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Article: Predicting and preferring

TitlePredicting and preferring
Authors
KeywordsAI
medical ethics
patient preference predictors
PPP
preference shaping
Issue Date25-Sep-2023
PublisherTaylor and Francis Group
Citation
Inquiry, 2023 How to Cite?
Abstract

The use of machine learning, or “artificial intelligence” (AI) in medicine is widespread and growing. In this paper, I focus on a specific proposed clinical application of AI: using models to predict incapacitated patients’ treatment preferences. Drawing on results from machine learning, I argue this proposal faces a special moral problem. Machine learning researchers owe us assurance on this front before experimental research can proceed. In my conclusion I connect this concern to broader issues in AI safety.


Persistent Identifierhttp://hdl.handle.net/10722/340930
ISSN
2023 Impact Factor: 1.0
2023 SCImago Journal Rankings: 0.769
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorSharadin, Nathaniel-
dc.date.accessioned2024-03-11T10:48:23Z-
dc.date.available2024-03-11T10:48:23Z-
dc.date.issued2023-09-25-
dc.identifier.citationInquiry, 2023-
dc.identifier.issn0020-174X-
dc.identifier.urihttp://hdl.handle.net/10722/340930-
dc.description.abstract<p>The use of machine learning, or “artificial intelligence” (AI) in medicine is widespread and growing. In this paper, I focus on a specific proposed clinical application of AI: using models to predict incapacitated patients’ treatment preferences. Drawing on results from machine learning, I argue this proposal faces a special moral problem. Machine learning researchers owe us assurance on this front before experimental research can proceed. In my conclusion I connect this concern to broader issues in AI safety.<br></p>-
dc.languageeng-
dc.publisherTaylor and Francis Group-
dc.relation.ispartofInquiry-
dc.subjectAI-
dc.subjectmedical ethics-
dc.subjectpatient preference predictors-
dc.subjectPPP-
dc.subjectpreference shaping-
dc.titlePredicting and preferring-
dc.typeArticle-
dc.identifier.doi10.1080/0020174x.2023.2261493-
dc.identifier.scopuseid_2-s2.0-85172789581-
dc.identifier.eissn1502-3923-
dc.identifier.isiWOS:001071526100001-
dc.identifier.issnl0020-174X-

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