The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index.
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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Cites
Artificial intelligence: A modern approach
Nilsson, N. J. (1996) · Artificial Intelligence · doi:10.1016/0004-3702(96)00007-0 · Cited by 10,730
Understanding information technology usage: a test of competing models
Taylor, S., & Todd, P. A. (1995) · Information Systems Research · doi:10.1287/isre.6.2.144 · Cited by 8,815
TRUST AND DISTRUST IN ORGANIZATIONS: Emerging Perspectives, Enduring Questions
Kramer, R. M. (1999) · Annual Review of Psychology · doi:10.1146/annurev.psych.50.1.569 · Cited by 3,415
A meta-analysis of the technology acceptance model
King, W. R., & He, J. (2006) · Information & Management · doi:10.1016/j.im.2006.05.003 · Cited by 2,906
Technology acceptance model: a literature review from 1986 to 2013
Marangunić, N., & Granić, A. (2015) · Universal Access in the Information Society · doi:10.1007/s10209-014-0348-1 · Cited by 2,458
Explaining the Role of User Participation in Information System Use
Hartwick, J., & Barki, H. (1994) · Management Science · doi:10.1287/mnsc.40.4.440 · Cited by 1,540
An Updated and Streamlined Technology Readiness Index: TRI 2.0
Parasuraman, A., & Colby, C. L. (2015) · Journal of Service Research · doi:10.1177/1094670514539730 · Cited by 1,440
Direct Experience And Attitude-Behavior Consistency
Fázio, R. H., & Zanna, M. P. (1981) · Advances in experimental social psychology · doi:10.1016/s0065-2601(08)60372-x · Cited by 1,248
An Empirical Assessment of a Modified Technology Acceptance Model
Chau, P. Y. K. (1996) · Journal of Management Information Systems · doi:10.1080/07421222.1996.11518128 · Cited by 1,131
Gauchat, G. (2012) · American Sociological Review · doi:10.1177/0003122412438225 · Cited by 1,091
Research works printed but not linked: 4
- Austin D, Pier C, Mitchell J, Schattner P, Wade V, Pierce D, Klein B (2006) A meta-analysis of the technology acceptance model. Aust Family Phys 35(5): 365–366. https://www.racgp.org.au/afpbackissues/2006/200605/200605austin.pdf
- Horowitz MC (2018) Artificial intelligence, international competition, and the balance of power. Oxf Rev Econ Policy 1:36–57
- Jasanoff S (ed) (2004). Routledge Taylor & Francis Group, London
- Leyer M, Schneider S (2019) Me, you or ai? how do we feel about delegation. In: In Proceedings of the 27th European Conference on Information Systems (ECIS), Stockholm & Uppsala, Sweden, June 8-14. https://aisel.aisnet.org/ecis2019_rp/36
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Christensen, J., Hansen, J. M., & Wilson, P. (2024) · Current Issues in Tourism · doi:10.1080/13683500.2023.2300032 · Cited by 253
Quantifying large language model usage in scientific papers
Liang, W., Zhang, Y., Wu, Z., Lepp, H., Ji, W., Zhao, X., Cao, H., Liu, S., He, S., Huang, Z., et al. (2025) · Nature Human Behaviour · doi:10.1038/s41562-025-02273-8 · Cited by 80
Wang, W., Zhiguang, C., & Kuang, J. (2025) · Foods · doi:10.3390/foods14060976 · Cited by 95
Horowitz, M. C., & Kahn, L. (2024) · International Studies Quarterly · doi:10.1093/isq/sqae020 · Cited by 68
Self-citationTopsakal, Y. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2426044 · Cited by 69
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
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