Fair ranking problems arise in many decision-making processes that often necessitate a trade-off between accuracy and fairness. Many existing studies have proposed correction methods such as adding fairness constraints to a ranking model's loss. However, the challenge of correcting the data bias for fair ranking remains, and the trade-off of the ranking models leaves room for improvement. In this paper, we propose a fair ranking framework that evaluates the order of training data in a pairwise manner as well as various fairness measurements in ranking. This study is the first proposal of a pre-processing method that solves fair ranking problems using the pairwise ordering method with our best knowledge. The fair pairwise ordering method is prominent in training the fair ranking models because it ensures that the resulting ranking likely becomes parity across groups. As far as the fairness measurements in ranking are represented as a linear constraint of the ranking models, we proved that the minimization of loss function subject to the constraints is reduced to the closed solution of the minimization problem augmented by weights to training data. This closed solution inspires us to present a practical and stable algorithm that iterates the optimization of weights and model parameters. The empirical results over real-world datasets demonstrated that our method outperforms the existing methods in the trade-off between accuracy and fairness over real-world datasets and various fairness measurements.
翻译:许多现有研究提出了纠正方法,例如增加公平性限制,以弥补排名模式的损失。然而,纠正数据偏差以争取公平排名的挑战依然存在,排名模式的权衡也留有改进的余地。在本文件中,我们提议了一个公平排名框架,以对等方式评价培训数据顺序,并采用各种公平等级衡量方法。本项研究是第一个用我们的最佳知识用对等订购方法解决公平排名问题的处理前方法的建议,它用我们的最佳知识解决公平排序问题。公平对等排序方法在培训公平排名模式中占有突出地位,因为它确保由此产生的排名有可能成为各组间的均等。只要排名的公平度衡量是排名模式的线性制约,我们证明受限制的损失功能的最小化已经减少到了通过培训数据权重增强的最小化问题的封闭性解决办法。这一封闭式解决方案激励我们提出一种实际和稳定的算法,用以优化权重和模型参数。真实世界数据测算的实证结果显示,真实世界数据测得的准确性超出了我们现有数据测算方法的准确性。