# Fair Tree Classifier using Strong Demographic Parity

arXiv: https://arxiv.org/abs/2110.09295 · HTML: https://mlsanity.com/paper/2110.09295/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 34 references that are in the map.
- First posted on arXiv 18 Oct 2021.

## Builds on (most cited first, 6 of 6)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Inherent Trade-Offs in the Fair Determination of Risk Scores](https://mlsanity.com/paper/1609.05807/) (2016)
- [A survey on datasets for fairness-aware machine learning](https://mlsanity.com/paper/2110.00530/) (2021)
- [Wasserstein Fair Classification](https://mlsanity.com/paper/1907.12059/) (2019)
- [FARF: A Fair and Adaptive Random Forests Classifier](https://mlsanity.com/paper/2108.07403/) (2021)

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

- [Mixed integer linear optimization formulations for learning optimal binary classification trees](https://mlsanity.com/paper/2206.04857/) (2022)
- [Retiring DeltaDP: New Distribution-Level Metrics for Demographic Parity](https://mlsanity.com/paper/2301.13443/) (2023)
- [M2FGB: A Min-Max Gradient Boosting Framework for Subgroup Fairness](https://mlsanity.com/paper/2504.12458/) (2025)
- [ReLU integral probability metric and its applications](https://mlsanity.com/paper/2504.18897/) (2025)
- [Doubly-Regressing Approach for Subgroup Fairness](https://mlsanity.com/paper/2510.21091/) (2025)
- [Uncertainty-Aware Fairness-Adaptive Classification Trees](https://mlsanity.com/paper/2410.05810/) (2024)
- [Fair Clustering with Clusterlets](https://mlsanity.com/paper/2505.06259/) (2025)
- [Exploring the Design Space of Fair Tree Learning Algorithms](https://mlsanity.com/paper/2509.03204/) (2025)
- [Visual Model Selection using Feature Importance Clusters in Fairness-Performance Similarity Optimized Space](https://mlsanity.com/paper/2510.22209/) (2025)

## Abstract (arXiv)

When dealing with sensitive data in automated data-driven decision-making, an important concern is to learn predictors with high performance towards a class label, whilst minimising for the discrimination towards any sensitive attribute, like gender or race, induced from biased data. A few hybrid tree optimisation criteria exist that combine classification performance and fairness. Although the threshold-free ROC-AUC is the standard for measuring traditional classification model performance, current fair tree classification methods mainly optimise for a fixed threshold on both the classification task as well as the fairness metric. In this paper, we propose a compound splitting criterion which combines threshold-free (i.e., strong) demographic parity with ROC-AUC termed SCAFF -- Splitting Criterion AUC for Fairness -- and easily extends to bagged and boosted tree frameworks. Our method simultaneously leverages multiple sensitive attributes of which the values may be multicategorical or intersectional, and is tunable with respect to the unavoidable performance-fairness trade-off. In our experiments, we demonstrate how SCAFF generates models with performance and fairness with respect to binary, multicategorical, and multiple sensitive attributes.