RCMI@Morgan: Center for Urban Health Disparities Research and Innovation

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Project Summary Characterization of health disparities in African ancestry and reduction of algorithmic bias In the epoch of big data, algorithms are present throughout society, transforming it into more personalized and flatter. One primary convergence is the application of algorithms for biology, medicine, and health care. Nonetheless, a recent study shows that a widely used algorithm, typical of this industry-wide approach and affecting millions of patients, exhibits significant racial bias: At a given risk score, Black patients are considerably sicker than White patients, as evidenced by signs of uncontrolled illnesses. This is a specific example of a broader issue known as algorithmic bias, wherein algorithms reinstate the cultural biases encoded in the data sets they are trained on. Increasing appreciation for the impact of algorithmic bias has led to a corresponding call for algorithmic accountability. Caplan et al. define "Algorithmic accountability ultimately refers to the assignment of responsibility for how an algorithm is created and its impact on society; if harm occurs, accountable systems include a mechanism for redress" (2018: 10). That is, algorithmic accountability includes harm reduction and prevention considerations in both the design of the algorithm and its effects. These issues of “algorithmic bias” and “algorithmic accountability” also involve in health disparities in African ancestry exposed in the parent RCMI award with following goals: (1) Enhance MSU's health disparities research infrastructure and capacity in both basic biomedical and behavioral/public health research, (2) Enhance high-quality research, including translational research, on urban health and health disparities through increased external funding, publications and scientific services to the community, (3) Facilitate collaborations between basic biomedical and social/behavioral faculty researchers and create a collaborative and supportive environment for faculty career development, especially for new and early career faculty and (4) Build sustainable partnerships with two research-intensive institutions, Johns Hopkins University and the University of Maryland, Baltimore, as well as local government and community-based organizations dealing with health disparities. In the Aim 1 for this project, the current metrics of the parent RCMI award will be redefined with statistics on the probability distribution space in the spectra of gender, race, and socioeconomic status. Potential data bias in de-identification will be filtered out before characterizing “algorithmic bias.” Based on the redefined metrics of Aim 1, AI techniques will be advanced toward precision medicine considering the broad spectra of population and ancestry to reduce the identified biases. Specifically, inference and learning algorithms will be developed on probability spaces with meta-learning via Bayesian optimization with regularization. We will host public seminars and workshops with case studies covering the topics in the interaction among ethics, AI, and health disparities. The analysis framework and tool will be publicly disseminated and will have extensibility and integrability with the public portal analytics of "All of Us."