Zhou Zhuoping, Tarzanagh Davoud Ataee, Hou Bojian, Tong Boning, Xu Jia, Feng Yanbo, Long Qi, Shen Li
University of Pennsylvania.
Adv Neural Inf Process Syst. 2023 Dec;36:3675-3705.
This paper investigates fairness and bias in Canonical Correlation Analysis (CCA), a widely used statistical technique for examining the relationship between two sets of variables. We present a framework that alleviates unfairness by minimizing the correlation disparity error associated with protected attributes. Our approach enables CCA to learn global projection matrices from all data points while ensuring that these matrices yield comparable correlation levels to group-specific projection matrices. Experimental evaluation on both synthetic and real-world datasets demonstrates the efficacy of our method in reducing correlation disparity error without compromising CCA accuracy.
本文研究了典型相关分析（CCA）中的公平性和偏差问题，CCA是一种广泛用于检验两组变量之间关系的统计技术。我们提出了一个框架，通过最小化与受保护属性相关的相关性差异误差来减轻不公平性。我们的方法使CCA能够从所有数据点学习全局投影矩阵，同时确保这些矩阵产生与特定组投影矩阵相当的相关性水平。在合成数据集和真实世界数据集上的实验评估证明了我们的方法在不影响CCA准确性的情况下减少相关性差异误差的有效性。
