研究信贷算法公平性与利润的权衡，发现不使用敏感属性反而更优。
Algorithmic Tradeoffs in Fair Lending: Profitability, Compliance, and Long-Term Impact
- 通过模拟真实借贷数据，比较不同公平性约束对利润的影响。
- 等机会约束比群体平等带来的利润损失更小，但去除敏感属性效果最佳。
- 揭示了在特定经济条件下公平借贷可盈利，适合金融机构设计伦理算法。
随着金融机构越来越多地依赖机器学习模型自动化信贷决策，算法公平性问题日益突出。本文探讨了强制实施公平性约束（如群体平等或等机会）与最大化贷款人利润之间的权衡。通过基于反映真实借贷模式的合成数据模拟，我们量化了不同公平性干预措施对利润率和违约率的影响。结果表明，等机会约束通常比群体平等带来的利润损失更小；但令人意外的是，从模型中移除受保护属性（公平性盲法）在公平性和盈利能力指标上均优于显式公平性干预。我们进一步识别出公平信贷在特定经济条件下可实现盈利，并分析了导致不公平的特征具体驱动因素。这些发现为平衡伦理考量与商业目标的信贷算法设计提供了实用指导。
原文摘要 · Abstract (English)
As financial institutions increasingly rely on machine learning models to automate lending decisions, concerns about algorithmic fairness have risen. This paper explores the tradeoff between enforcing fairness constraints (such as demographic parity or equal opportunity) and maximizing lender profitability. Through simulations on synthetic data that reflects real-world lending patterns, we quantify how different fairness interventions impact profit margins and default rates. Our results demonstrate that equal opportunity constraints typically impose lower profit costs than demographic parity, but surprisingly, removing protected attributes from the model (fairness through unawareness) outperforms explicit fairness interventions in both fairness and profitability metrics. We further identify the specific economic conditions under which fair lending becomes profitable and analyze the feature-specific drivers of unfairness. These findings offer practical guidance for designing lending algorithms that balance ethical considerations with business objectives.
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