  Machine learning (ML) has become a critical tool in public health, offering
the potential to improve population health, diagnosis, treatment selection, and
health system efficiency. However, biases in data and model design can result
in disparities for certain protected groups and amplify existing inequalities
in healthcare. To address this challenge, this study summarizes seminal
literature on ML fairness and presents a framework for identifying and
mitigating biases in the data and model. The framework provides guidance on
incorporating fairness into different stages of the typical ML pipeline, such
as data processing, model design, deployment, and evaluation. To illustrate the
impact of biases in data on ML models, we present examples that demonstrate how
systematic biases can be amplified through model predictions. These case
studies suggest how the framework can be used to prevent these biases and
highlight the need for fair and equitable ML models in public health. This work
aims to inform and guide the use of ML in public health towards a more ethical
and equitable outcome for all populations.
