Race Correction and Algorithmic Bias in Atrial Fibrillation Wearable Technologies.

Abstract

Stakeholders in biomedicine are evaluating how race corrections in clinical algorithms inequitably allocate health care resources on the basis of a misunderstanding of race-as-genetic difference. Ostensibly used to intervene on persistent disparities in health outcomes across different racial groups, these troubling corrections in risk assessments embed essentialist ideas of race as a biological reality, rather than a social and political construct that reproduces a racial hierarchy, into practice guidelines. This article explores the harms of such race corrections by considering how the technologies we use to account for disparities in health outcomes can actually innovate and amplify these harms. Focusing on the design of wearable digital health technologies that use photoplethysmographic sensors to detect atrial fibrillation, we argue that these devices, which are notoriously poor in accurately functioning on users with darker skin tones, embed a subtle form of race correction that presupposes the need for explicit adjustments in the clinical interpretation of their data outputs. We point to research on responsible innovation in health, and its commitment to being responsive in addressing inequities and harms, as a way forward for those invested in the elimination of race correction.