Personal Data for Public Benefit: The Regulatory Determinants of Social Licence for Technologically Enhanced Antimicrobial Resistance Surveillance.

Abstract

Technologically enhanced surveillance systems have been proposed for the task of monitoring and responding to antimicrobial resistance (AMR) in both human, animal and environmental contexts. The use of these systems is in their infancy, although the advent of COVID-19 has progressed similar technologies in response to that pandemic. We conducted qualitative research to identify the Australian public's key concerns about the ethical, legal and social implications of an artificial intelligence (AI) and machine learning-enhanced One Health AMR surveillance system. Our study provides preliminary evidence of public support for AI/machine learning-enhanced One Health monitoring systems for AMR, provided that three main conditions are met: personal health care data must be deidentified; data use and access must be tightly regulated under strong governance; and the system must generate high-quality, reliable analyses to guide trusted health care decision-makers.