Machine learning (ML) may be oblivious to human bias but it is not immune to its perpetuation. Marginalisation and iniquitous group representation are often traceable in the very data used for training, and may be reflected or even enhanced by the learning models. In the present work, we aim at clarifying the role played by data geometry in the emergence of ML bias. We introduce an exactly solvable high-dimensional model of data imbalance, where parametric control over the many bias-inducing factors allows for an extensive exploration of the bias inheritance mechanism. Through the tools of statistical physics, we analytically characterise the typical properties of learning models trained in this synthetic framework and obtain exact predictions for the observables that are commonly employed for fairness assessment. Despite the simplicity of the data model, we retrace and unpack typical unfairness behaviour observed on real-world datasets. We also obtain a detailed analytical characterisation of a class of bias mitigation strategies. We first consider a basic loss-reweighing scheme, which allows for an implicit minimisation of different unfairness metrics, and quantify the incompatibilities between some existing fairness criteria. Then, we consider a novel mitigation strategy based on a matched inference approach, consisting in the introduction of coupled learning models. Our theoretical analysis of this approach shows that the coupled strategy can strike superior fairness-accuracy trade-offs.
翻译:在目前的工作中,我们的目标是澄清数据几何在出现ML偏差时所起的作用。我们引入了完全可以溶解的高维数据不平衡模型,对许多偏差诱因的控制允许广泛探索偏差继承机制。我们通过统计物理工具分析这一合成框架所培训的学习模型的典型特性,并用精确的预测来判断通常用于公平评估的观测结果。尽管数据模型简洁,我们还是要回溯和解析现实世界数据集所观察到的典型不公平行为。我们还获得了一个详细分析的减少偏差战略分类的高度模型。我们首先考虑一种基本的损失比重方案,它可以隐含地减少不同的不公平度指标,并量化在这种合成框架中所培训的学习模型的典型特性,并获得对通常用于公平评估的观测结果的准确预测。尽管数据模型简单,我们还是要追溯和解析在现实世界数据集中观察到的典型的不公平行为。我们还能够详细分析一种减轻偏差战略的分类。我们首先考虑一种基本的损失比重方案,这样可以隐含地减少不同的不公平度指标,并且用量化某种不相容性的方法在采用一种基于理论分析的方法中相互平衡的战略。我们后来可以比较地研究。</s>