Understanding Race as a Key Analytic Dimension in AI-Enabled, Health-Related Social Engagement Online and Its Role and Impact in Health Research

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 Viewpoint, 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 on AI-mediated digital disenfranchisement. We describe how AI-driven online engagement centered on race, ethnicity, and health can facilitate mutual aid and peer support while also posing privacy, reputational, algorithmic, and psychosocial risks when improperly mediated. These effects, alone or combined, can erode trust in digital health research and other engagement initiatives. Therefore, we propose 2 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 the other focused on race-conscious AI performance within the scope of fairness, representation, and downstream health outcomes. Together, these metrics position race and ethnicity as a key analytic dimension, rather than an optional covariate, to guide teams focused on understanding race as a key analytic dimension in AI-enabled, health-related social engagement online and its full role in and impact on health research.