# Individually Fair Gradient Boosting

arXiv: https://arxiv.org/abs/2103.16785 · HTML: https://mlsanity.com/paper/2103.16785/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 56 references that are in the map.
- First posted on arXiv 31 Mar 2021.

## Builds on (most cited first, 30 of 31)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Mitigating Unwanted Biases with Adversarial Learning](https://mlsanity.com/paper/1801.07593/) (2018)
- [Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness](https://mlsanity.com/paper/1711.05144/) (2017)
- [Fairness Without Demographics in Repeated Loss Minimization](https://mlsanity.com/paper/1806.08010/) (2018)
- [Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting](https://mlsanity.com/paper/1901.09451/) (2019)
- [Certifying Some Distributional Robustness with Principled Adversarial Training](https://mlsanity.com/paper/1710.10571/) (2017)
- [AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias](https://mlsanity.com/paper/1810.01943/) (2018)
- [Data-driven Distributionally Robust Optimization Using the Wasserstein Metric: Performance Guarantees and Tractable Reformulations](https://mlsanity.com/paper/1505.05116/) (2015)
- [Multiaccuracy: Black-Box Post-Processing for Fairness in Classification](https://mlsanity.com/paper/1805.12317/) (2018)
- [Flexibly Fair Representation Learning by Disentanglement](https://mlsanity.com/paper/1906.02589/) (2019)
- [Stochastic Optimization for Large-scale Optimal Transport](https://mlsanity.com/paper/1605.08527/) (2016)
- [Variance-based regularization with convex objectives](https://mlsanity.com/paper/1610.02581/) (2016)
- [Counterfactual Fairness in Text Classification through Robustness](https://mlsanity.com/paper/1809.10610/) (2018)
- [The Frontiers of Fairness in Machine Learning](https://mlsanity.com/paper/1810.08810/) (2018)
- [An Empirical Study of Rich Subgroup Fairness for Machine Learning](https://mlsanity.com/paper/1808.08166/) (2018)
- [Training individually fair ML models with Sensitive Subspace Robustness](https://mlsanity.com/paper/1907.00020/) (2019)
- [Operationalizing Individual Fairness with Pairwise Fair Representations](https://mlsanity.com/paper/1907.01439/) (2019)
- [Online Learning with an Unknown Fairness Metric](https://mlsanity.com/paper/1802.06936/) (2018)
- [Fairness Through Computationally-Bounded Awareness](https://mlsanity.com/paper/1803.03239/) (2018)
- [Two Simple Ways to Learn Individual Fairness Metrics from Data](https://mlsanity.com/paper/2006.11439/) (2020)
- [Metric Learning for Individual Fairness](https://mlsanity.com/paper/1906.00250/) (2019)
- [Average Individual Fairness: Algorithms, Generalization and Experiments](https://mlsanity.com/paper/1905.10607/) (2019)
- [Robust Decision Trees Against Adversarial Examples](https://mlsanity.com/paper/1902.10660/) (2019)
- [SenSeI: Sensitive Set Invariance for Enforcing Individual Fairness](https://mlsanity.com/paper/2006.14168/) (2020)
- [Calibration for the (Computationally-Identifiable) Masses](https://mlsanity.com/paper/1711.08513/) (2017)
- [Debiasing Embeddings for Reduced Gender Bias in Text Classification](https://mlsanity.com/paper/1908.02810/) (2019)
- [An Algorithmic Framework for Fairness Elicitation](https://mlsanity.com/paper/1905.10660/) (2019)
- [An Empirical Study on Learning Fairness Metrics for COMPAS Data with Human Supervision](https://mlsanity.com/paper/1910.10255/) (2019)

## Built on by (most cited first, 12 of 12)

- [Post-processing for Individual Fairness](https://mlsanity.com/paper/2110.13796/) (2021)
- [FairGBM: Gradient Boosting with Fairness Constraints](https://mlsanity.com/paper/2209.07850/) (2022)
- [Learning Antidote Data to Individual Unfairness](https://mlsanity.com/paper/2211.15897/) (2022)
- [Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Tools](https://mlsanity.com/paper/2309.17337/) (2023)
- [iFlipper: Label Flipping for Individual Fairness](https://mlsanity.com/paper/2209.07047/) (2022)
- [Causal Adversarial Perturbations for Individual Fairness and Robustness in Heterogeneous Data Spaces](https://mlsanity.com/paper/2308.08938/) (2023)
- [Monotone Individual Fairness](https://mlsanity.com/paper/2403.06812/) (2024)
- [Best Practices for Responsible Machine Learning in Credit Scoring](https://mlsanity.com/paper/2409.20536/) (2024)
- [M2FGB: A Min-Max Gradient Boosting Framework for Subgroup Fairness](https://mlsanity.com/paper/2504.12458/) (2025)
- [Identifying, measuring, and mitigating individual unfairness for supervised learning models and application to credit risk models](https://mlsanity.com/paper/2211.06106/) (2022)
- [Local Statistical Parity for the Estimation of Fair Decision Trees](https://mlsanity.com/paper/2504.18262/) (2025)
- [Fairness-Aware Insurance Pricing: A Multi-Objective Optimization Approach](https://mlsanity.com/paper/2512.24747/) (2025)

## 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.