Fair Principal Component Analysis

Fair Principal Component Analysis (PCA) aims to improve the fairness of dimensionality reduction techniques by mitigating biases encoded in data, ensuring that resulting representations treat different subpopulations equitably. Current research focuses on developing more stable and efficient algorithms, including those based on neural networks (e.g., Fair CoVariance Neural Networks), spectral graph learning (e.g., FUGNN), and streaming approaches (e.g., Fair Noisy Power Method), to address limitations of existing methods, particularly in low-sample regimes. These advancements are significant because they enhance the reliability and applicability of PCA in sensitive domains like loan applications or healthcare, where biased algorithms can lead to discriminatory outcomes. The development of efficient and statistically sound fair PCA methods is crucial for ensuring responsible use of machine learning in high-stakes applications.