  Errors in labels obtained via human annotation adversely affect a model's
performance. Existing approaches propose ways to mitigate the effect of label
error on a model's downstream accuracy, yet little is known about its impact on
a model's disparity metrics. Here we study the effect of label error on a
model's disparity metrics. We empirically characterize how varying levels of
label error, in both training and test data, affect these disparity metrics. We
find that group calibration and other metrics are sensitive to train-time and
test-time label error -- particularly for minority groups. This disparate
effect persists even for models trained with noise-aware algorithms. To
mitigate the impact of training-time label error, we present an approach to
estimate the influence of a training input's label on a model's group disparity
metric. We empirically assess the proposed approach on a variety of datasets
and find significant improvement, compared to alternative approaches, in
identifying training inputs that improve a model's disparity metric. We
complement the approach with an automatic relabel-and-finetune scheme that
produces updated models with, provably, improved group calibration error.
