Governance for anti-racist AI in healthcare: integrating racism-related stress in psychiatric algorithms for Black Americans.

Abstract

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. This Perspective argues that algorithmic bias-manifested in the worsened performance of clinical algorithms for Black vs. white patients-is significantly driven by the failure to model the cumulative impacts of racism-related stress, particularly racial heteroscedasticity. Racial heteroscedasticity refers to the unequal variance in health outcomes and algorithmic predictions across racial groups, driven by differential exposure to racism-related stress. This may be particularly salient for Black Americans, where anti-Black bias has wide-ranging impacts that interact with differing backgrounds of generational trauma, socioeconomic status, and other social factors, promoting unaccounted for sources of variance that are not easily captured with a blanket "race" factor. Not accounting for these factors deteriorates performance for these clinical algorithms for all Black patients. 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, AI can be developed to produce more equitable and culturally responsive healthcare interventions. 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.