JMIR Publications

ABSTRACT

Race, ethnicity, online health information sharing, and AI-mediated environments can intersect in ways that may importantly shape individuals’ willingness to share health data with researchers. In this review, we closely examine this intersection by drawing parallels between historical and contemporary public health controversies and discussing recent empirical work centered on privacy-related experiences, data donation, and emerging evidence of AI-mediated digital disenfranchisement. We describe how AI-driven engagement online centered on race/ethnicity and health can simultaneously enable mutual aid and peer-support, as well as privacy, reputational, algorithmic, and psychosocial harms when improperly mediated. These effects, alone or combined, can erode trust in digital health research and other engagement initiatives. Therefore, we propose two complementary sets of evaluation metrics in response: one focused on how race-related online experiences influence trust, consent behavior, and participation in data sharing, and another, focused on race-conscious AI performance within the scope of fairness, representation, and downstream health outcomes. Together, these metrics position race/ethnicity as a key analytic dimension, rather than an optional covariate, to guide teams focused on supporting or developing safe and accountable digital health and AI-supported social engagement online.