  In the data-driven artificial intelligence paradigm, models heavily rely on
large amounts of training data. However, factors like sampling distribution
imbalance can lead to issues of bias and unfairness in healthcare data.
Sensitive attributes, such as race, gender, age, and medical condition, are
characteristics of individuals that are commonly associated with discrimination
or bias. In healthcare AI, these attributes can play a significant role in
determining the quality of care that individuals receive. For example, minority
groups often receive fewer procedures and poorer-quality medical care than
white individuals in US. Therefore, detecting and mitigating bias in data is
crucial to enhancing health equity. Bias mitigation methods include
pre-processing, in-processing, and post-processing. Among them, Reweighting
(RW) is a widely used pre-processing method that performs well in balancing
machine learning performance and fairness performance. RW adjusts the weights
for samples within each (group, label) combination, where these weights are
utilized in loss functions. However, RW is limited to considering only a single
sensitive attribute when mitigating bias and assumes that each sensitive
attribute is equally important. This may result in potential inaccuracies when
addressing intersectional bias. To address these limitations, we propose
M3Fair, a multi-level and multi-sensitive-attribute reweighting method by
extending the RW method to multiple sensitive attributes at multiple levels.
Our experiments on real-world datasets show that the approach is effective,
straightforward, and generalizable in addressing the healthcare fairness
issues.
