Addressing algorithmic bias and the perpetuation of health inequities: An AI bias aware framework

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.

Automated summary

The use of artificial intelligence and machine learning in healthcare is met with optimism and caution. This article focuses on the intersection of AI/ML and racial disparities in healthcare, addressing algorithmic bias that perpetuates health inequalities. The authors describe a four-step analytical process for developing and deploying AI/ML algorithms and offer recommendations for promoting fairness.