论文标题

FAL-CUR:使用不确定性和代表性的公平积极学习

FAL-CUR: Fair Active Learning using Uncertainty and Representativeness on Fair Clustering

论文作者

Fajri, Ricky, Saxena, Akrati, Pei, Yulong, Pechenizkiy, Mykola

论文摘要

活跃的学习(AL)技术已被证明在降低一系列机器学习任务的数据标记成本方面非常有效。然而,这些方法的一个已知挑战是它们对敏感属性不公平的潜力。尽管最近的方法集中在提高AL的公平性上,但它们倾向于降低模型的准确性。为了解决这个问题,我们提出了一种新颖的策略,使用公平的聚类,不确定性和代表性(FAL-Cur)称为公平的积极学习,以提高AL的公平性。 Fal-Cur通过将公平聚类与采集函数相结合来解决AL中的公平问题,该功能根据其不确定性和代表性得分来确定哪些样本要查询。我们评估了FAL-CUR在四个现实世界数据集上的性能,结果表明,与最佳最先进的方法相比,FAL-CUR在保持稳定的精度得分的同时,公平性提高了15% - 20%。此外,一项消融研究强调了公平聚类在保留公平性和稳定准确性性能中的收购功能中的关键作用。

Active Learning (AL) techniques have proven to be highly effective in reducing data labeling costs across a range of machine learning tasks. Nevertheless, one known challenge of these methods is their potential to introduce unfairness towards sensitive attributes. Although recent approaches have focused on enhancing fairness in AL, they tend to reduce the model's accuracy. To address this issue, we propose a novel strategy, named Fair Active Learning using fair Clustering, Uncertainty, and Representativeness (FAL-CUR), to improve fairness in AL. FAL-CUR tackles the fairness problem in AL by combining fair clustering with an acquisition function that determines which samples to query based on their uncertainty and representativeness scores. We evaluate the performance of FAL-CUR on four real-world datasets, and the results demonstrate that FAL-CUR achieves a 15% - 20% improvement in fairness compared to the best state-of-the-art method in terms of equalized odds while maintaining stable accuracy scores. Furthermore, an ablation study highlights the crucial roles of fair clustering in preserving fairness and the acquisition function in stabilizing the accuracy performance.

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