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PLS-based approach for fair representation learning

arXiv 2502.16263 ↗First posted 22 Feb 2025cs.LG cs.CY math.STcited by 0

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Abstract (arXiv)

We revisit the problem of fair representation learning by proposing Fair Partial Least Squares (PLS) components. PLS is widely used in statistics to efficiently reduce the dimension of the data by providing representation tailored for the prediction. We propose a novel method to incorporate fairness constraints in the construction of PLS components. This new algorithm provides a feasible way to construct such features both in the linear and the non linear case using kernel embeddings. The efficiency of our method is evaluated on different datasets, and we prove its superiority with respect to standard fair PCA method.

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References · 21

  1. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  2. 2017Counterfactual Fairnesscited by 802
  3. 2016Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatmentcited by 368
  4. 2018Learning Adversarially Fair and Transferable Representationscited by 353
  5. 2023LEACE: Perfect linear concept erasure in closed formcited by 254
  6. 2018Empirical Risk Minimization under Fairness Constraintscited by 226
  7. 2015The Variational Fair Autoencodercited by 223
  8. 2015Censoring Representations with an Adversarycited by 191
  9. 2017Controllable Invariance through Adversarial Feature Learningcited by 127
  10. 2018The Price of Fair PCA: One Extra Dimensioncited by 67
  11. 2021Controllable Guarantees for Fair Outcomes via Contrastive Information Estimationcited by 45
  12. 2020A survey of bias in Machine Learning through the prism of Statistical Parity for the Adult Data Setcited by 35
9 more
  1. 2021Transport-based Counterfactual Modelscited by 27
  2. 2023Efficient fair PCA for fair representation learningcited by 23
  3. 2018Convex Formulations for Fair Principal Component Analysiscited by 20
  4. 2019Efficient Fair Principal Component Analysiscited by 20
  5. 2019Tackling Algorithmic Bias in Neural-Network Classifiers using Wasserstein-2 Regularizationcited by 18
  6. 2021Fast and Efficient MMD-based Fair PCA via Optimization over Stiefel Manifoldcited by 18
  7. 2019Learning Fair Representations for Kernel Modelscited by 17
  8. 2023COCKATIEL: COntinuous Concept ranKed ATtribution with Interpretable ELements for explaining neural net classifiers on NLP taskscited by 16
  9. 2019Optimized Score Transformation for Consistent Fair Classificationcited by 7

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