用零样本预测选数据，高效消除标签偏见提升公平性
Navigating Towards Fairness with Data Selection
- 用零样本预测器模拟干净数据训练，无需额外验证集
- 在多个数据集上显著降低标签偏见，提升模型公平性
- 不改模型结构，适配多模态数据，适合大规模场景
机器学习算法常难以消除由不可靠标签引发的数据偏见，这严重影响公平性。现有公平性方法多需修改模型或干预训练过程，难以适应大规模数据集。为此，本文提出一种数据选择方法，通过零样本预测器作为代理模型，在无额外验证集的情况下模拟清洁数据训练，结合同行预测确保代理模型公平性。该方法不改变分类器架构，具备模态无关性，实验表明其在多个数据集上均能有效缓解标签偏见、提升公平性，且计算高效。
原文摘要 · Abstract (English)
Machine learning algorithms often struggle to eliminate inherent data biases, particularly those arising from unreliable labels, which poses a significant challenge in ensuring fairness. Existing fairness techniques that address label bias typically involve modifying models and intervening in the training process, but these lack flexibility for large-scale datasets. To address this limitation, we introduce a data selection method designed to efficiently and flexibly mitigate label bias, tailored to more practical needs. Our approach utilizes a zero-shot predictor as a proxy model that simulates training on a clean holdout set. This strategy, supported by peer predictions, ensures the fairness of the proxy model and eliminates the need for an additional holdout set, which is a common requirement in previous methods. Without altering the classifier's architecture, our modality-agnostic method effectively selects appropriate training data and has proven efficient and effective in handling label bias and improving fairness across diverse datasets in experimental evaluations.
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