一句话：首个大规模研究集成学习中公平性如何组合，并给出168个模型基准与公平集成设计启示。
现有公平性研究多聚焦单一分类器，而真实机器学习模型常由多个独立或依赖的学习器以集成方式组合，公平性在其中的组合方式尚不清楚。本文系统研究 Bagging、Boosting、Stacking 与 Voting 等常用集成算法，从 Kaggle 收集 168 个集成模型，并在四个常用公平性数据集上使用现有公平性指标进行分析。结果发现，无需采用偏见缓解技术也可设计出更公平的集成；公平性组合与数据特征之间存在交互，可为公平集成设计提供指导；所构建基准可支持后续研究。这是目前规模较大的集成公平性组合研究之一。
Machine Learning (ML) software has been widely adopted in modern society, with reported fairness implications for minority groups based on race, sex, age, etc. Many recent works have proposed methods to measure and mitigate algorithmic bias in ML models. The existing approaches focus on single classifier-based ML models. However, real-world ML models are often composed of multiple independent or dependent learners in an ensemble (e.g., Random Forest), where the fairness composes in a non-trivial way. How does fairness compose in ensembles? What are the fairness impacts of the learners on the ultimate fairness of the ensemble? Can fair learners result in an unfair ensemble? Furthermore, studies have shown that hyperparameters influence the fairness of ML models. Ensemble hyperparameters are more complex since they affect how learners are combined in different categories of ensembles. Understanding the impact of ensemble hyperparameters on fairness will help programmers design fair ensembles. Today, we do not understand these fully for different ensemble algorithms. In this paper, we comprehensively study popular real-world ensembles: Bagging, Boosting, Stacking, and Voting. We have developed a benchmark of 168 ensemble models collected from Kaggle on four popular fairness datasets. We use existing fairness metrics to understand the composition of fairness. Our results show that ensembles can be designed to be fairer without using mitigation techniques. We also identify the interplay between fairness composition and data characteristics to guide fair ensemble design. Finally, our benchmark can be leveraged for further research on fair ensembles. To the best of our knowledge, this is one of the first and largest studies on fairness composition in ensembles yet presented in the literature.
DOI 原文 · arXiv · PDF（开放获取） · DBLP
@inproceedings{abs-2212-04593,
title = {Towards Understanding Fairness and its Composition in Ensemble Machine Learning},
author = {Usman Gohar and Sumon Biswas and Hridesh Rajan},
booktitle = {ICSE},
year = {2023},
doi = {10.1109/icse48619.2023.00133}
}