  Clinical notes are becoming an increasingly important data source for machine
learning (ML) applications in healthcare. Prior research has shown that
deploying ML models can perpetuate existing biases against racial minorities,
as bias can be implicitly embedded in data. In this study, we investigate the
level of implicit race information available to ML models and human experts and
the implications of model-detectable differences in clinical notes. Our work
makes three key contributions. First, we find that models can identify patient
self-reported race from clinical notes even when the notes are stripped of
explicit indicators of race. Second, we determine that human experts are not
able to accurately predict patient race from the same redacted clinical notes.
Finally, we demonstrate the potential harm of this implicit information in a
simulation study, and show that models trained on these race-redacted clinical
notes can still perpetuate existing biases in clinical treatment decisions.
