论文标题
学习认证的个人公平表示
Learning Certified Individually Fair Representations
论文作者
论文摘要
公平表示学习提供了一种有效的方法来实施公平限制，而不会损害下游用户的实用性。如此公平的限制的理想家族，每个家庭都需要类似的待遇，称为个人公平。在这项工作中，我们介绍了第一种方法，该方法使数据消费者能够获得现有和新数据点的个人公平性证书。关键的想法是映射类似的人以关闭潜在的表示，并利用这种潜在的距离来证明个人公平。也就是说，我们的方法使数据生产者能够学习和认证一个表示，其中对于数据点，所有类似的人以$ \ ell_ \ infty $ distance（最多$ε$）学习，从而使消费者可以通过证明其分类器的$ε$ -Robustness来证明个人公平性。我们对五个现实世界数据集的实验评估和几个公平限制证明了我们方法的表现力和可扩展性。
Fair representation learning provides an effective way of enforcing fairness constraints without compromising utility for downstream users. A desirable family of such fairness constraints, each requiring similar treatment for similar individuals, is known as individual fairness. In this work, we introduce the first method that enables data consumers to obtain certificates of individual fairness for existing and new data points. The key idea is to map similar individuals to close latent representations and leverage this latent proximity to certify individual fairness. That is, our method enables the data producer to learn and certify a representation where for a data point all similar individuals are at $\ell_\infty$-distance at most $ε$, thus allowing data consumers to certify individual fairness by proving $ε$-robustness of their classifier. Our experimental evaluation on five real-world datasets and several fairness constraints demonstrates the expressivity and scalability of our approach.
