# Error Parity Fairness: Testing for Group Fairness in Regression Tasks

arXiv: https://arxiv.org/abs/2208.08279 · HTML: https://mlsanity.com/paper/2208.08279/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 69 references that are in the map.
- First posted on arXiv 16 Aug 2022.

## Builds on (most cited first, 12 of 12)

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Fair Regression: Quantitative Definitions and Reduction-based Algorithms](https://mlsanity.com/paper/1905.12843/) (2019)
- [A Convex Framework for Fair Regression](https://mlsanity.com/paper/1706.02409/) (2017)
- [Fair Regression with Wasserstein Barycenters](https://mlsanity.com/paper/2006.07286/) (2020)
- [A Clarification of the Nuances in the Fairness Metrics Landscape](https://mlsanity.com/paper/2106.00467/) (2021)
- [AI in Finance: Challenges, Techniques and Opportunities](https://mlsanity.com/paper/2107.09051/) (2021)
- [Algorithmic Fairness Datasets: the Story so Far](https://mlsanity.com/paper/2202.01711/) (2022)
- [Fair Kernel Learning](https://mlsanity.com/paper/1710.05578/) (2017)
- [System Cards for AI-Based Decision-Making for Public Policy](https://mlsanity.com/paper/2203.04754/) (2022)
- [Algorithmic Audit of Italian Car Insurance: Evidence of Unfairness in Access and Pricing](https://mlsanity.com/paper/2105.10174/) (2021)
- [Normalise for Fairness: A Simple Normalisation Technique for Fairness in Regression Machine Learning Problems](https://mlsanity.com/paper/2202.00993/) (2022)
- [Comparing distributions by multiple testing across quantiles or CDF values](https://mlsanity.com/paper/1708.04658/) (2017)

## Built on by (most cited first, 5 of 5)

- [Uncovering Bias in Personal Informatics](https://mlsanity.com/paper/2303.15592/) (2023)
- [Towards clinical AI fairness: A translational perspective](https://mlsanity.com/paper/2304.13493/) (2023)
- [RobustFair: Adversarial Evaluation through Fairness Confusion Directed Gradient Search](https://mlsanity.com/paper/2305.10906/) (2023)
- [When Machine Learning Gets Personal: Evaluating Prediction and Explanation](https://mlsanity.com/paper/2502.02786/) (2025)
- [Wasserstein projection distance for fairness testing of regression models](https://mlsanity.com/paper/2510.04114/) (2025)

## Abstract (arXiv)

The applications of Artificial Intelligence (AI) surround decisions on increasingly many aspects of human lives. Society responds by imposing legal and social expectations for the accountability of such automated decision systems (ADSs). Fairness, a fundamental constituent of AI accountability, is concerned with just treatment of individuals and sensitive groups (e.g., based on sex, race). While many studies focus on fair learning and fairness testing for the classification tasks, the literature is rather limited on how to examine fairness in regression tasks. This work presents error parity as a regression fairness notion and introduces a testing methodology to assess group fairness based on a statistical hypothesis testing procedure. The error parity test checks whether prediction errors are distributed similarly across sensitive groups to determine if an ADS is fair. It is followed by a suitable permutation test to compare groups on several statistics to explore disparities and identify impacted groups. The usefulness and applicability of the proposed methodology are demonstrated via a case study on COVID-19 projections in the US at the county level, which revealed race-based differences in forecast errors. Overall, the proposed regression fairness testing methodology fills a gap in the fair machine learning literature and may serve as a part of larger accountability assessments and algorithm audits.