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

> **NIH NIH U54** · MORGAN STATE UNIVERSITY · 2022 · $296,709

## Abstract

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 ...

## Key facts

- **NIH application ID:** 10599734
- **Project number:** 3U54MD013376-04S3
- **Recipient organization:** MORGAN STATE UNIVERSITY
- **Principal Investigator:** Valerie Odero-Marah
- **Activity code:** U54 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $296,709
- **Award type:** 3
- **Project period:** 2019-07-31 → 2024-02-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10599734

## Citation

> US National Institutes of Health, RePORTER application 10599734, RCMI@Morgan: Center for Urban Health Disparities Research and Innovation (3U54MD013376-04S3). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10599734. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*