# Fast Fair Regression via Efficient Approximations of Mutual Information

arXiv: https://arxiv.org/abs/2002.06200 · HTML: https://mlsanity.com/paper/2002.06200/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2020.

## Built on by (most cited first, 10 of 10)

- [A Stochastic Optimization Framework for Fair Risk Minimization](https://mlsanity.com/paper/2102.12586/) (2021)
- [Achieving Fairness with a Simple Ridge Penalty](https://mlsanity.com/paper/2105.13817/) (2021)
- [Gradual (In)Compatibility of Fairness Criteria](https://mlsanity.com/paper/2109.04399/) (2021)
- [Mean Parity Fair Regression in RKHS](https://mlsanity.com/paper/2302.10409/) (2023)
- [Fair Regression under Sample Selection Bias](https://mlsanity.com/paper/2110.04372/) (2021)
- [Practical Approaches for Fair Learning with Multitype and Multivariate Sensitive Attributes](https://mlsanity.com/paper/2211.06138/) (2022)
- [Machine Learning with Multitype Protected Attributes: Intersectional Fairness through Regularisation](https://mlsanity.com/paper/2509.08163/) (2025)
- [Counterfactually Fair Regression with Double Machine Learning](https://mlsanity.com/paper/2303.11529/) (2023)
- [Toward Unifying Group Fairness Evaluation from a Sparsity Perspective](https://mlsanity.com/paper/2511.00359/) (2025)
- [Separation-Utility Pareto Frontier: An Information-Theoretic Characterization](https://mlsanity.com/paper/2602.04408/) (2026)

## Abstract (arXiv)

Most work in algorithmic fairness to date has focused on discrete outcomes, such as deciding whether to grant someone a loan or not. In these classification settings, group fairness criteria such as independence, separation and sufficiency can be measured directly by comparing rates of outcomes between subpopulations. Many important problems however require the prediction of a real-valued outcome, such as a risk score or insurance premium. In such regression settings, measuring group fairness criteria is computationally challenging, as it requires estimating information-theoretic divergences between conditional probability density functions. This paper introduces fast approximations of the independence, separation and sufficiency group fairness criteria for regression models from their (conditional) mutual information definitions, and uses such approximations as regularisers to enforce fairness within a regularised risk minimisation framework. Experiments in real-world datasets indicate that in spite of its superior computational efficiency our algorithm still displays state-of-the-art accuracy/fairness tradeoffs.