mlsanityData to 7 Oct 2026

Individually Fair Gradient Boosting

arXiv 2103.16785 ↗First posted 31 Mar 2021cs.LG stat.MLcited by 12

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Abstract (arXiv)

We consider the task of enforcing individual fairness in gradient boosting. Gradient boosting is a popular method for machine learning from tabular data, which arise often in applications where algorithmic fairness is a concern. At a high level, our approach is a functional gradient descent on a (distributionally) robust loss function that encodes our intuition of algorithmic fairness for the ML task at hand. Unlike prior approaches to individual fairness that only work with smooth ML models, our approach also works with non-smooth models such as decision trees. We show that our algorithm converges globally and generalizes. We also demonstrate the efficacy of our algorithm on three ML problems susceptible to algorithmic bias.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 31 of its 56 references that are in the map. First posted on arXiv 31 Mar 2021.

Builds on

31 of 56 references.

References · 31

  1. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  2. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  3. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
  4. 2018Mitigating Unwanted Biases with Adversarial Learningcited by 663
  5. 2017Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairnesscited by 381
  6. 2018Fairness Without Demographics in Repeated Loss Minimizationcited by 363
  7. 2019Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Settingcited by 346
  8. 2017Certifying Some Distributional Robustness with Principled Adversarial Trainingcited by 333
  9. 2018AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Biascited by 319
  10. 2015Data-driven Distributionally Robust Optimization Using the Wasserstein Metric: Performance Guarantees and Tractable Reformulationscited by 285
  11. 2018Multiaccuracy: Black-Box Post-Processing for Fairness in Classificationcited by 221
  12. 2019Flexibly Fair Representation Learning by Disentanglementcited by 196
19 more
  1. 2016Stochastic Optimization for Large-scale Optimal Transportcited by 188
  2. 2016Variance-based regularization with convex objectivescited by 153
  3. 2018Counterfactual Fairness in Text Classification through Robustnesscited by 150
  4. 2018The Frontiers of Fairness in Machine Learningcited by 139
  5. 2018An Empirical Study of Rich Subgroup Fairness for Machine Learningcited by 81
  6. 2019Training individually fair ML models with Sensitive Subspace Robustnesscited by 73
  7. 2019Operationalizing Individual Fairness with Pairwise Fair Representationscited by 64
  8. 2018Online Learning with an Unknown Fairness Metriccited by 61
  9. 2018Fairness Through Computationally-Bounded Awarenesscited by 55
  10. 2020Two Simple Ways to Learn Individual Fairness Metrics from Datacited by 54
  11. 2019Metric Learning for Individual Fairnesscited by 51
  12. 2019Average Individual Fairness: Algorithms, Generalization and Experimentscited by 46
  13. 2019Robust Decision Trees Against Adversarial Examplescited by 40
  14. 2020SenSeI: Sensitive Set Invariance for Enforcing Individual Fairnesscited by 37
  15. 2017Calibration for the (Computationally-Identifiable) Massescited by 23
  16. 2019Debiasing Embeddings for Reduced Gender Bias in Text Classificationcited by 19
  17. 2019An Algorithmic Framework for Fairness Elicitationcited by 15
  18. 2019An Empirical Study on Learning Fairness Metrics for COMPAS Data with Human Supervisioncited by 13
  19. 2018Debiasing representations by removing unwanted variation due to protected attributescited by 1

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Cited by year

Citing papers per year: 2021: 1; 2022: 4; 2023: 2; 2024: 2; 2025: 3.

12 papers.

  1. 2021Post-processing for Individual Fairnesscited by 61
  2. 2022FairGBM: Gradient Boosting with Fairness Constraintscited by 25
  3. 2022Learning Antidote Data to Individual Unfairnesscited by 8
  4. 2023Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Toolscited by 7
  5. 2022iFlipper: Label Flipping for Individual Fairnesscited by 5
  6. 2023Causal Adversarial Perturbations for Individual Fairness and Robustness in Heterogeneous Data Spacescited by 4
  7. 2024Monotone Individual Fairnesscited by 3
  8. 2024Best Practices for Responsible Machine Learning in Credit Scoringcited by 2
  9. 2025M2FGB: A Min-Max Gradient Boosting Framework for Subgroup Fairnesscited by 1
  10. 2022Identifying, measuring, and mitigating individual unfairness for supervised learning models and application to credit risk modelscited by 0
  11. 2025Local Statistical Parity for the Estimation of Fair Decision Treescited by 0
  12. 2025Fairness-Aware Insurance Pricing: A Multi-Objective Optimization Approachcited by 0