mlsanityData to 7 Oct 2026

FairGBM: Gradient Boosting with Fairness Constraints

arXiv 2209.07850 ↗First posted 16 Sep 2022cs.LG cs.AI cs.CYcited by 25

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Abstract (arXiv)

Tabular data is prevalent in many high-stakes domains, such as financial services or public policy. Gradient Boosted Decision Trees (GBDT) are popular in these settings due to their scalability, performance, and low training cost. While fairness in these domains is a foremost concern, existing in-processing Fair ML methods are either incompatible with GBDT, or incur in significant performance losses while taking considerably longer to train. We present FairGBM, a dual ascent learning framework for training GBDT under fairness constraints, with little to no impact on predictive performance when compared to unconstrained GBDT. Since observational fairness metrics are non-differentiable, we propose smooth convex error rate proxies for common fairness criteria, enabling gradient-based optimization using a ``proxy-Lagrangian'' formulation. Our implementation shows an order of magnitude speedup in training time relative to related work, a pivotal aspect to foster the widespread adoption of FairGBM by real-world practitioners.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). Builds on 22 of its 55 references that are in the map. First posted on arXiv 16 Sep 2022.

Builds on

22 of 55 references.

References · 22

  1. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
  2. 2021Convex optimizationcited by 2,479
  3. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  4. 2018Datasheets for Datasetscited by 1,371
  5. 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,304
  6. 2018A Reductions Approach to Fair Classificationcited by 585
  7. 2021Tabular Data: Deep Learning is Not All You Needcited by 530
  8. 2021Retiring Adult: New Datasets for Fair Machine Learningcited by 443
  9. 2017On Fairness and Calibrationcited by 406
  10. 2017Algorithmic decision making and the cost of fairnesscited by 402
  11. 2018AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Biascited by 319
  12. 2015Censoring Representations with an Adversarycited by 191
10 more
  1. 2019Fair Regression: Quantitative Definitions and Reduction-based Algorithmscited by 168
  2. 2019Show Your Work: Improved Reporting of Experimental Resultscited by 164
  3. 2018Aequitas: A Bias and Fairness Audit Toolkitcited by 145
  4. 2018Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goalscited by 103
  5. 2016A Confidence-Based Approach for Balancing Fairness and Accuracycited by 73
  6. 2019AdaFair: Cumulative Fairness Adaptive Boostingcited by 45
  7. 2019Fair Adversarial Gradient Tree Boostingcited by 17
  8. 2021Promoting Fairness through Hyperparameter Optimizationcited by 17
  9. 2021Individually Fair Gradient Boostingcited by 12
  10. 2020FairXGBoost: Fairness-aware Classification in XGBoostcited by 9

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 7; 2024: 8; 2025: 7; 2026: 2.

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  1. 2023Questioning the Survey Responses of Large Language Modelscited by 70
  2. 2023Bias Testing and Mitigation in LLM-based Code Generationcited by 53
  3. 2023Unprocessing Seven Years of Algorithmic Fairnesscited by 18
  4. 2023FRAPPE: A Group Fairness Framework for Post-Processing Everythingcited by 16
  5. 2026Machine Unlearning under Retain-Forget Entanglementcited by 10
  6. 2022Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraintscited by 9
  7. 2023When Fairness Meets Privacy: Exploring Privacy Threats in Fair Binary Classifiers via Membership Inference Attackscited by 5
  8. 2025Overcoming Fairness Trade-offs via Pre-processing: A Causal Perspectivecited by 5
  9. 2023Improving Fairness with Ensemble Combination: Margin-Dependent Boundscited by 3
  10. 2024Learning Fairer Representations with FairVICcited by 2
  11. 2024Measuring Model-Induced Discrimination via Efficient Fairness Approximationcited by 2
  12. 2024Efficient Fairness-Performance Pareto Front Computationcited by 2
13 more
  1. 2024Best Practices for Responsible Machine Learning in Credit Scoringcited by 2
  2. 2025Do Fairness Interventions Come at the Cost of Privacy: Evaluations for Binary Classifierscited by 2
  3. 2023GBM-based Bregman Proximal Algorithms for Constrained Learningcited by 1
  4. 2024Approximating Discrimination Within Models When Faced With Several Non-Binary Sensitive Attributescited by 1
  5. 2024RIFF: Inducing Rules for Fraud Detection from Decision Treescited by 1
  6. 2025M2FGB: A Min-Max Gradient Boosting Framework for Subgroup Fairnesscited by 1
  7. 2024Fair Multivariate Adaptive Regression Splines for Ensuring Equity and Transparencycited by 0
  8. 2024Fair MP-BOOST: Fair and Interpretable Minipatch Boostingcited by 0
  9. 2025A Critical Review of Predominant Bias in Neural Networkscited by 0
  10. 2025You Only Debias Once: Towards Flexible Accuracy-Fairness Trade-offs at Inference Timecited by 0
  11. 2025Algorithmic Fairness: Not a Purely Technical but Socio-Technical Propertycited by 0
  12. 2025Reinforcement-Guided Hyper-Heuristic Hyperparameter Optimization for Fair and Explainable Spiking Neural Network-Based Financial Fraud Detectioncited by 0
  13. 2026Projected Boosting with Fairness Constraints: Quantifying the Cost of Fair Training Distributionscited by 0