用于高效公平排序的可解释差异补偿
Explainable Disparity Compensation for Efficient Fair Ranking
AI总结：
本文提出一种易于解释的数据驱动补偿措施，通过向代表性不足群体发放加分并结合高效采样算法最小化排序差异，实现公平排序。
摘要：
用于决策系统的排序函数常因底层数据偏差对不同群体产生差异结果。解决并补偿这些差异结果是实现公平决策的关键问题。近期补偿措施多聚焦于对排序函数进行不透明变换以满足公平性保证，或使用配额或预留来保证代表性不足群体获得最低数量的正面结果。本文提出易于解释的数据驱动排序函数补偿措施。我们的措施依赖于向代表性不足群体成员发放加分以解决排序函数中的差异。加分可预先设定并可组合，从而允许考虑表征的交集并为利益相关者提供更好的透明度。我们提出高效的基于采样的算法来计算加分数量以最小化差异。我们使用真实世界学校招生和再犯数据集验证算法，并将结果与现有公平排序算法进行比较。
英文摘要：
Ranking functions that are used in decision systems often produce disparate results for different populations because of bias in the underlying data. Addressing, and compensating for, these disparate outcomes is a critical problem for fair decision-making. Recent compensatory measures have mostly focused on opaque transformations of the ranking functions to satisfy fairness guarantees or on the use of quotas or set-asides to guarantee a minimum number of positive outcomes to members of underrepresented groups. In this paper we propose easily explainable data-driven compensatory measures for ranking functions. Our measures rely on the generation of bonus points given to members of underrepresented groups to address disparity in the ranking function. The bonus points can be set in advance, and can be combined, allowing for considering the intersections of representations and giving better transparency to stakeholders. We propose efficient sampling-based algorithms to calculate the number of bonus points to minimize disparity. We validate our algorithms using real-world school admissions and recidivism datasets, and compare our results with that of existing fair ranking algorithms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
