  Unintended bias in Machine Learning can manifest as systemic differences in
performance for different demographic groups, potentially compounding existing
challenges to fairness in society at large. In this paper, we introduce a suite
of threshold-agnostic metrics that provide a nuanced view of this unintended
bias, by considering the various ways that a classifier's score distribution
can vary across designated groups. We also introduce a large new test set of
online comments with crowd-sourced annotations for identity references. We use
this to show how our metrics can be used to find new and potentially subtle
unintended bias in existing public models.
