Ensembling independent deep neural networks (DNNs) is a simple and effective
way to improve top-line metrics and to outperform larger single models. In this
work, we go beyond top-line metrics and instead explore the impact of
ensembling on subgroup performances. Surprisingly, even with a simple
homogenous ensemble -- all the individual models share the same training set,
architecture, and design choices -- we find compelling and powerful gains in
worst-k and minority group performance, i.e. fairness naturally emerges from
ensembling. We show that the gains in performance from ensembling for the
minority group continue for far longer than for the majority group as more
models are added. Our work establishes that simple DNN ensembles can be a
powerful tool for alleviating disparate impact from DNN classifiers, thus
curbing algorithmic harm. We also explore why this is the case. We find that
even in homogeneous ensembles, varying the sources of stochasticity through
parameter initialization, mini-batch sampling, and the data-augmentation
realizations, results in different fairness outcomes.

通过分析异质性模型，我们探究了不同培训集合的神经网络集成对子群体表现的影响。发现使用同质集成，即使所有单独模型使用相同的训练集，架构和设计选择，仍然可以获得令人信服和有力的最坏 k 值和少数群体的表现提升。我们的研究证明，简单的神经网络集成可以是减轻不同 DNN 分类器的不同影响的有效工具，从而遏制算法伤害。

FAIR-Ensemble: 深度集成自然产生公平性

FAIR-Ensemble: When Fairness Naturally Emerges From Deep Ensembling

Collecting more diverse and representative training data is often touted as a
remedy for the disparate performance of machine learning predictors across
subpopulations. However, a precise framework for understanding how dataset
properties like diversity affect learning outcomes is largely lacking. By
casting data collection as part of the learning process, we demonstrate that
diverse representation in training data is key not only to increasing subgroup
performances, but also to achieving population level objectives. Our analysis
and experiments describe how dataset compositions influence performance and
provide constructive results for using trends in existing data, alongside
domain knowledge, to help guide intentional, objective-aware dataset design.

通过将数据收集视为学习过程的一部分，我们表明不仅多样化的训练数据可以提高子群体的性能，还可以实现人口水平的目标。我们的分析和实验描述了数据集组成如何影响性能，并提供了建设性的结果，以利用现有数据的趋势和领域知识，帮助指导有意识、具有目标意识的数据集设计。