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

Carter, DJ; Byrne, MK; Djordjevic, SP; Robertson, H; Labbate, M; Morgan, BS; Billington, L

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

Carter, DJ Byrne, MK Djordjevic, SP Robertson, H Labbate, M Morgan, BS Billington, L

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Publication Type:: Journal Article
Citation:: J Law Med, 2023, 30, (1), pp. 179-190
Issue Date:: 2023-05

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Field	Value	Language
dc.contributor.author	Carter, DJ
dc.contributor.author	Byrne, MK
dc.contributor.author	Djordjevic, SP
dc.contributor.author	Robertson, H
dc.contributor.author	Labbate, M
dc.contributor.author	Morgan, BS
dc.contributor.author	Billington, L https://orcid.org/0000-0003-0011-1221
dc.date.accessioned	2023-09-29T05:23:56Z
dc.date.available	2023-09-29T05:23:56Z
dc.date.issued	2023-05
dc.identifier.citation	J Law Med, 2023, 30, (1), pp. 179-190
dc.identifier.issn	1320-159X
dc.identifier.uri	http://hdl.handle.net/10453/172386
dc.description.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.
dc.format	Print
dc.language	eng
dc.relation.ispartof	J Law Med
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	11 Medical and Health Sciences, 18 Law and Legal Studies, 22 Philosophy and Religious Studies
dc.subject.classification	4203 Health services and systems
dc.subject.classification	4804 Law in context
dc.subject.mesh	Animals
dc.subject.mesh	Humans
dc.subject.mesh	Artificial Intelligence
dc.subject.mesh	COVID-19
dc.subject.mesh	Anti-Bacterial Agents
dc.subject.mesh	Australia
dc.subject.mesh	Drug Resistance, Bacterial
dc.subject.mesh	Animals
dc.subject.mesh	Humans
dc.subject.mesh	Anti-Bacterial Agents
dc.subject.mesh	Drug Resistance, Bacterial
dc.subject.mesh	Artificial Intelligence
dc.subject.mesh	Australia
dc.subject.mesh	COVID-19
dc.subject.mesh	Animals
dc.subject.mesh	Humans
dc.subject.mesh	Artificial Intelligence
dc.subject.mesh	COVID-19
dc.subject.mesh	Anti-Bacterial Agents
dc.subject.mesh	Australia
dc.subject.mesh	Drug Resistance, Bacterial
dc.title	Personal Data for Public Benefit: The Regulatory Determinants of Social Licence for Technologically Enhanced Antimicrobial Resistance Surveillance.
dc.type	Journal Article
utslib.citation.volume	30
utslib.location.activity	Australia
utslib.for	11 Medical and Health Sciences
utslib.for	18 Law and Legal Studies
utslib.for	22 Philosophy and Religious Studies
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Law
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Strength - AIMI - Australian Institute for Microbiology & Infection
utslib.copyright.status	closed_access	*
dc.date.updated	2023-09-29T05:23:55Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	30
utslib.citation.issue	1

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.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/172386