论文标题
公平的积极学习
Fair Active Learning
论文作者
论文摘要
机器学习（ML）越来越多地用于影响社会的高风险应用。因此，ML模型不传播歧视至关重要。在社会应用程序中收集准确的标记数据是具有挑战性且昂贵的。主动学习是一种有前途的方法，可以通过在标签预算内交互查询甲骨文来构建准确的分类器。我们为公平的积极学习设计算法，仔细选择要标记的数据点，以平衡模型的准确性和公平性。具体而言，我们专注于人口统计学奇偶校验 - 一种广泛使用的公平度量。基准数据集的广泛实验证明了我们提出的方法的有效性。
Machine learning (ML) is increasingly being used in high-stakes applications impacting society. Therefore, it is of critical importance that ML models do not propagate discrimination. Collecting accurate labeled data in societal applications is challenging and costly. Active learning is a promising approach to build an accurate classifier by interactively querying an oracle within a labeling budget. We design algorithms for fair active learning that carefully selects data points to be labeled so as to balance model accuracy and fairness. Specifically, we focus on demographic parity - a widely used measure of fairness. Extensive experiments over benchmark datasets demonstrate the effectiveness of our proposed approach.
