通过图模型修正标签噪声，提升分类公平性。
GFLC: Graph-based Fairness-aware Label Correction for Fair Classification
- 构建带鲁棒正则化的图结构，结合置信度与群体平等约束
- 在多个数据集上实现性能与公平性的显著平衡提升
- 适合需要消除训练标签偏差的公平算法开发者
机器学习中的公平性对构建可信赖的AI系统至关重要，尤其在医疗决策和法律判断等社会关键领域。现有研究已揭示大量模型存在不公平结果，亟需更稳健的公平感知方法。然而，用于训练和去偏的技术常依赖含偏见和噪声的标签数据，导致训练数据中的标签偏差影响模型表现，并扭曲测试时的公平性评估。为此，本文提出图基公平感知标签修正方法（GFLC），在修正标签噪声的同时保持数据集中的群体均等性。该方法融合三个核心组件：预测置信度度量、基于里奇流优化图拉普拉斯的图正则化，以及显式的群体均等激励机制。实验表明，所提方法在性能与公平性权衡上显著优于基线，有效缓解了标签偏差对公平性的影响。
原文摘要 · Abstract (English)
Fairness in machine learning (ML) has a critical importance for building trustworthy machine learning system as artificial intelligence (AI) systems increasingly impact various aspects of society, including healthcare decisions and legal judgments. Moreover, numerous studies demonstrate evidence of unfair outcomes in ML and the need for more robust fairness-aware methods. However, the data we use to train and develop debiasing techniques often contains biased and noisy labels. As a result, the label bias in the training data affects model performance and misrepresents the fairness of classifiers during testing. To tackle this problem, our paper presents Graph-based Fairness-aware Label Correction (GFLC), an efficient method for correcting label noise while preserving demographic parity in datasets. In particular, our approach combines three key components: prediction confidence measure, graph-based regularization through Ricci-flow-optimized graph Laplacians, and explicit demographic parity incentives. Our experimental findings show the effectiveness of our proposed approach and show significant improvements in the trade-off between performance and fairness metrics compared to the baseline.
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