Fair Canonical Correlation Analysis
This addresses fairness issues in statistical analysis for users of CCA, but it is incremental as it builds on existing CCA methods.
The paper tackled unfairness and bias in Canonical Correlation Analysis (CCA) by developing a framework that minimizes correlation disparity error related to protected attributes, resulting in reduced error without compromising CCA accuracy as shown in experiments on synthetic and real-world datasets.
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.