  The adoption of diagnosis and prognostic algorithms in healthcare has led to
concerns about the perpetuation of bias against disadvantaged groups of
individuals. Deep learning methods to detect and mitigate bias have revolved
around modifying models, optimization strategies, and threshold calibration
with varying levels of success. Here, we generate a data-centric,
model-agnostic, task-agnostic approach to evaluate dataset bias by
investigating the relationship between how easily different groups are learned
at small sample sizes (AEquity). We then apply a systematic analysis of AEq
values across subpopulations to identify and mitigate manifestations of racial
bias in two known cases in healthcare - Chest X-rays diagnosis with deep
convolutional neural networks and healthcare utilization prediction with
multivariate logistic regression. AEq is a novel and broadly applicable metric
that can be applied to advance equity by diagnosing and remediating bias in
healthcare datasets.
