# Fair MP-BOOST: Fair and Interpretable Minipatch Boosting

arXiv: https://arxiv.org/abs/2404.01521 · HTML: https://mlsanity.com/paper/2404.01521/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 21 references that are in the map.
- First posted on arXiv 1 Apr 2024.

## Builds on (most cited first, 12 of 12)

- [Mitigating Unwanted Biases with Adversarial Learning](https://mlsanity.com/paper/1801.07593/) (2018)
- [Techniques for Interpretable Machine Learning](https://mlsanity.com/paper/1808.00033/) (2018)
- [Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations](https://mlsanity.com/paper/1707.00075/) (2017)
- [Algorithmic Fairness](https://mlsanity.com/paper/2001.09784/) (2020)
- [Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learning](https://mlsanity.com/paper/2106.02705/) (2021)
- [FairGBM: Gradient Boosting with Fairness Constraints](https://mlsanity.com/paper/2209.07850/) (2022)
- [Fair Adversarial Gradient Tree Boosting](https://mlsanity.com/paper/1911.05369/) (2019)
- [To the Fairness Frontier and Beyond: Identifying, Quantifying, and Optimizing the Fairness-Accuracy Pareto Frontier](https://mlsanity.com/paper/2206.00074/) (2022)
- [Feature Selection for Huge Data via Minipatch Learning](https://mlsanity.com/paper/2010.08529/) (2020)
- [Data Augmentation via Subgroup Mixup for Improving Fairness](https://mlsanity.com/paper/2309.07110/) (2023)
- [MP-Boost: Minipatch Boosting via Adaptive Feature and Observation Sampling](https://mlsanity.com/paper/2011.07218/) (2020)
- [Fair Feature Importance Scores for Interpreting Tree-Based Methods and Surrogates](https://mlsanity.com/paper/2310.04352/) (2023)

## Abstract (arXiv)

Ensemble methods, particularly boosting, have established themselves as highly effective and widely embraced machine learning techniques for tabular data. In this paper, we aim to leverage the robust predictive power of traditional boosting methods while enhancing fairness and interpretability. To achieve this, we develop Fair MP-Boost, a stochastic boosting scheme that balances fairness and accuracy by adaptively learning features and observations during training. Specifically, Fair MP-Boost sequentially samples small subsets of observations and features, termed minipatches (MP), according to adaptively learned feature and observation sampling probabilities. We devise these probabilities by combining loss functions, or by combining feature importance scores to address accuracy and fairness simultaneously. Hence, Fair MP-Boost prioritizes important and fair features along with challenging instances, to select the most relevant minipatches for learning. The learned probability distributions also yield intrinsic interpretations of feature importance and important observations in Fair MP-Boost. Through empirical evaluation of simulated and benchmark datasets, we showcase the interpretability, accuracy, and fairness of Fair MP-Boost.