算法公平性-准确率前沿的推断
Inference for an Algorithmic Fairness-Accuracy Frontier
AI总结：
针对有限数据下算法公平性与准确率的权衡评估问题，提出公平性-准确率前沿的去偏机器学习估计量及推断方法，经模拟和医院护理管理算法验证，可得到双维度均有提升的前沿替代算法。
摘要：
算法正越来越多地被用于辅助高风险决策。然而，其预测能力在不同人口子群体间往往存在系统性差异。为利用有限数据评估公平性与准确率之间的权衡关系，我们针对Liang、Lu、Mu和Okumura（2024）提出的公平性-准确率前沿，提出了一种去偏机器学习估计量。我们推导了其渐近分布，并提出了用于检验公平性文献中关键假设的推断方法，例如（i）在训练算法时排除群体身份信息是否为最优选择，以及（ii）对于给定算法是否存在歧视性更低的替代方案。此外，我们构建了给定算法与前沿上最公平点之间距离的估计量，并刻画了其渐近分布。通过蒙特卡洛模拟，我们评估了所提推断方法的有限样本性能。我们将该框架应用于重新评估医院护理管理中使用的算法，结果表明我们的方法能得到位于公平性-准确率前沿上的替代算法，在两个维度上均有所提升。
英文摘要：
Algorithms are increasingly used to aid with high-stakes decision making. Yet, their predictive ability frequently exhibits systematic variation across population subgroups. To assess the trade-off between fairness and accuracy using finite data, we propose a debiased machine learning estimator for the fairness-accuracy frontier introduced by Liang, Lu, Mu, and Okumura (2024). We derive its asymptotic distribution and propose inference methods to test key hypotheses in the fairness literature, such as (i) whether excluding group identity from use in training the algorithm is optimal and (ii) whether there are less discriminatory alternatives to a given algorithm. In addition, we construct an estimator for the distance between a given algorithm and the fairest point on the frontier, and characterize its asymptotic distribution. Using Monte Carlo simulations, we evaluate the finite-sample performance of our inference methods. We apply our framework to re-evaluate algorithms used in hospital care management and show that our approach yields alternative algorithms that lie on the fairness-accuracy frontier, offering improvements along both dimensions.
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