Efficient fair principal component analysis

• Published in: Machine learning Vol. 111; no. 10; pp. 3671 - 3702
• Main Authors: Kamani, Mohammad Mahdi, Haddadpour, Farzin, Forsati, Rana, Mahdavi, Mehrdad
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.10.2022; Springer Nature B.V
• Subjects: Adaptive algorithms; Algorithms; Artificial Intelligence; Computer Science; Control; Empirical analysis; First order algorithms; Machine Learning; Mechatronics; Natural Language Processing (NLP); Optimization; Pareto optimum; Principal components analysis; Reconstruction; Reduction; Robotics; Simulation and Modeling; Special Issue: Foundations of Data Science; Dimension reduction; Pareto efficient; Fairness; PCA
• ISSN: 0885-6125; 1573-0565
• DOI: 10.1007/s10994-021-06100-9

It has been shown that dimension reduction methods such as Principal Component Analysis (PCA) may be inherently prone to unfairness and treat data from different sensitive groups such as race, color, sex, etc., unfairly. In pursuit of fairness-enhancing dimensionality reduction, using the notion of Pareto optimality, we propose an adaptive first-order algorithm to learn a subspace that preserves fairness, while slightly compromising the reconstruction loss. Theoretically, we provide sufficient conditions that the solution of the proposed algorithm belongs to the Pareto frontier for all sensitive groups; thereby, the optimal trade-off between overall reconstruction loss and fairness constraints is guaranteed. We also provide the convergence analysis of our algorithm and show its efficacy through empirical studies on different datasets, which demonstrates superior performance in comparison with state-of-the-art algorithms. The proposed fairness-aware PCA algorithm can be efficiently generalized to multiple group sensitive features and effectively reduce the unfairness decisions in downstream tasks such as classification.