AI 中文总结
该研究针对信贷决策中主流公平准则的隐性不平等问题，提出以努力为核心的公平框架，用抵押贷款数据验证了其可缩小努力差距，为信贷公平提供了新路径。
AI 中文摘要
算法信用评分必须满足公平性和可解释性要求，但主流的预测公平性准则仅评估决策点的结果，因此可能忽略被拒申请人在未来获得批准时是否面临不平等负担，我们将这一现象称为“隐性不平等”。我们开发了以努力为核心的框架，该框架将申请人的努力定义为跨越批准边界所需可行变化的最小加权成本；该框架区分与特征无关的行动和通过因果模型传播的加性结构转变，并通过比较受保护群体间的平均最小努力来定义公平性。我们推导了一般可微分类器的易处理局部表达式，以及逻辑回归的精确表达式，将其嵌入处理中的公平性目标，并对投资组合信用风险的变化进行了界定。同一优化还产生了可操作的批准路径。使用包含连续和离散特征的抵押贷款数据，我们发现即使满足标准的预测公平性准则，被拒的女性申请人仍需要付出更大的努力。与特征无关的正则化在预测发生适度变化的情况下，可将努力差距缩小50%以上；在测试的正惩罚权重下，因果正则化可使努力差距缩小90%以上，但会带来更大的预测和风险回报权衡。与特征无关的正则化下，预期和意外损失基本保持稳定，而因果正则化下则会增加；风险调整后资本回报率（RAROC）会下降但仍为正。这些结果表明，努力公平性是对预测公平性的补充，可揭示和缓解未来信贷获取的隐性障碍，同时明确相关的运营权衡。
英文摘要
Algorithmic credit scoring must satisfy fairness and explanation requirements, yet prevailing predictive-parity criteria assess only outcomes at the decision point. They can therefore overlook whether rejected applicants face unequal burdens in reaching future approval, a phenomenon we call masked inequality. We develop an effort-centric framework that measures an applicant's effort as the minimum weighted cost of feasible changes required to cross the approval boundary. The framework distinguishes feature-independent actions from additive structural shifts that propagate through a causal model and defines parity by comparing average minimum effort across protected groups. We derive tractable local expressions for general differentiable classifiers and exact expressions for logistic regression, embed them in an in-processing fairness objective, and bound changes in portfolio credit risk. The same optimisation yields actionable pathways to approval. Using mortgage data with continuous and discrete features, we find that rejected female applicants require greater effort even when standard predictive-parity criteria are satisfied. Feature-independent regularisation reduces the effort gap by more than 50\% with modest predictive changes. Causal regularisation yields reductions above 90\% at the tested positive penalty weights, but with larger predictive and risk-return trade-offs. Expected and unexpected losses remain broadly stable under feature-independent regularisation and increase under causal regularisation; RAROC declines but remains positive. These results show that effort parity complements predictive fairness by revealing and mitigating hidden barriers to future credit access while making the associated operational trade-offs explicit.
