论文标题
通过生成有效的初始个人歧视实例来增强公平测试
Enhanced Fairness Testing via Generating Effective Initial Individual Discriminatory Instances
论文作者
论文摘要
公平测试旨在减轻数据驱动的AI系统决策过程中的意外歧视。当AI模型为两个独特的人做出不同的决策时，可能会发生个人歧视，这些个人仅根据受保护的属性（例如年龄和种族）来区分。这样的实例揭示了偏见的AI行为，被称为个人歧视实例（IDI）。 在本文中，我们提出了一种选择初始种子以生成IDI进行公平测试的方法。先前的研究主要使用随机的初始种子来实现这一目标。但是，这个阶段至关重要，因为这些种子是后续IDIS生成的基础。我们称我们提出的种子选择方法I＆D。它产生了大量的初始IDI，表现出巨大的多样性，旨在提高公平测试的整体性能。 我们的实证研究表明，I＆D能够相对于四种最先进的种子生成方法产生更多的IDI，平均产生1.68倍的IDI。此外，我们比较I＆D在训练机器学习模型中的使用，并发现与最先进的ART相比，使用I＆D将剩余IDI的数量减少了29％，因此表明I＆D有效地改善了模型公平。
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
