Achieving Fair Inference Using Error-Prone Outcomes

Recently, an increasing amount of research has focused on methods to assess and account for fairness criteria when predicting ground truth targets in supervised learning. However, recent literature has shown that prediction unfairness can potentially arise due to measurement error when target labels are error prone. In this study we demonstrate that existing methods to assess and calibrate fairness criteria do not extend to the true target variable of interest, when an error-prone proxy target is used. As a solution to this problem, we suggest a framework that combines two existing fields of research: fair ML methods, such as those found in the counterfactual fairness literature and measurement models found in the statistical literature. Firstly, we discuss these approaches and how they can be combined to form our framework. We also show that, in a healthcare decision problem, a latent variable model to account for measurement error removes the unfairness detected previously.