TL;DR介绍了一种新的数据预处理算法 Orthogonal to Bias (OB),旨在消除连续敏感变量的影响,从而实现机器学习应用中的反事实公平性,并通过模拟和实际数据集的实证评估证明该方法能够在不损害准确性的情况下实现更加公平的结果。
Abstract
machine learning models have shown exceptional prowess in solving complex issues across various domains. Nonetheless, these models can sometimes exhibit biased decision-making, leading to disparities in treatment