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Conservatism predicts aversion to consequential Artificial Intelligence

Artificial intelligence (AI) has the potential to revolutionize society by automating tasks as diverse as driving cars, diagnosing diseases, and providing legal advice. The degree to which AI can improve outcomes in these and other domains depends on how comfortable people are trusting AI for these...

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Detalles Bibliográficos
Autores principales: Castelo, Noah, Ward, Adrian F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8687590/
https://www.ncbi.nlm.nih.gov/pubmed/34928989
http://dx.doi.org/10.1371/journal.pone.0261467
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author Castelo, Noah
Ward, Adrian F.
author_facet Castelo, Noah
Ward, Adrian F.
author_sort Castelo, Noah
collection PubMed
description Artificial intelligence (AI) has the potential to revolutionize society by automating tasks as diverse as driving cars, diagnosing diseases, and providing legal advice. The degree to which AI can improve outcomes in these and other domains depends on how comfortable people are trusting AI for these tasks, which in turn depends on lay perceptions of AI. The present research examines how these critical lay perceptions may vary as a function of conservatism. Using five survey experiments, we find that political conservatism is associated with low comfort with and trust in AI—i.e., with AI aversion. This relationship between conservatism and AI aversion is explained by the link between conservatism and risk perception; more conservative individuals perceive AI as being riskier and are therefore more averse to its adoption. Finally, we test whether a moral reframing intervention can reduce AI aversion among conservatives.
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spelling pubmed-86875902021-12-21 Conservatism predicts aversion to consequential Artificial Intelligence Castelo, Noah Ward, Adrian F. PLoS One Research Article Artificial intelligence (AI) has the potential to revolutionize society by automating tasks as diverse as driving cars, diagnosing diseases, and providing legal advice. The degree to which AI can improve outcomes in these and other domains depends on how comfortable people are trusting AI for these tasks, which in turn depends on lay perceptions of AI. The present research examines how these critical lay perceptions may vary as a function of conservatism. Using five survey experiments, we find that political conservatism is associated with low comfort with and trust in AI—i.e., with AI aversion. This relationship between conservatism and AI aversion is explained by the link between conservatism and risk perception; more conservative individuals perceive AI as being riskier and are therefore more averse to its adoption. Finally, we test whether a moral reframing intervention can reduce AI aversion among conservatives. Public Library of Science 2021-12-20 /pmc/articles/PMC8687590/ /pubmed/34928989 http://dx.doi.org/10.1371/journal.pone.0261467 Text en © 2021 Castelo, Ward https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Castelo, Noah
Ward, Adrian F.
Conservatism predicts aversion to consequential Artificial Intelligence
title Conservatism predicts aversion to consequential Artificial Intelligence
title_full Conservatism predicts aversion to consequential Artificial Intelligence
title_fullStr Conservatism predicts aversion to consequential Artificial Intelligence
title_full_unstemmed Conservatism predicts aversion to consequential Artificial Intelligence
title_short Conservatism predicts aversion to consequential Artificial Intelligence
title_sort conservatism predicts aversion to consequential artificial intelligence
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8687590/
https://www.ncbi.nlm.nih.gov/pubmed/34928989
http://dx.doi.org/10.1371/journal.pone.0261467
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