M$^3$Fair: Mitigating Bias in Healthcare Data through Multi-Level and Multi-Sensitive-Attribute Reweighting Method

Understand

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.