# Projection to Fairness in Statistical Learning

arXiv: https://arxiv.org/abs/2005.11720 · HTML: https://mlsanity.com/paper/2005.11720/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 May 2020.

## Built on by (most cited first, 4 of 4)

- [Fairness with Continuous Optimal Transport](https://mlsanity.com/paper/2101.02084/) (2021)
- [Fair Regression under Sample Selection Bias](https://mlsanity.com/paper/2110.04372/) (2021)
- [Optimisation Strategies for Ensuring Fairness in Machine Learning: With and Without Demographics](https://mlsanity.com/paper/2411.09056/) (2024)
- [Counterfactually Fair Regression via Optimal Transport](https://mlsanity.com/paper/2605.28251/) (2026)

## Abstract (arXiv)

In the context of regression, we consider the fundamental question of making an estimator fair while preserving its prediction accuracy as much as possible. To that end, we define its projection to fairness as its closest fair estimator in a sense that reflects prediction accuracy. Our methodology leverages tools from optimal transport to construct efficiently the projection to fairness of any given estimator as a simple post-processing step. Moreover, our approach precisely quantifies the cost of fairness, measured in terms of prediction accuracy.