this paper, we propose an approach for the selection of the initial seeds to generate IDIs for fairness testing. Previous studies mainly used random initial seeds to this end. However this phase is crucial, as these seeds are the basis of the follow-up IDIs generation. We dubbed our proposed seed selection approach I&D. It generates a large number of initial IDIs exhibiting a great diversity, aiming at improving the overall performance of fairness testing. Our empirical study reveal that I&D is able to produce a larger number of IDIs with respect to four state-of-the-art seed generation approaches, generating 1.68X more IDIs on average. Moreover, we compare the use of I&D to train machine learning models and find that using I&D reduces the number of remaining IDIs by 29% when compared to the state-of-the-art, thus indicating that I&D is effective for improving model fairness
翻译:公平性测试旨在减轻数据驱动的AI系统决策过程中无意中的歧视。当AI模式对两个不同的个人做出不同的决定时,个人歧视可能会发生,因为这两个不同的个人完全根据年龄和种族等受保护的属性而区分,这些例子显示有偏见的AI行为,被称为个人歧视案例。在本文中,我们提出选择初始种子的方法,为公平性测试产生独立数据测试。以前的研究主要为此使用随机初始种子。然而,这一阶段至关重要,因为这些种子是后续的 IDI 一代的基础。我们称我们提议的种子选择方法I&D为ID。它产生了大量显示巨大多样性的初始IDI,目的是改善公平性测试的总体绩效。我们的经验研究表明,IDD能够产生更多关于四种最先进的种子生成方法的IDI,平均产生1.68X更多的国际数据。此外,我们比较了使用I&D来培训机器学习模型的用途。我们发现,使用I&D来减少其余的ID数量,从而展示了极大的多样性测试。我们的经验研究表明,在将I&D的可靠性加以改进的时候,将ID的数值降低29 %。