Fair Few-shot Learning with Auxiliary Sets
This addresses fairness degradation in few-shot scenarios, which is a critical issue for deploying equitable ML in data-scarce applications, though it appears incremental as it builds on existing meta-learning and fairness constraint methods.
The paper tackles the problem of maintaining fairness in machine learning models when only few labeled samples are available, by proposing a meta-learning framework that transfers fairness-aware knowledge from meta-training tasks to meta-test tasks using auxiliary sets, achieving superior fairness performance compared to state-of-the-art baselines on three real-world datasets.
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.