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Clinical notes are becoming an increasingly important data source for machinelearning (ML) applications in healthcare. Prior research has shown thatdeploying ML models can perpetuate existing biases against racial minorities,as bias can be implicitly embedded in data. In this study, we investigate thelevel of implicit race information available to ML models and human experts andthe implications of model-detectable differences in clinical notes. Our workmakes three key contributions. First, we find that models can identify patientself-reported race from clinical notes even when the notes are stripped ofexplicit indicators of race. Second, we determine that human experts are notable to accurately predict patient race from the same redacted clinical notes.Finally, we demonstrate the potential harm of this implicit information in asimulation study, and show that models trained on these race-redacted clinicalnotes can still perpetuate existing biases in clinical treatment decisions.

Write It Like You See It: Detectable Differences in Clinical Notes By Race Lead To Differential Model Recommendations