Machine learning (ML) models can underperform on certain population groups due to choices made during model development and bias inherent in the data. We categorize sources of discrimination in the ML pipeline into two classes: aleatoric discrimination, which is inherent in the data distribution, and epistemic discrimination, which is due to decisions during model development. We quantify aleatoric discrimination by determining the performance limits of a model under fairness constraints, assuming perfect knowledge of the data distribution. We demonstrate how to characterize aleatoric discrimination by applying Blackwell's results on comparing statistical experiments. We then quantify epistemic discrimination as the gap between a model's accuracy given fairness constraints and the limit posed by aleatoric discrimination. We apply this approach to benchmark existing interventions and investigate fairness risks in data with missing values. Our results indicate that state-of-the-art fairness interventions are effective at removing epistemic discrimination. However, when data has missing values, there is still significant room for improvement in handling aleatoric discrimination.

翻译：由于模型开发过程中的选择和数据固有的偏差,机器学习模式在某些人口群体上可能表现不佳。我们将ML管道中的歧视来源分为两类:在数据分配中固有的偏向歧视,和由于模型开发过程中的决定而产生的偏向歧视。我们通过在公平限制下确定模型的性能限制来量化偏向歧视,假设对数据分布有完全的了解。我们通过应用Blackwell在比较统计实验方面的结果来说明隔离歧视的特点。然后,我们根据公平限制和偏向歧视造成的限制,将特征歧视量化为模式准确性之间的差距。我们采用这一方法对现有干预措施进行基准评估,并调查缺少值的数据中的公平风险。我们的结果表明,在消除偏向歧视方面,最先进的公平干预是有效的。然而,如果数据缺乏价值,在处理偏向歧视方面仍有很大的改进余地。