AI Governance: A Challenge for Public Health.

AI AI governance artificial intelligence engagement harm reduction invisible algorithms legislation and jurisprudence modern life one health public health social determinants of health soft law traditional regulation

Journal

JMIR public health and surveillance
ISSN: 2369-2960
Titre abrégé: JMIR Public Health Surveill
Pays: Canada
ID NLM: 101669345

Informations de publication

Date de publication:
30 Sep 2024
Historique:
received: 13 03 2024
revised: 24 06 2024
accepted: 06 07 2024
medline: 30 9 2024
pubmed: 30 9 2024
entrez: 30 9 2024
Statut: epublish

Résumé

The rapid evolution of artificial intelligence (AI) is structuralizing social, political, and economic determinants of health into the invisible algorithms that shape all facets of modern life. Nevertheless, AI holds immense potential as a public health tool, enabling beneficial objectives such as precision public health and medicine. Developing an AI governance framework that can maximize the benefits and minimize the risks of AI is a significant challenge. The benefits of public health engagement in AI governance could be extensive. Here, we describe how several public health concepts can enhance AI governance. Specifically, we explain how (1) harm reduction can provide a framework for navigating the governance debate between traditional regulation and "soft law" approaches; (2) a public health understanding of social determinants of health is crucial to optimally weigh the potential risks and benefits of AI; (3) public health ethics provides a toolset for guiding governance decisions where individual interests intersect with collective interests; and (4) a One Health approach can improve AI governance effectiveness while advancing public health outcomes. Public health theories, perspectives, and innovations could substantially enrich and improve AI governance, creating a more equitable and socially beneficial path for AI development.

Identifiants

pubmed: 39347615
pii: v10i1e58358
doi: 10.2196/58358
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e58358

Informations de copyright

© Jennifer K Wagner, Megan Doerr, Cason D Schmit. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org).

Auteurs

Jennifer K Wagner (JK)

School of Engineering Design and Innovation, Penn State University, University Park, PA, United States.
Department of Anthropology, Penn State University, University Park, PA, United States.
Institute for Computational and Data Sciences, Penn State University, University Park, PA, United States.
Department of Biomedical Engineering, Penn State University, University Park, PA, United States.
Rock Ethics Institute, Penn State University, University Park, PA, United States.
Penn State Law, Penn State University, University Park, PA, United States.
Huck Institutes for the Life Sciences, Penn State University, University Park, PA, United States.

Megan Doerr (M)

Sage Bionetworks, Seattle, WA, United States.

Cason D Schmit (CD)

Population Informatics Lab, Department of Health Policy and Management, School of Public Health, Texas A&M University, College Station, TX, United States.

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