TEACHING POINTS
*Understanding bias in AI requires awareness of coexisting definitions of the term bias framed within the context of AI development and deployment.
*Algorithm fairness is a growing area of research in ML aimed at minimizing differences in model outcomes and potential discrimination involving protected groups, as defined by shared sensitive attributes (eg, age, race, sex).
*Data distribution shift should be anticipated after clinical AI deployment, and practices must be proactive in monitoring AI to prevent clinical action based on erroneous AI results owing to data shift.
*Implementing a formal governance structure to supervise model performance can aid efforts for prospective detection of AI bias.
*Attempting to generalize models developed on specific populations to other groups, especially in the setting of known training dataset bias or discriminatory predictions, introduces inequitable bias and risks augmentation of health disparities.