# Fair Training of Decision Tree Classifiers

arXiv: https://arxiv.org/abs/2101.00909 · HTML: https://mlsanity.com/paper/2101.00909/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 39 references that are in the map.
- First posted on arXiv 4 Jan 2021.

## Builds on (most cited first, 15 of 15)

- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Fair prediction with disparate impact: A study of bias in recidivism prediction instruments](https://mlsanity.com/paper/1610.07524/) (2016)
- [Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment](https://mlsanity.com/paper/1610.08452/) (2016)
- [Learning Optimal and Fair Decision Trees for Non-Discriminative Decision-Making](https://mlsanity.com/paper/1903.10598/) (2019)
- [Training individually fair ML models with Sensitive Subspace Robustness](https://mlsanity.com/paper/1907.00020/) (2019)
- [Learning Certified Individually Fair Representations](https://mlsanity.com/paper/2002.10312/) (2020)
- [FR-Train: A Mutual Information-Based Approach to Fair and Robust Training](https://mlsanity.com/paper/2002.10234/) (2020)
- [Evasion and Hardening of Tree Ensemble Classifiers](https://mlsanity.com/paper/1509.07892/) (2015)
- [Perfectly Parallel Fairness Certification of Neural Networks](https://mlsanity.com/paper/1912.02499/) (2019)
- [Robust Decision Trees Against Adversarial Examples](https://mlsanity.com/paper/1902.10660/) (2019)
- [Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks](https://mlsanity.com/paper/1906.03526/) (2019)
- [Fair Forests: Regularized Tree Induction to Minimize Model Bias](https://mlsanity.com/paper/1712.08197/) (2017)
- [Treant: Training Evasion-Aware Decision Trees](https://mlsanity.com/paper/1907.01197/) (2019)
- [Genetic Adversarial Training of Decision Trees](https://mlsanity.com/paper/2012.11352/) (2020)

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

- [Strong Optimal Classification Trees](https://mlsanity.com/paper/2103.15965/) (2021)
- [FARE: Provably Fair Representation Learning with Practical Certificates](https://mlsanity.com/paper/2210.07213/) (2022)
- [Learning Optimal Fair Classification Trees: Trade-offs Between Interpretability, Fairness, and Accuracy](https://mlsanity.com/paper/2201.09932/) (2022)
- [Efficient Algorithms For Fair Clustering with a New Fairness Notion](https://mlsanity.com/paper/2109.00708/) (2021)

## Abstract (arXiv)

We study the problem of formally verifying individual fairness of decision tree ensembles, as well as training tree models which maximize both accuracy and individual fairness. In our approach, fairness verification and fairness-aware training both rely on a notion of stability of a classification model, which is a variant of standard robustness under input perturbations used in adversarial machine learning. Our verification and training methods leverage abstract interpretation, a well established technique for static program analysis which is able to automatically infer assertions about stability properties of decision trees. By relying on a tool for adversarial training of decision trees, our fairness-aware learning method has been implemented and experimentally evaluated on the reference datasets used to assess fairness properties. The experimental results show that our approach is able to train tree models exhibiting a high degree of individual fairness w.r.t. the natural state-of-the-art CART trees and random forests. Moreover, as a by-product, these fair decision trees turn out to be significantly compact, thus enhancing the interpretability of their fairness properties.