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：机器学习模型可能因开发过程中的选择以及数据固有的偏差，在某些群体上表现欠佳。我们将机器学习流水线中的歧视来源分为两类：偶然性歧视（由数据分布固有特性导致）和认知性歧视（由模型开发过程中的决策导致）。通过假设对数据分布的完美认知，我们量化了在公平性约束下模型的性能极限，从而界定偶然性歧视。利用布莱克威尔关于统计实验比较的研究成果，我们展示了如何表征偶然性歧视。随后，我们将认知性歧视定义为在公平性约束下模型的准确率与偶然性歧视所设极限之间的差距。我们应用这一方法对现有干预措施进行基准测试，并探究含缺失值数据中的公平性风险。结果表明，当前最先进的公平性干预措施能有效消除认知性歧视，但当数据存在缺失值时，处理偶然性歧视方面仍有显著改进空间。