提出首个在保护组噪声下的鲁棒AUC公平性方法，保障模型公平性。
Preserving AUC Fairness in Learning with Noisy Protected Groups
- 基于分布鲁棒优化，应对保护组标签噪声问题
- 在表格与图像数据集上显著提升AUC公平性表现
- 适合医疗影像、深度伪造检测等高风险场景
受保护组标签噪声影响，现有AUC公平性优化方法易失效。本文首次提出在保护组存在噪声条件下的鲁棒AUC公平性方法，基于分布鲁棒优化框架，提供理论公平性保证。在多个表格与图像数据集上的实验表明，该方法在不同噪声水平下均优于现有最先进方法，有效缓解了因标签错误导致的公平性偏差。代码已开源：https://github.com/Purdue-M2/AUC_Fairness_with_Noisy_Groups。
原文摘要 · Abstract (English)
The Area Under the ROC Curve (AUC) is a key metric for classification, especially under class imbalance, with growing research focus on optimizing AUC over accuracy in applications like medical image analysis and deepfake detection. This leads to fairness in AUC optimization becoming crucial as biases can impact protected groups. While various fairness mitigation techniques exist, fairness considerations in AUC optimization remain in their early stages, with most research focusing on improving AUC fairness under the assumption of clean protected groups. However, these studies often overlook the impact of noisy protected groups, leading to fairness violations in practice. To address this, we propose the first robust AUC fairness approach under noisy protected groups with fairness theoretical guarantees using distributionally robust optimization. Extensive experiments on tabular and image datasets show that our method outperforms state-of-the-art approaches in preserving AUC fairness. The code is in https://github.com/Purdue-M2/AUC_Fairness_with_Noisy_Groups.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。