# fairml: A Statistician's Take on Fair Machine Learning Modelling

arXiv: https://arxiv.org/abs/2305.02009 · HTML: https://mlsanity.com/paper/2305.02009/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 58 references that are in the map.
- First posted on arXiv 3 May 2023.

## Builds on (most cited first, 16 of 16)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [A Reductions Approach to Fair Classification](https://mlsanity.com/paper/1803.02453/) (2018)
- [Fairness in Criminal Justice Risk Assessments: The State of the Art](https://mlsanity.com/paper/1703.09207/) (2017)
- [Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices](https://mlsanity.com/paper/1906.09208/) (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)
- [Conscientious Classification: A Data Scientist's Guide to Discrimination-Aware Classification](https://mlsanity.com/paper/1907.09013/) (2019)
- [Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints](https://mlsanity.com/paper/1807.00028/) (2018)
- [Fair Kernel Learning](https://mlsanity.com/paper/1710.05578/) (2017)
- [Review of Mathematical frameworks for Fairness in Machine Learning](https://mlsanity.com/paper/2005.13755/) (2020)
- [Elastic Net Regularization Paths for All Generalized Linear Models](https://mlsanity.com/paper/2103.03475/) (2021)
- [Achieving Fairness with a Simple Ridge Penalty](https://mlsanity.com/paper/2105.13817/) (2021)
- [Fair Generalized Linear Models with a Convex Penalty](https://mlsanity.com/paper/2206.09076/) (2022)
- [fairmodels: A Flexible Tool For Bias Detection, Visualization, And Mitigation](https://mlsanity.com/paper/2104.00507/) (2021)

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

- [Generalizing Orthogonalization for Models with Non-Linearities](https://mlsanity.com/paper/2405.02475/) (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)
- [Automated Segmentation and Tracking of Group Housed Pigs Using Foundation Models](https://mlsanity.com/paper/2604.03426/) (2026)
- [The Role of Uncertainty in Assessing the Fairness of Machine Learning Models](https://mlsanity.com/paper/2609.07959/) (2026)

## Abstract (arXiv)

The adoption of machine learning in applications where it is crucial to ensure fairness and accountability has led to a large number of model proposals in the literature, largely formulated as optimisation problems with constraints reducing or eliminating the effect of sensitive attributes on the response. While this approach is very flexible from a theoretical perspective, the resulting models are somewhat black-box in nature: very little can be said about their statistical properties, what are the best practices in their applied use, and how they can be extended to problems other than those they were originally designed for. Furthermore, the estimation of each model requires a bespoke implementation involving an appropriate solver which is less than desirable from a software engineering perspective. In this paper, we describe the fairml R package which implements our previous work (Scutari, Panero, and Proissl 2022) and related models in the literature. fairml is designed around classical statistical models (generalised linear models) and penalised regression results (ridge regression) to produce fair models that are interpretable and whose properties are well-known. The constraint used to enforce fairness is orthogonal to model estimation, making it possible to mix-and-match the desired model family and fairness definition for each application. Furthermore, fairml provides facilities for model estimation, model selection and validation including diagnostic plots.