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Adopting AI: how familiarity breeds both trust and contempt
Open accessAbstract
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.
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“Ubiquitous performance degradation over multi-turn interactions is likely a reason for low uptake of AI systems [73, 4, 28], particularly with novice users who are less skilled at providing complete, detailed instructions from the onset of conversation [87, 35].”
From LLMs Get Lost In Multi-Turn Conversation (2025) · cites this paper as [28]
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Metadata
- Authors
- Horowitz, M. C., Kahn, L., Macdonald, J., & Schneider, J.
- Affiliations
- University of Pennsylvania (US)
- Council on Foreign Relations (US)
- University of Denver (US)
- Stanford University (US)
- Corresponding author
- Horowitz, Michael C.
- Venue
- AI & SOCIETY
- Published
- 2023-05-12
- Volume
- 39
- Issue
- 4
- Pages
- 1721-1735
- ISSN
- 0951-5666 (ISSN-L)
- 1435-5655
- PMC
- PMC10175926
- OpenAlex
- W4376255515
- Cited by
- 135
- References
- 55
- Citations by year
- 2026: 51 · 2025: 58 · 2024: 23 · 2023: 2
- FWCI
- 27.64
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- AI & Society (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- Air Force Office of Scientific Research (FA9550-18-1-0194)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Human Factors and Ergonomics › Human-Automation Interaction and Safety
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Keywords
- artificial intelligence adoption, public opinion, trust in AI, technology familiarity, expertise, autonomous vehicles, autonomous surgery, autonomous weapons, cyber defense, artificial intelligence in warfare, national security, technology acceptance
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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