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.

翻译：决策系统中使用的排序函数常因底层数据偏差对不同群体产生不平等结果。解决并补偿这些不平等结果对于实现公平决策至关重要。近年来的补偿措施主要关注对排序函数进行黑箱式转换以满足公平性保证，或通过配额制与预留机制保障弱势群体成员获得最低数量的正向结果。本文提出一种易于解释的数据驱动型排序函数补偿措施。该措施通过向弱势群体成员分配可预先设定的加分项来解决排序函数中的不平等问题，这些加分项可叠加使用，从而兼顾多重身份交叉特征并增强决策透明度。我们提出基于采样的高效算法来计算最小化不平等所需的加分数量，并利用真实世界的高校录取数据和再犯率数据集进行算法验证，最后将实验结果与现有公平排序算法进行对比分析。