论文标题
公平地推断出容易产生错误的结果
Fair inference on error-prone outcomes
论文作者
论文摘要
监督学习的公平推断是研究的重要和积极的领域，在预测地面真相目标时产生了一系列有用的方法来评估和说明公平标准。但是，如最近的工作所示，当目标标签容易出错时，潜在的预测不公平可能是由于测量误差引起的。在本文中，我们表明，当使用容易出错的代理目标时，现有的评估和校准公平标准的方法不会扩展到感兴趣的真正目标变量。为了解决这个问题，我们建议由两种现有文献的组合产生的框架：公平的ML方法，例如一方面的反事实公平文献中发现的方法，另一方面是统计文献中发现的测量模型。我们讨论这些方法及其连接导致我们的框架。在医疗保健决策问题中，我们发现使用潜在变量模型来解释测量误差可以消除之前检测到的不公平性。
Fair inference in supervised learning is an important and active area of research, yielding a range of useful methods to assess and account for fairness criteria when predicting ground truth targets. As shown in recent work, however, when target labels are error-prone, potential prediction unfairness can arise from measurement error. In this paper, we show that, when an error-prone proxy target is used, existing methods to assess and calibrate fairness criteria do not extend to the true target variable of interest. To remedy this problem, we suggest a framework resulting from the combination of two existing literatures: fair ML methods, such as those found in the counterfactual fairness literature on the one hand, and, on the other, measurement models found in the statistical literature. We discuss these approaches and their connection resulting in our framework. In a healthcare decision problem, we find that using a latent variable model to account for measurement error removes the unfairness detected previously.
