01677nas a2200253   4500000000100000000000100001008004100002260001200043653002100055653002600076653001900102653002600121653002700147100002100174700002600195700001800221700002200239245005600261856008200317300000900399490000600408520099500414022001401409       2021                            d  c03/202110aAlgorithmic Bias10aLatent Variable Model10aError Analysis10aFair Machine Learning10aMeasurement Invariance1 aLaura Boeschoten1 aErik-Jan van Kesteren1 aAyoub Bagheri1 aDaniel L. Oberski00aAchieving Fair Inference Using Error-Prone Outcomes  uhttps://www.ijimai.org/journal/sites/default/files/2021-02/ijimai_6_5_1_0.pdf  a9-150 v63 aRecently, 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.  a1989-1660