Description
As global health threats like antimicrobial resistance (AMR) escalate, the governance of public health is increasingly shaped by AI-driven tools that promise to integrate disparate data sources and enhance predictive capabilities. This paper critically examines the role of algorithms in AMR surveillance, specifically in the context of the One Health approach, which seeks to integrate data from human, veterinary, agricultural, and environmental sectors. While algorithms promise enhanced efficiency and precision in mapping AMR patterns, they also introduce new governance challenges that mirror existing political and structural hierarchies. Drawing on case studies from the European Union and India, this paper explores how surveillance systems and algorithms—designed to process and analyze vast datasets—are entangled in complex political narratives. Central to this paper is the argument that algorithms, far from being a neutral, objective tool, reinforces the political dynamics of current global governance hierarchies, particularly through the control and framing of data. Building on the concept of "data sovereignty," the paper investigates how algorithms become a mechanism of power to shape access, interpretation, and application of AMR data across borders and sectors. By engaging with the tension between technological potential and political contestation, this paper critically assesses the intersection of algorithms, surveillance, and AMR, revealing how algorithmic processes both challenge and perpetuate global inequities in global governance.
