Howard University Researchers Awarded NIH AIM-AHEAD Grant To Help Mitigate Harmful Biases in National Health Data

Dr. Anietie Andy

Howard University computer science assistant professor Anietie Andy, Ph.D. (MSc ’09; Ph.D. ’17), recently received a $500K grant from the National Institutes of Health Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD) to build the Howard University Integrated Clinical Data Repository (CDR). Anietie Andy and Abiodun Otolorin, MD, MS, FAAFP, assistant professor of community and family medicine, were named PIs on the project in collaboration with MedStar Health.

The AI/ML field is lacking in diversity in researchers and data, which includes electronic health record data. Harmful biases in how artificial intelligence and machine learning (AI/ML) are used, algorithms are developed, and data is interpreted can continue to increase health disparities and inequities for underrepresented communities.   

Funded through the AIM-AHEAD Data and Infrastructure Capacity Building (DICB) Program, the project will further enable Howard University as a significant contributor of researchers from underrepresented communities to become a major supplier of extremely critical longitudinal minority health datasets and help bridge the gaps created by harmful biases in AI/ML.

Other important members of the Howard University team are co-PIs Thomas Meldman, MD, professor of psychiatry and director of the Center for Clinical and Translational Science, William Southerland, Ph.D., professor of biochemistry, director of the Center for Computational Biology & Bioinformatics, and interim director of the Center for Applied Data Science and Analytics, and Betelihem Tobo, Ph.D., MPH, professor of community and family medicine and director of the Center for Statistics and Data Science.

Andy and Team of HU Researchers
HU Researchers: Anietie Andy, Abiodun Otolorin, Thomas Meldman, William Southerland and Betelihem Tobo

This partnership will also address the underrepresentation of minorities in the biomedical informatics workforce by offering AI/ML and clinical research informatics learning experiences for students, faculty, and staff.


Alumni, Research, College of Engineering and Architecture and Electrical Engineering and Computer Science