Fair Few-shot Learning with Auxiliary Sets

TL;DR

This paper introduces a novel fair few-shot learning framework that leverages auxiliary sets and meta-learning to improve fairness in scenarios with limited labeled data, outperforming existing methods.

Contribution

The paper proposes a new framework that uses auxiliary sets and meta-learning to enhance fairness in few-shot learning tasks with limited data.

Findings

Outperforms state-of-the-art baselines on three real-world datasets.

Effectively transfers fairness-aware knowledge across tasks.

Improves fairness metrics in low-data regimes.

Abstract

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.