Addressing algorithmic bias and the perpetuation of health inequities: An AI bias aware framework. Issue 1 (March 2023) - Agarwal, R.; Bjarnadottir, M.; Rhue, L.; Dugas, M.; Crowley, K.; Clark, J.; Gao, G.
Highlights: Artificial intelligence and machine learning (AI/ML) applications in healthcare could have algorithmic biases that perpetuate health disparities. We describe a four-step analytical process for building and deploying AI/ML algorithms. We highlight both the sources of bias and methods for bias mitigation in the above processes. Abstract: The emergence and increasing use of artificial intelligence and machine learning (AI/ML) in healthcare practice and delivery is being greeted with both optimism and caution. We focus on the nexus of AI/ML and racial disparities in healthcare: an issue that must be addressed if the promise of AI to improve patient care and health outcomes is to be realized in an equitable manner for all populations. We unpack the challenge of algorithmic bias that may perpetuate health disparities. Synthesizing research from multiple disciplines, we describe a four-step analytical process used to build and deploy AI/ML algorithms and solutions, highlighting both the sources of bias as well as methods for bias mitigation. Finally, we offer recommendations for moving the pursuit of fairness further.