这篇论文想办法解决交叉群体数据少导致的模型歧视，搞了个框架能平衡公平和成本，还告诉你修改多少数据能换多少公平。
该论文针对机器学习模型在种族和性别等交叉敏感属性上表现出的歧视问题，提出了一种扩展的偏差缓解框架。该框架引入覆盖约束，确保训练数据中交叉子群有足够代表性。通过将偏差缓解表述为整数线性规划，量化了公平性代价（最小数据修改成本作为公平容忍度的函数）。在公开数据集上的实验表明，该方法能在保留预测精度的同时，通过容忍小近似偏差提高数据效率，并满足覆盖约束对下游性能的保护。
Data Bias Mitigation under Coverage Constraints & The Price of Fairness
Machine learning models have been shown to exhibit discriminatory outcomes or degraded performance for individuals at the intersection of multiple sensitive attributes, such as race and gender. This stems in part from two interrelated challenges: the lack of principled measures for quantifying bias (potentially intersectional), and insufficient representation of intersectional subgroups in training data. We extend a recent bias mitigation framework to incorporate coverage constraints that enforce sufficient representation across groups, including intersectional subgroups. Since achieving exactly zero bias for all groups may not be data efficient (meaning it may require large amounts of data), our solution trades small approximation errors in bias for greater data efficiency while satisfying coverage constraints. We also formulate bias mitigation as an integer linear program that optimizes over all mitigation strategies, and characterize the price of fairness, the minimum data modification cost, as a function of fairness tolerance. This is essential both for legal compliance, where regulations may mandate specific fairness thresholds, and for data governance, enabling practitioners to make informed trade-offs between bias reduction and data modification (particularly, data purchasing) costs. We evaluate our techniques on publicly available datasets, demonstrating that bias mitigation via our framework preserves predictive accuracy across multiple classifiers, and that coverage constraints, while motivated by statistical considerations, are essential for preserving downstream ML performance.