Translating Intersectionality to Fair Machine Learning in Health Sciences

Machine learning (ML)-derived tools are rapidly being deployed as an additional input in the clinical decision-making process to optimize health interventions. However, ML models also risk propagating societal discrimination and exacerbating existing health inequities. The field of ML fairness has focused on developing approaches to mitigate bias in ML models. To date, the focus has been on the model fitting process, simplifying the processes of structural discrimination to definitions of model bias based on performance metrics. Here, we reframe the ML task through the lens of intersectionality, a Black feminist theoretical framework that contextualizes individuals in interacting systems of power and oppression, linking inquiry into measuring fairness to the pursuit of health justice. In doing so, we present intersectional ML fairness as a paradigm shift that moves from an emphasis on model metrics to an approach for ML that is centered around achieving more equitable health outcomes.