AI-based healthcare: a new dawn or apartheid revisited?

Artificial intelligence Bias Healthcare History Mathematics

Journal

AI & society
ISSN: 0951-5666
Titre abrégé: AI Soc
Pays: Germany
ID NLM: 9883157

Informations de publication

Date de publication:
2021
Historique:
received: 14 11 2020
accepted: 16 11 2020
pubmed: 29 12 2020
medline: 29 12 2020
entrez: 28 12 2020
Statut: ppublish

Résumé

The Bubonic Plague outbreak that wormed its way through San Francisco's Chinatown in 1900 tells a story of prejudice guiding health policy, resulting in enormous suffering for much of its Chinese population. This article seeks to discuss the potential for hidden "prejudice" should Artificial Intelligence (AI) gain a dominant foothold in healthcare systems. Using a toy model, this piece explores potential future outcomes, should AI continue to develop without bound. Where potential dangers may lurk will be discussed, so that the full benefits AI has to offer can be reaped whilst avoiding the pitfalls. The model is produced using the computer programming language MATLAB and offers visual representations of potential outcomes. Interwoven with these potential outcomes are numerous historical models for problems caused by prejudice and recent issues in AI systems, from police prediction and facial recognition software to recruitment tools. Therefore, this research's novel angle, of using historical precedents to model and discuss potential futures, offers a unique contribution. The online version contains supplementary material available at 10.1007/s00146-020-01120-w.

Identifiants

pubmed: 33362363
doi: 10.1007/s00146-020-01120-w
pii: 1120
pmc: PMC7754701
doi:

Types de publication

Journal Article

Langues

eng

Pagination

983-999

Subventions

Organisme : Medical Research Council
ID : MR/K000799/1
Pays : United Kingdom

Informations de copyright

© The Author(s) 2020.

Références

PLoS One. 2017 Apr 4;12(4):e0174944
pubmed: 28376093
Sci Rep. 2018 Sep 5;8(1):13247
pubmed: 30185868
BMC Nephrol. 2019 Feb 14;20(1):56
pubmed: 30764796

Auteurs

Alice Parfett (A)

Integrated PhD student at the Centre of Doctoral Training for Accountable, Responsible and Transparent AI, University of Bath, Claverton Down, Bath, BA2 7AY UK.
BSc from the University of Exeter, Penryn Campus, Penryn,, TR10 9FE Cornwall UK.

Stuart Townley (S)

University of Exeter (Environment and Sustainability Institute), Penryn, Cornwall UK.

Kristofer Allerfeldt (K)

University of Exeter (Humanities), Penryn, Cornwall UK.

Classifications MeSH