Enhancing Health Equity through Community Engagement in Artificial Intelligence-Driven Prevention Science

• DOI: 10.31235/osf.io/y9r75_v1
• Publication Date: 2025-04-09T08:58:32Z
• Authors: Emily E Haroz; Novalene Goklish; Adrienne Dillard; Roy Adams; Sheana S. Bull; Ricardo Gonzalez-...; Pamela Valenza; Spero M. Manson; J. Roland Thorpe
• Keywords: Equity

Artificial intelligence and machine learning (AI/ML) in prevention science may improve or perpetuate health inequities. Community engagement is one proposed strategy thought to empirically mitigate bias AI/ML tools. We outline how incorporate community at every stage of the model development implementation. Borrowing from a framework for phases research, we describe value application engaging communities help shape more rigorous relevant applications science. provide concrete examples real-world applications, including efforts suicide with Indigenous communities, on chronic disease Hispanic Latino populations, community-driven effort leverage allocation resources focused social determinants Native Hawaiians. This work aims applied community-engagement has been incorporated into implementation, goal encouraging those field consider voices as use such tools grows. Engaging around critical ensure these reach populations need advance equity all.