Governance for Anti-Racist AI in Healthcare: Integrating Racism-Related Stress in Psychiatric Algorithms for Black Americans

While the world is aware of America’s history of enslavement, the ongoing impact of anti-Black racism in the United States remains underemphasized in health intervention modeling. In this Perspective, we argue that algorithmic bias—manifested in the worsened performance of clinical algorithms for Black versus white patients—is significantly driven by the failure to model the cumulative impacts of racism-related stress, particularly racial heteroscedasticity. We assert that current approaches to developing, implementing, and regulating AI in healthcare are insufficient to address the complex ways in which racism influences health outcomes. Accurately capturing these dynamics, and specifically accounting for the pervasive effects of racism on health, is essential for improving racial parity in clinical algorithm performance. Using psychiatric models as a conceptual framework, we outline key principles for anti-racist AI governance in healthcare, including: 1) mandating the inclusion of Black researchers and community members in AI development; 2) implementing rigorous audits to assess anti-Black bias; 3) requiring transparency in how algorithms process race-related data; and 4) establishing accountability measures that prioritize equitable outcomes for Black patients. By integrating these principles into healthcare AI governance, we can develop more equitable and culturally responsive interventions across medical specialties. This anti-racist approach challenges policymakers, researchers, clinicians, and AI developers to fundamentally rethink how AI is created, used, and regulated in healthcare, with profound implications for health policy, clinical practice, and patient outcomes across all medical domains.