研究公平性机器学习在信贷评分中的应用效果
An Experimental Study on Fairness-aware Machine Learning for Credit Scoring Problems
- 对比多种公平性模型与传统分类模型在信贷数据上的表现
- 公平性模型在准确率与公平性间取得更好平衡
- 适合关注算法公平性的金融从业者和研究者
信贷评分的数字化已成为金融机构和商业银行在数字化转型时代的关键需求。机器学习技术被广泛用于评估客户信用状况，但其预测结果可能对种族、性别等受保护属性产生偏见。尽管已有大量公平性感知的机器学习模型和衡量方法提出，其在信贷评分场景下的表现仍缺乏系统研究。本文通过全面实验，考察了金融数据集、预测模型和公平性度量等关键因素。在多个常用金融数据集上，对公平性感知模型和公平性度量进行了详细评估。实验结果表明，相较于传统分类模型，公平性感知模型在预测准确率与公平性之间实现了更优平衡。
原文摘要 · Abstract (English)
The digitalization of credit scoring has become essential for financial institutions and commercial banks, especially in the era of digital transformation. Machine learning techniques are commonly used to evaluate customers' creditworthiness. However, the predicted outcomes of machine learning models can be biased toward protected attributes, such as race or gender. Numerous fairness-aware machine learning models and fairness measures have been proposed. Nevertheless, their performance in the context of credit scoring has not been thoroughly investigated. In this paper, we present a comprehensive experimental study of fairness-aware machine learning in credit scoring. The study explores key aspects of credit scoring, including financial datasets, predictive models, and fairness measures. We also provide a detailed evaluation of fairness-aware predictive models and fairness measures on widely used financial datasets. The experimental results show that fairness-aware models achieve a better balance between predictive accuracy and fairness compared to traditional classification models.
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