# Learning Fair Representations for Kernel Models

arXiv: https://arxiv.org/abs/1906.11813 · HTML: https://mlsanity.com/paper/1906.11813/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2019.

## Built on by (most cited first, 17 of 17)

- [Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey](https://mlsanity.com/paper/2207.07068/) (2022)
- [Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learning](https://mlsanity.com/paper/2106.02705/) (2021)
- [Group-Aware Threshold Adaptation for Fair Classification](https://mlsanity.com/paper/2111.04271/) (2021)
- [FARE: Provably Fair Representation Learning with Practical Certificates](https://mlsanity.com/paper/2210.07213/) (2022)
- [Metadata Normalization](https://mlsanity.com/paper/2104.09052/) (2021)
- [Attainability and Optimality: The Equalized Odds Fairness Revisited](https://mlsanity.com/paper/2202.11853/) (2022)
- [Fairness Through Regularization for Learning to Rank](https://mlsanity.com/paper/2102.05996/) (2021)
- [Ultra-marginal Feature Importance: Learning from Data with Causal Guarantees](https://mlsanity.com/paper/2204.09938/) (2022)
- [Mean Parity Fair Regression in RKHS](https://mlsanity.com/paper/2302.10409/) (2023)
- [Fair CoVariance Neural Networks](https://mlsanity.com/paper/2409.08558/) (2024)
- [Enforcing Conditional Independence for Fair Representation Learning and Causal Image Generation](https://mlsanity.com/paper/2404.13798/) (2024)
- [On the Power of Randomization in Fair Classification and Representation](https://mlsanity.com/paper/2406.03142/) (2024)
- [FLEA: Provably Robust Fair Multisource Learning from Unreliable Training Data](https://mlsanity.com/paper/2106.11732/) (2021)
- [On Disentangled and Locally Fair Representations](https://mlsanity.com/paper/2205.02673/) (2022)
- [PLS-based approach for fair representation learning](https://mlsanity.com/paper/2502.16263/) (2025)
- [Deep Fair Learning: Task-Aware Fair Representations via Joint Distance-Covariance Regularization](https://mlsanity.com/paper/2504.06470/) (2025)
- [Extending Fair Null-Space Projections for Continuous Attributes to Kernel Methods](https://mlsanity.com/paper/2511.03304/) (2025)

## Abstract (arXiv)

Fair representations are a powerful tool for establishing criteria like statistical parity, proxy non-discrimination, and equality of opportunity in learned models. Existing techniques for learning these representations are typically model-agnostic, as they preprocess the original data such that the output satisfies some fairness criterion, and can be used with arbitrary learning methods. In contrast, we demonstrate the promise of learning a model-aware fair representation, focusing on kernel-based models. We leverage the classical Sufficient Dimension Reduction (SDR) framework to construct representations as subspaces of the reproducing kernel Hilbert space (RKHS), whose member functions are guaranteed to satisfy fairness. Our method supports several fairness criteria, continuous and discrete data, and multiple protected attributes. We further show how to calibrate the accuracy tradeoff by characterizing it in terms of the principal angles between subspaces of the RKHS. Finally, we apply our approach to obtain the first Fair Gaussian Process (FGP) prior for fair Bayesian learning, and show that it is competitive with, and in some cases outperforms, state-of-the-art methods on real data.