Fair Few-shot Learning with Auxiliary Sets
Song Wang Jing Ma Lu Cheng Jundong Li
Recently, there has been a growing interest in developing machine learning
(ML) models that can promote fairness, i.e., eliminating biased predictions
towards certain populations (e.g., individuals from a specific demographic
group). Most existing works learn such models based on well-designed fairness
constraints in optimization. Nevertheless, in many practical ML tasks, only
very few labeled data samples can be collected, which can lead to inferior
fairness performance. This is because existing fairness constraints are
designed to restrict the prediction disparity among different sensitive groups,
but with few samples, it becomes difficult to accurately measure the disparity,
thus rendering ineffective fairness optimization. In this paper, we define the
fairness-aware learning task with limited training samples as the \emph{fair
few-shot learning} problem. To deal with this problem, we devise a novel
framework that accumulates fairness-aware knowledge across different
meta-training tasks and then generalizes the learned knowledge to meta-test
tasks. To compensate for insufficient training samples, we propose an essential
strategy to select and leverage an auxiliary set for each meta-test task. These
auxiliary sets contain several labeled training samples that can enhance the
model performance regarding fairness in meta-test tasks, thereby allowing for
the transfer of learned useful fairness-oriented knowledge to meta-test tasks.
Furthermore, we conduct extensive experiments on three real-world datasets to
validate the superiority of our framework against the state-of-the-art
baselines.