论文标题
私人学习有多不公平？
How unfair is private learning ?
论文作者
论文摘要
随着机器学习算法在关键决策过程中的敏感数据上部署，它们也越来越私人和公平变得越来越重要。在本文中，我们表明，当数据具有长尾结构时，不可能构建既私有的学习算法，又可以使少数族裔亚群的准确性更高。我们进一步表明，即使有严格的隐私要求，放松的整体准确性也会导致良好的公平性。为了证实我们在实践中的理论结果，我们使用各种综合，视觉（CIFAR10和CELEBA）以及表格（法学院）数据集和学习算法提供了一系列实验结果。
As machine learning algorithms are deployed on sensitive data in critical decision making processes, it is becoming increasingly important that they are also private and fair. In this paper, we show that, when the data has a long-tailed structure, it is not possible to build accurate learning algorithms that are both private and results in higher accuracy on minority subpopulations. We further show that relaxing overall accuracy can lead to good fairness even with strict privacy requirements. To corroborate our theoretical results in practice, we provide an extensive set of experimental results using a variety of synthetic, vision (CIFAR10 and CelebA), and tabular (Law School) datasets and learning algorithms.
