Perspective: Machine Learning for Health Should Consider Social Drivers of Health

Clinical machine learning (ML) algorithms can exacerbate a wide range of injustices across multiple domains and levels of society. These harms are often underemphasized and differentially distributed, with minoritized communities disproportionately experiencing the harms and not the benefits of health ML algorithms. By proposing a correspondence between prominent algorithmic harm and social drivers of health (SDOH) frameworks, we show that a range of algorithmic harms ultimately impact human health through SDOH factors, especially structural factors. This presents an inherent tension in the development of ML for health, where the harms of algorithms may lead to the worsening of health inequities. We recommend the consideration of SDOH throughout the pipeline of ML system development for examining algorithmic harms to health. Effectively considering SDOH necessitates developing competencies in structural analysis and community-engaged approaches. Accounting for SDOH could illuminate pathways toward equity-promoting algorithms, although we highlight that, in many cases, an equity-promoting algorithm may not exist. Thus, we also emphasize the need for interventions on the root causes of health inequities.