黑人贷款很重要：为打击子群体歧视而进行的分配稳健公平
Black Loans Matter: Distributionally Robust Fairness for Fighting Subgroup Discrimination
摘要：
当今贷款的算法公平性依赖于用于监测受保护群体之间统计平等的群体公平性指标。这种方法很容易受到代理人的分组歧视，给贷款人带来法律和声誉损害的重大风险，并对借款人造成明显的不公平结果。实际的挑战来自许多可能的组合和保护基团的子集。我们在美国历史和残余种族主义的背景下激发了这个问题，污染了所有可用的训练数据，并提高了公众对算法偏见的敏感性。我们回顾了目前的监管合规协议的公平贷款，并讨论其局限性相对于国家的最先进的公平的方法可能提供的贡献。我们提出了一个解决子群歧视的解决方案，同时坚持现有的组公平性的要求，从最近的发展，在个人的公平性方法和相应的公平度量学习算法。
英文摘要：
Algorithmic fairness in lending today relies on group fairness metrics for monitoring statistical parity across protected groups. This approach is vulnerable to subgroup discrimination by proxy, carrying significant risks of legal and reputational damage for lenders and blatantly unfair outcomes for borrowers. Practical challenges arise from the many possible combinations and subsets of protected groups. We motivate this problem against the backdrop of historical and residual racism in the United States polluting all available training data and raising public sensitivity to algorithimic bias. We review the current regulatory compliance protocols for fairness in lending and discuss their limitations relative to the contributions state-of-the-art fairness methods may afford. We propose a solution for addressing subgroup discrimination, while adhering to existing group fairness requirements, from recent developments in individual fairness methods and corresponding fair metric learning algorithms.
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