Population health AI researchers' perceptions of the public portrayal of AI: A pilot study.

AI artificial intelligence digital data expectations health health technology hype interviews media newspaper qualitative research

Journal

Public understanding of science (Bristol, England)
ISSN: 1361-6609
Titre abrégé: Public Underst Sci
Pays: England
ID NLM: 9306503

Informations de publication

Date de publication:
02 2021
Historique:
pubmed: 22 10 2020
medline: 14 4 2022
entrez: 21 10 2020
Statut: ppublish

Résumé

This article reports how 18 UK and Canadian population health artificial intelligence researchers in Higher Education Institutions perceive the use of artificial intelligence systems in their research, and how this compares with their perceptions about the media portrayal of artificial intelligence systems. This is triangulated with a small scoping analysis of how UK and Canadian news articles portray artificial intelligence systems associated with health research and care. Interviewees had concerns about what they perceived as sensationalist reporting of artificial intelligence systems - a finding reflected in the media analysis. In line with Pickersgill's concept of 'epistemic modesty', they considered artificial intelligence systems better perceived as non-exceptionalist methodological tools that were uncertain and unexciting. Adopting 'epistemic modesty' was sometimes hindered by stakeholders to whom the research is disseminated, who may be less interested in hearing about the uncertainties of scientific practice, having implications on both research and policy.

Identifiants

pubmed: 33084490
doi: 10.1177/0963662520965490
pmc: PMC7859568
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Pagination

196-211

Subventions

Organisme : Wellcome Trust
ID : 213619/Z/18/Z/
Pays : United Kingdom

Références

J Clin Med. 2019 Mar 14;8(3):
pubmed: 30875745
Nat Med. 2019 Jan;25(1):60-64
pubmed: 30617323
Public Underst Sci. 2017 Jan;26(1):55-69
pubmed: 26265709
Public Underst Sci. 2009 Jan;18(1):43-59
pubmed: 19579534
Popul Health Metr. 2010 May 10;8:9
pubmed: 20459720
Sociol Health Illn. 2007 Jan;29(1):46-65
pubmed: 17286705
Sociol Health Illn. 2020 Nov;42(8):1873-1887
pubmed: 32914445
Hastings Cent Rep. 2019 Jan;49(1):36-39
pubmed: 30790317
JOMEC J. 2013 Jun 01;3:10244
pubmed: 33604037
New Genet Soc. 2017 Sep 06;36(4):336-353
pubmed: 29238265
Sci Technol Human Values. 2015 Nov;40(6):998-1021
pubmed: 26527846
J Public Health Manag Pract. 2017 Nov/Dec;23(6):577-580
pubmed: 28166175
Can Assoc Radiol J. 2018 May;69(2):120-135
pubmed: 29655580
Sci Rep. 2015 Mar 09;5:8923
pubmed: 25747871
Health (London). 2010 Nov;14(6):547-63
pubmed: 20974691
J Transl Med. 2020 Jan 9;18(1):14
pubmed: 31918710
Minerva. 2019 Sep;57(3):317-343
pubmed: 31501635
Heliyon. 2018 Nov 27;4(11):e00970
pubmed: 30519662
Sociol Rev Monogr. 2016 Mar;64(1):186-202
pubmed: 27397944
Nat Med. 2019 Jan;25(1):30-36
pubmed: 30617336

Auteurs

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