Fairness-Aware Active Online Learning with Changing Environments

In real-world applications, data-driven classifiers often grapple with a three-pronged challenge: data arrives in a continuous stream, most data in the wild are often unlabeled, and there is a critical need to maintain fairness in predictions across different sub-groups. Existing methods falter when addressing all these three factors concurrently. This work tackles this challenge by addressing a novel paradigm: Fairness-Aware Active Online Learning. We introduce a simple yet effective approach - FACTION, which actively selects the most crucial data points for labeling, going beyond traditional methods by considering both model uncertainty (epistemic uncertainty) and a newly introduced fairness notion derived from this very uncertainty. Additionally, FACTION leverages a system adept at identifying out-of-distribution samples within online learning environ-ments. Extensive evaluations on real-world datasets, coupled with theoretical analysis, demonstrate FACTION's effectiveness in handling this complex challenge. Our model demonstrably outperforms relevant baselines adapted for this new setting.