biostudies-literatureZhou ZNIA NIH HHSNLM NIH HHSNCI NIH HHS3675-3705https://www.ebi.ac.uk/biostudies/studies/S-EPMC11040228biostudies-literatureUnknown36This 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.Advances in neural information processing systemsFair Canonical Correlation Analysis.PMC11040228U01 CA274576U01 AG066833RF1 AG063481P30 AG073105U01 AG024904R01 LM011360R01 LM013463U01 AG068057Tong BShen LFeng YTarzanagh DAHou BLong QXu JZhou ZfalseFair Canonical Correlation Analysis.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.2023-01-01T00:00:00Z2023 Dec2025-04-18T12:56:40.514Z2025-04-06T22:23:10.148ZS-EPMC1104022838665178