Variance in predictions across different trained models is a significant, under-explored source of error in fair classification. In practice, the variance on some data examples is so large that decisions can be effectively arbitrary. To investigate this problem, we take an experimental approach and make four overarching contributions: We 1) Define a metric called self-consistency, derived from variance, which we use as a proxy for measuring and reducing arbitrariness; 2) Develop an ensembling algorithm that abstains from classification when a prediction would be arbitrary; 3) Conduct the largest to-date empirical study of the role of variance (vis-a-vis self-consistency and arbitrariness) in fair classification; and, 4) Release a toolkit that makes the US Home Mortgage Disclosure Act (HMDA) datasets easily usable for future research. Altogether, our experiments reveal shocking insights about the reliability of conclusions on benchmark datasets. Most fairness classification benchmarks are close-to-fair when taking into account the amount of arbitrariness present in predictions -- before we even try to apply common fairness interventions. This finding calls into question the practical utility of common algorithmic fairness methods, and in turn suggests that we should fundamentally reconsider how we choose to measure fairness in machine learning.

翻译：不同训练模型预测中的方差是公平分类中一个重大且尚未充分探索的误差来源。在实践中，某些数据样本的方差如此之大，以至于决策实际上可以变得任意。为研究此问题，我们采用实验方法并做出四项核心贡献：1) 定义一项源自方差的度量指标——自一致性，将其作为衡量和减少任意性的代理；2) 开发一种集成算法，在预测具有任意性时选择放弃分类；3) 开展迄今为止规模最大的实证研究，探讨方差（相对于自一致性与任意性）在公平分类中的作用；4) 发布一个工具包，使美国住房抵押贷款披露法案（HMDA）数据集便于未来研究使用。总体而言，我们的实验揭示了关于基准数据集结论可靠性的惊人见解：在考虑预测中存在的任意性程度后，大多数公平分类基准本身已接近公平——甚至在我们尝试应用常见公平干预措施之前。这一发现对常见算法公平方法的实际效用提出质疑，进而表明我们应从根本上重新思考如何在机器学习中衡量公平性。