  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.
