  Label bias occurs when the outcome of interest is not directly observable and
instead modeling is performed with proxy labels. When the difference between
the true outcome and the proxy label is correlated with predictors, this can
yield systematic disparities in predictions for different groups of interest.
We propose Bayesian hierarchical measurement models to address these issues.
Through practical examples, we demonstrate how our approach improves accuracy
and helps with algorithmic fairness.
