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

TL;DR

FAL-CUR introduces a novel active learning approach that combines fair clustering with uncertainty and representativeness measures to enhance fairness without sacrificing accuracy across multiple datasets.

Contribution

It proposes a new fair active learning method that effectively balances fairness and accuracy using fair clustering and a combined acquisition strategy.

Findings

Achieves 15-20% improvement in fairness over state-of-the-art methods.

Maintains stable accuracy scores while improving fairness.

Highlights the importance of fair clustering and acquisition functions through ablation studies.

Abstract

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.