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 surveillance, data, and algorithms in relations to AMR, specifically in the context of the One Health approach, which seeks to integrate data from human, veterinary, agricultural, and environmental sectors. While surveillance and the advances of AI promise enhanced efficiency and precision in mapping AMR patterns, they also introduce new governance challenges that mirror existing political and structural inequities. Drawing on case studies from the European Union and India, this paper explores how surveillance systems and algorithms are entangled in complex political narratives. The recent inclusion of mandatory AMR monitoring in urban wastewater under the EU’s revised Urban Wastewater Treatment Directive (UWWTD) further highlights these tensions. While it marks a significant move toward formalised regulation, it leaves open critical questions about what should be measured, how, and by whom—revealing a persistent ambiguity at the heart of global governance for health. Central to this paper is the argument that data and the mechanism that produce them, far from being a neutral, reinforce the political dynamics of current global governance hierarchies. Building on the concept of "data sovereignty" and quasi sovereignty, the paper investigates how data become a mechanism of power to shape access, interpretation, and application of AMR data across borders and sectors.
