# How fair can we go in machine learning? Assessing the boundaries of fairness in decision trees

arXiv: https://arxiv.org/abs/2006.12399 · HTML: https://mlsanity.com/paper/2006.12399/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jun 2020.

## Built on by (most cited first, 22 of 22)

- [A survey on datasets for fairness-aware machine learning](https://mlsanity.com/paper/2110.00530/) (2021)
- [Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey](https://mlsanity.com/paper/2207.07068/) (2022)
- [Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers](https://mlsanity.com/paper/2203.04913/) (2022)
- [Evaluating Proposed Fairness Models for Face Recognition Algorithms](https://mlsanity.com/paper/2203.05051/) (2022)
- [Bias Reducing Multitask Learning on Mental Health Prediction](https://mlsanity.com/paper/2208.03621/) (2022)
- [Mixed integer linear optimization formulations for learning optimal binary classification trees](https://mlsanity.com/paper/2206.04857/) (2022)
- [Towards Fairness-Aware Multi-Objective Optimization](https://mlsanity.com/paper/2207.12138/) (2022)
- [A Theoretical Approach to Characterize the Accuracy-Fairness Trade-off Pareto Frontier](https://mlsanity.com/paper/2310.12785/) (2023)
- [Compress and Compare: Interactively Evaluating Efficiency and Behavior Across ML Model Compression Experiments](https://mlsanity.com/paper/2408.03274/) (2024)
- [Domain Adaptive Decision Trees: Implications for Accuracy and Fairness](https://mlsanity.com/paper/2302.13846/) (2023)
- [On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms](https://mlsanity.com/paper/2502.08932/) (2025)
- [Fair Feature Selection: A Comparison of Multi-Objective Genetic Algorithms](https://mlsanity.com/paper/2310.02752/) (2023)
- [Achievable Fairness on Your Data With Utility Guarantees](https://mlsanity.com/paper/2402.17106/) (2024)
- [A Self-Supervised Learning Pipeline for Demographically Fair Facial Attribute Classification](https://mlsanity.com/paper/2407.10104/) (2024)
- [Enhancing Multi-Attribute Fairness in Healthcare Predictive Modeling](https://mlsanity.com/paper/2501.13219/) (2025)
- [Privacy Constrained Fairness Estimation for Decision Trees](https://mlsanity.com/paper/2312.08413/) (2023)
- [Fairness vs Performance: Characterizing the Pareto Frontier of Algorithmic Decision Systems](https://mlsanity.com/paper/2605.10604/) (2026)
- [Optimal Mixed Integer Linear Optimization Trained Multivariate Classification Trees](https://mlsanity.com/paper/2408.01297/) (2024)
- [FairUDT: Fairness-aware Uplift Decision Trees](https://mlsanity.com/paper/2502.01188/) (2025)
- [Uncovering Fairness through Data Complexity as an Early Indicator](https://mlsanity.com/paper/2504.05923/) (2025)
- [APFEx: Adaptive Pareto Front Explorer for Intersectional Fairness](https://mlsanity.com/paper/2509.13908/) (2025)
- [A pipeline for enabling path-specific causal fairness in observational health data](https://mlsanity.com/paper/2601.09841/) (2026)

## Abstract (arXiv)

Fair machine learning works have been focusing on the development of equitable algorithms that address discrimination of certain groups. Yet, many of these fairness-aware approaches aim to obtain a unique solution to the problem, which leads to a poor understanding of the statistical limits of bias mitigation interventions. We present the first methodology that allows to explore those limits within a multi-objective framework that seeks to optimize any measure of accuracy and fairness and provides a Pareto front with the best feasible solutions. In this work, we focus our study on decision tree classifiers since they are widely accepted in machine learning, are easy to interpret and can deal with non-numerical information naturally. We conclude experimentally that our method can optimize decision tree models by being fairer with a small cost of the classification error. We believe that our contribution will help stakeholders of sociotechnical systems to assess how far they can go being fair and accurate, thus serving in the support of enhanced decision making where machine learning is used.