用公平性约束生成金融数据，让合成数据更公正且有用
FairFinGAN: Fairness-aware Synthetic Financial Data Generation
- 基于WGAN框架，在训练中加入公平性分类器
- 五组真实金融数据测试，公平性显著提升且数据可用性不降
- 适合需要防偏见的金融风控、信贷等场景
金融数据常存在偏差，可能导致自动化系统做出不公平决策。本文提出FairFinGAN，一种基于WGAN的合成金融数据生成框架，通过在训练中引入公平性分类器，直接在生成过程中缓解受保护属性相关的偏差。该方法确保生成数据既公平又保留下游预测任务的实用性。我们在五个真实金融数据集上评估模型，并与现有GAN生成方法对比。实验结果表明，该方法在不显著损失数据效用的前提下，实现了更优的公平性指标，展现出在金融应用中进行偏见感知数据生成的潜力。
原文摘要 · Abstract (English)
Financial datasets often suffer from bias that can lead to unfair decision-making in automated systems. In this work, we propose FairFinGAN, a WGAN-based framework designed to generate synthetic financial data while mitigating bias with respect to the protected attribute. Our approach incorporates fairness constraints directly into the training process through a classifier, ensuring that the synthetic data is both fair and preserves utility for downstream predictive tasks. We evaluate our proposed model on five real-world financial datasets and compare it with existing GAN-based data generation methods. Experimental results show that our approach achieves superior fairness metrics without significant loss in data utility, demonstrating its potential as a tool for bias-aware data generation in financial applications.
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