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 race, color, sex, etc., unfairly. In pursuit of fairness-enhancing dimensionality reduction, using the notion Pareto optimality, we propose an adaptive first-order algorithm learn a subspace preserves fairness, while s...