  With the growing prevalence of machine learning and artificial
intelligence-based medical decision support systems, it is equally important to
ensure that these systems provide patient outcomes in a fair and equitable
fashion. This paper presents an innovative framework for detecting areas of
algorithmic bias in medical-AI decision support systems. Our approach
efficiently identifies potential biases in medical-AI models, specifically in
the context of sepsis prediction, by employing the Classification and
Regression Trees (CART) algorithm with conformity scores. We verify our
methodology by conducting a series of synthetic data experiments, showcasing
its ability to estimate areas of bias in controlled settings precisely. The
effectiveness of the concept is further validated by experiments using
electronic medical records from Grady Memorial Hospital in Atlanta, Georgia.
These tests demonstrate the practical implementation of our strategy in a
clinical environment, where it can function as a vital instrument for
guaranteeing fairness and equity in AI-based medical decisions.
