Efficient fair principal component analysis

Mohammad Mahdi Kamani, Farzin Haddadpour, Rana Forsati, Mehrdad Mahdavi

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)3671-3702
Number of pages32
JournalMachine Learning
Volume111
Issue number10
DOIs
StatePublished - Oct 2022

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

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