Tracking funding disparities in global health aid with machine learning

Machine learning offers a transparent lens to quantify and address systemic inequities in the allocation of international health resources. By identifying patterns in complex datasets, these models empower policymakers to direct aid more effectively toward regions historically overlooked by global health initiatives.