# A Maximal Correlation Approach to Imposing Fairness in Machine Learning

arXiv: https://arxiv.org/abs/2012.15259 · HTML: https://mlsanity.com/paper/2012.15259/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Dec 2020.

## Built on by (most cited first, 13 of 13)

- [Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey](https://mlsanity.com/paper/2207.07068/) (2022)
- [Selective Regression Under Fairness Criteria](https://mlsanity.com/paper/2110.15403/) (2021)
- [Conditional Contrastive Learning for Improving Fairness in Self-Supervised Learning](https://mlsanity.com/paper/2106.02866/) (2021)
- [A Comprehensive Survey and Classification of Evaluation Criteria for Trustworthy Artificial Intelligence](https://mlsanity.com/paper/2410.17281/) (2024)
- [Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective](https://mlsanity.com/paper/2506.07861/) (2025)
- [Fair Supervised Learning with A Simple Random Sampler of Sensitive Attributes](https://mlsanity.com/paper/2311.05866/) (2023)
- [Evaluating Fairness and Mitigating Bias in Machine Learning: A Novel Technique using Tensor Data and Bayesian Regression](https://mlsanity.com/paper/2506.11627/) (2025)
- [More than Vanilla Fusion: a Simple, Decoupling-free, Attention Module for Multimodal Fusion Based on Signal Theory](https://mlsanity.com/paper/2312.07212/) (2023)
- [From Discrete to Continuous: Deep Fair Clustering With Transferable Representations](https://mlsanity.com/paper/2403.16201/) (2024)
- [dsld: A Socially Relevant Tool for Teaching Statistics](https://mlsanity.com/paper/2411.04228/) (2024)
- [TowerDebias: A Novel Unfairness Removal Method Based on the Tower Property](https://mlsanity.com/paper/2411.08297/) (2024)
- [Task-tailored Pre-processing: Fair Downstream Supervised Learning](https://mlsanity.com/paper/2601.11897/) (2026)
- [The Gold in Bias: Maturing the AI Design Process through Verification](https://mlsanity.com/paper/2609.29730/) (2026)

## Abstract (arXiv)

As machine learning algorithms grow in popularity and diversify to many industries, ethical and legal concerns regarding their fairness have become increasingly relevant. We explore the problem of algorithmic fairness, taking an information-theoretic view. The maximal correlation framework is introduced for expressing fairness constraints and shown to be capable of being used to derive regularizers that enforce independence and separation-based fairness criteria, which admit optimization algorithms for both discrete and continuous variables which are more computationally efficient than existing algorithms. We show that these algorithms provide smooth performance-fairness tradeoff curves and perform competitively with state-of-the-art methods on both discrete datasets (COMPAS, Adult) and continuous datasets (Communities and Crimes).