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Adopting AI: how familiarity breeds both trust and contempt
Despite pronouncements about the inevitable diffusion of artificial intelligence and autonomous technologies, in practice, it is human behavior, not technology in a vacuum, that dictates how technology seeps into—and changes—societies. To better understand how human preferences shape technological a...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer London
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10175926/ https://www.ncbi.nlm.nih.gov/pubmed/37358948 http://dx.doi.org/10.1007/s00146-023-01666-5 |
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author | Horowitz, Michael C. Kahn, Lauren Macdonald, Julia Schneider, Jacquelyn |
author_facet | Horowitz, Michael C. Kahn, Lauren Macdonald, Julia Schneider, Jacquelyn |
author_sort | Horowitz, Michael C. |
collection | PubMed |
description | Despite pronouncements about the inevitable diffusion of artificial intelligence and autonomous technologies, in practice, it is human behavior, not technology in a vacuum, that dictates how technology seeps into—and changes—societies. To better understand how human preferences shape technological adoption and the spread of AI-enabled autonomous technologies, we look at representative adult samples of US public opinion in 2018 and 2020 on the use of four types of autonomous technologies: vehicles, surgery, weapons, and cyber defense. By focusing on these four diverse uses of AI-enabled autonomy that span transportation, medicine, and national security, we exploit the inherent variation between these AI-enabled autonomous use cases. We find that those with familiarity and expertise with AI and similar technologies were more likely to support all of the autonomous applications we tested (except weapons) than those with a limited understanding of the technology. Individuals that had already delegated the act of driving using ride-share apps were also more positive about autonomous vehicles. However, familiarity cut both ways; individuals are also less likely to support AI-enabled technologies when applied directly to their life, especially if technology automates tasks they are already familiar with operating. Finally, we find that familiarity plays little role in support for AI-enabled military applications, for which opposition has slightly increased over time. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00146-023-01666-5. |
format | Online Article Text |
id | pubmed-10175926 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer London |
record_format | MEDLINE/PubMed |
spelling | pubmed-101759262023-05-14 Adopting AI: how familiarity breeds both trust and contempt Horowitz, Michael C. Kahn, Lauren Macdonald, Julia Schneider, Jacquelyn AI Soc Main Paper Despite pronouncements about the inevitable diffusion of artificial intelligence and autonomous technologies, in practice, it is human behavior, not technology in a vacuum, that dictates how technology seeps into—and changes—societies. To better understand how human preferences shape technological adoption and the spread of AI-enabled autonomous technologies, we look at representative adult samples of US public opinion in 2018 and 2020 on the use of four types of autonomous technologies: vehicles, surgery, weapons, and cyber defense. By focusing on these four diverse uses of AI-enabled autonomy that span transportation, medicine, and national security, we exploit the inherent variation between these AI-enabled autonomous use cases. We find that those with familiarity and expertise with AI and similar technologies were more likely to support all of the autonomous applications we tested (except weapons) than those with a limited understanding of the technology. Individuals that had already delegated the act of driving using ride-share apps were also more positive about autonomous vehicles. However, familiarity cut both ways; individuals are also less likely to support AI-enabled technologies when applied directly to their life, especially if technology automates tasks they are already familiar with operating. Finally, we find that familiarity plays little role in support for AI-enabled military applications, for which opposition has slightly increased over time. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00146-023-01666-5. Springer London 2023-05-12 /pmc/articles/PMC10175926/ /pubmed/37358948 http://dx.doi.org/10.1007/s00146-023-01666-5 Text en © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Main Paper Horowitz, Michael C. Kahn, Lauren Macdonald, Julia Schneider, Jacquelyn Adopting AI: how familiarity breeds both trust and contempt |
title | Adopting AI: how familiarity breeds both trust and contempt |
title_full | Adopting AI: how familiarity breeds both trust and contempt |
title_fullStr | Adopting AI: how familiarity breeds both trust and contempt |
title_full_unstemmed | Adopting AI: how familiarity breeds both trust and contempt |
title_short | Adopting AI: how familiarity breeds both trust and contempt |
title_sort | adopting ai: how familiarity breeds both trust and contempt |
topic | Main Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10175926/ https://www.ncbi.nlm.nih.gov/pubmed/37358948 http://dx.doi.org/10.1007/s00146-023-01666-5 |
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