# Fair Forests: Regularized Tree Induction to Minimize Model Bias

arXiv: https://arxiv.org/abs/1712.08197 · HTML: https://mlsanity.com/paper/1712.08197/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2017.

## Built on by (most cited first, 19 of 19)

- [Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey](https://mlsanity.com/paper/2207.07068/) (2022)
- [Selective Regression Under Fairness Criteria](https://mlsanity.com/paper/2110.15403/) (2021)
- [A Stochastic Optimization Framework for Fair Risk Minimization](https://mlsanity.com/paper/2102.12586/) (2021)
- [Fairness without Imputation: A Decision Tree Approach for Fair Prediction with Missing Values](https://mlsanity.com/paper/2109.10431/) (2021)
- [FARE: Provably Fair Representation Learning with Practical Certificates](https://mlsanity.com/paper/2210.07213/) (2022)
- [Subgroup Robustness Grows On Trees: An Empirical Baseline Investigation](https://mlsanity.com/paper/2211.12703/) (2022)
- [A Critical Survey on Fairness Benefits of Explainable AI](https://mlsanity.com/paper/2310.13007/) (2023)
- [FairCanary: Rapid Continuous Explainable Fairness](https://mlsanity.com/paper/2106.07057/) (2021)
- [Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Tools](https://mlsanity.com/paper/2309.17337/) (2023)
- [Mean Parity Fair Regression in RKHS](https://mlsanity.com/paper/2302.10409/) (2023)
- [Fair Training of Decision Tree Classifiers](https://mlsanity.com/paper/2101.00909/) (2021)
- [Enhancing Group Fairness in Online Settings Using Oblique Decision Forests](https://mlsanity.com/paper/2310.11401/) (2023)
- [Inducing Group Fairness in Prompt-Based Language Model Decisions](https://mlsanity.com/paper/2406.16738/) (2024)
- [EiFFFeL: Enforcing Fairness in Forests by Flipping Leaves](https://mlsanity.com/paper/2112.14435/) (2021)
- [Towards A Scalable Solution for Improving Multi-Group Fairness in Compositional Classification](https://mlsanity.com/paper/2307.05728/) (2023)
- [Intersectional Fairness via Mixed-Integer Optimization](https://mlsanity.com/paper/2601.19595/) (2026)
- [Dive into Decision Trees and Forests: A Theoretical Demonstration](https://mlsanity.com/paper/2101.08656/) (2021)
- [FairUDT: Fairness-aware Uplift Decision Trees](https://mlsanity.com/paper/2502.01188/) (2025)
- [Exploring the Design Space of Fair Tree Learning Algorithms](https://mlsanity.com/paper/2509.03204/) (2025)

## Abstract (arXiv)

The potential lack of fairness in the outputs of machine learning algorithms has recently gained attention both within the research community as well as in society more broadly. Surprisingly, there is no prior work developing tree-induction algorithms for building fair decision trees or fair random forests. These methods have widespread popularity as they are one of the few to be simultaneously interpretable, non-linear, and easy-to-use. In this paper we develop, to our knowledge, the first technique for the induction of fair decision trees. We show that our "Fair Forest" retains the benefits of the tree-based approach, while providing both greater accuracy and fairness than other alternatives, for both "group fairness" and "individual fairness.'" We also introduce new measures for fairness which are able to handle multinomial and continues attributes as well as regression problems, as opposed to binary attributes and labels only. Finally, we demonstrate a new, more robust evaluation procedure for algorithms that considers the dataset in its entirety rather than only a specific protected attribute.