# FairXGBoost: Fairness-aware Classification in XGBoost

arXiv: https://arxiv.org/abs/2009.01442 · HTML: https://mlsanity.com/paper/2009.01442/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Sep 2020.

## Built on by (most cited first, 9 of 9)

- [Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey](https://mlsanity.com/paper/2207.07068/) (2022)
- [FairGBM: Gradient Boosting with Fairness Constraints](https://mlsanity.com/paper/2209.07850/) (2022)
- [Fairness without Imputation: A Decision Tree Approach for Fair Prediction with Missing Values](https://mlsanity.com/paper/2109.10431/) (2021)
- [Robustness in Large Language Models: A Survey of Mitigation Strategies and Evaluation Metrics](https://mlsanity.com/paper/2505.18658/) (2025)
- [Best Practices for Responsible Machine Learning in Credit Scoring](https://mlsanity.com/paper/2409.20536/) (2024)
- [M2FGB: A Min-Max Gradient Boosting Framework for Subgroup Fairness](https://mlsanity.com/paper/2504.12458/) (2025)
- [De-Biasing Models of Biased Decisions: A Comparison of Methods Using Mortgage Application Data](https://mlsanity.com/paper/2405.00910/) (2024)
- [Explainable post-training bias mitigation with distribution-based fairness metrics](https://mlsanity.com/paper/2504.01223/) (2025)
- [ZKBoost: Zero-Knowledge Verifiable Training for XGBoost](https://mlsanity.com/paper/2602.04113/) (2026)

## Abstract (arXiv)

Highly regulated domains such as finance have long favoured the use of machine learning algorithms that are scalable, transparent, robust and yield better performance. One of the most prominent examples of such an algorithm is XGBoost. Meanwhile, there is also a growing interest in building fair and unbiased models in these regulated domains and numerous bias-mitigation algorithms have been proposed to this end. However, most of these bias-mitigation methods are restricted to specific model families such as logistic regression or support vector machine models, thus leaving modelers with a difficult decision of choosing between fairness from the bias-mitigation algorithms and scalability, transparency, performance from algorithms such as XGBoost. We aim to leverage the best of both worlds by proposing a fair variant of XGBoost that enjoys all the advantages of XGBoost, while also matching the levels of fairness from the state-of-the-art bias-mitigation algorithms. Furthermore, the proposed solution requires very little in terms of changes to the original XGBoost library, thus making it easy for adoption. We provide an empirical analysis of our proposed method on standard benchmark datasets used in the fairness community.