Fairness in Machine Learning meets with Equity in Healthcare. (arXiv:2305.07041v1 [cs.LG])

With the growing utilization of machine learning in healthcare, there is
increasing potential to enhance healthcare outcomes and efficiency. However,
this also brings the risk of perpetuating biases in data and model design that
can harm certain protected groups based on factors such as age, gender, and
race. This study proposes an artificial intelligence framework, grounded in
software engineering principles, for identifying and mitigating biases in data
and models while ensuring fairness in healthcare settings. A case study is
presented …

age arxiv biases data design efficiency equity fairness gender healthcare machine machine learning model design race risk