A large survey of American adults explored the complex landscape of attitudes toward artificial intelligence (AI). It explored the degree of concern regarding specific potential outcomes of the new advances in AI technology and correlates of these concerns. Key variables associated with the direction and intensity of concern include prior experience using a large language model such as Chat GPT, general trust in science, adherence to the precautionary principle versus support for unrestricted innovation, and demographic factors such as gender. By identifying these relationships, the paper offers insights into the American public’s response to AI that can inform the development of policies aimed at either regulating or encouraging its advancement.
AjzenI (2020) The theory of planned behavior: Frequently asked questions. Human Behavior and Emerging Technologies2: 314–324. https://doi.org/10.1002/hbe2.195
2.
AjzenIFishbeinM (2002) Understanding Attitudes and Predicting Social Behavior, Transferred to Digital Print on Demand. Englewood Cliffs, NJ: Prentice-Hall.
3.
AndersonJRainieL (2023) As AI Spreads, Experts Predict the Best and Worst Changes in Digital Life by 2035. Washington, DC: Pew Research Center.
4.
ArmutatSWattenbergMMauritzN (2024) Artificial intelligence—Gender-specific differences in perception, understanding, and training interest. Icgr7: 36–43.
BaoLKrauseNMCaliceMNScheufeleDAWirzCDBrossardD, et al. (2022) Whose AI? How different publics think about AI and its social impacts. Computers in Human Behavior130: 107182.
7.
BayesRBolsenTDruckmanJN (2023) A research agenda for climate change communication and public opinion: The role of scientific consensus messaging and beyond. Environmental Communication17: 16–34.
8.
BeetsBNewmanTPHowellELBaoLYangS (2023) Surveying public perceptions of artificial intelligence in health care in the United States: Systematic review. J Med Internet Res25: e40337.
9.
BengioYHintonGYaoASongDAbbeelPDarrellT, et al. (2024) Managing extreme AI risks amid rapid progress. Science384: 842–845.
10.
BolsenTDruckmanJNCookFL (2014) How frames can undermine support for scientific adaptations: Politicization and the status-quo bias. Public Opinion Quarterly78(1): 1–26.
11.
BorweinSMagistroBLoewenPBonikowskiBLee-WhitingB (2024) The gender gap in attitudes toward workplace technological change. Socio-economic Review22: 993–1017.
12.
BrewerPRBingamanJPaintsilAWilsonDCDawsonW (2022) Media use, interpersonal communication, and attitudes toward artificial intelligence. Science Communication44: 559–592.
13.
CaliceMBaoLNewmanTScheufeleDABrossardDXenosMA (2022) U.S. public attitudes on artificial intelligence. Available at: https://doi.org/10.17605/OSF.IO/K82D6
DaeppMICountsS (2024) The emerging AI divide in the United States. arXiv preprint arXiv: 2404.11988.
16.
DavidPChoungHSebergerJS (2024) Who is responsible? US Public perceptions of AI governance through the lenses of trust and ethics. Public Understanding of Science33(5):654–672.
17.
DavisFD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly13: 319.
18.
DavisFD (1993) User acceptance of information technology: System characteristics, user perceptions and behavioral impacts. International Journal of Man-Machine Studies38: 475–487.
19.
DohleSWingenTSchreiberM (2020) Acceptance and adoption of protective measures during the COVID-19 pandemic: The role of trust in politics and trust in science. Soc Psychol Bull15: e4315.
20.
DruzinBBouteARamsdenM (2025) Confronting catastrophic risk: The international obligation to regulate artificial intelligence. Mich. J. Int’l L46: 173.
21.
DupontJAliVBaronDPriceAWrideS (2024) What Does the Public Think about AI? (UK). Washington, DC: Center for Data Innovation.
22.
EomDNewmanTBrossardDScheufeleDA (2024) Societal guardrails for AI? Perspectives on what we know about public opinion on artificial intelligence. Science and Public Policy51(5): 1004–1013.
23.
FastEHorvitzE (2017) Long-term trends in the public perception of artificial intelligence. Proceedings of the AAAI Conference on Artificial Intelligence31: 1.
FeherKVicsekLDeuzeM (2024) Modeling AI trust for 2050: Perspectives from media and info-communication experts. AI & SOCIETY: 1–14.
26.
HargittaiEDobranskyK (2017) Old dogs, new clicks: Digital inequality in skills and uses among older adults. Canadian Journal of Communication42: 195–212.
27.
HemphillTA (2020) The innovation governance dilemma: Alternatives to the precautionary principle. Technology in Society63: 101381.
28.
HorowitzMCKahnLMacdonaldJSchneiderJ (2024) Adopting AI: How familiarity breeds both trust and contempt. AI & Soc39: 1721–1735.
29.
HouTYTsengYCYuanCWT (2024) Is this AI sexist? The effects of a biased AI’s anthropomorphic appearance and explainability on users’ bias perceptions and trust. International Journal of Information Management76: 102775.
HuY (2024) Generative AI, communication, and stereotypes: Learning critical AI literacy through experience, analysis, and reflection. Communication Teacher39(1): 6–12.
32.
IsmatullaevUVUKimS-H (2024) Review of the factors affecting acceptance of AI-infused systems. Hum Factors66: 126–144.
33.
JacksonJCYamKCTangPMSibleyCGWaytzA (2023) Exposure to automation explains religious declines. Proc Natl Acad Sci USA120: e2304748120.
34.
KaivantoK (2025) The precautionary principle and the innovation principle: Incompatible guides for AI innovation governance?arXiv preprint arXiv: 2505.02846.
35.
KarataşMCutrightKM (2023) Thinking about God increases acceptance of artificial intelligence in decision-making. Proc Natl Acad Sci USA120: e2218961120.
36.
KoltNShur-OfryMCohenR (2025) Lessons from complexity theory for AI governance. arXiv preprint arXiv: 2502.00012.
37.
KöttlHGallistlVRohnerRAyalonL (2021) But at the age of 85? Forget it!”: Internalized ageism, a barrier to technology use. Journal of Aging Studies59: 100971.
38.
KozakJFelS (2024) The relationship between religiosity level and emotional responses to artificial intelligence in university students. Religions15: 331.
39.
KrepsSGeorgeJLushenkoPRaoA (2023) Exploring the artificial intelligence “trust paradox”: Evidence from a survey experiment in the United States. PLoS ONE18: e0288109.
40.
KurzweilR (2024) The Singularity is Nearer: When We Merge With AI. New York, NY: Viking.
41.
McClainCKennedyBGottfriedJAndersonMPasquiniG (2025) US public and AI experts’ views on artificial intelligence.
42.
MaiorAD (2025) The normative significance of the precautionary principle in artificial intelligence problem. Curentul Juridic100(1): 55–67.
43.
MernykJSPinkSLDruckmanJNWillerR (2022) Correcting inaccurate metaperceptions reduces Americans’ support for partisan violence. Proc Natl Acad Sci USA119: e2116851119.
44.
National Artificial Intelligence Act of2020, H.R. 6216.
45.
O’ConnorSLiuH (2024) Gender bias perpetuation and mitigation in AI technologies: Challenges and opportunities. AI & SOCIETY39(4): 2045–2057.
46.
O’ShaughnessyMRSchiffDSVarshneyLRRozellCJDavenportMA (2023) What governs attitudes toward artificial intelligence adoption and governance?Science and Public Policy50: 161–176.
47.
PalmRKingslandJTBolsenT (2025) Framing affects support for the development of artificial intelligence in the United States. Science Communication. Epub ahead of print 1 February. DOI: 10.1177/10755470251317172.
48.
PengY (2023) The role of ideological dimensions in shaping acceptance of facial recognition technology and reactions to algorithm bias. Public Understanding of Science32(2): 190–207.
49.
QiWPanJLyuHLuoJ (2024) Excitements and concerns in the post-chatgpt era: Deciphering public perception of ai through social media analysis. Telematics and Informatics92: 102158.
50.
ReadRO’RiordanT (2017) The precautionary principle under fire. Environment: Science and Policy for Sustainable Development59:4–15.
51.
Reading TurchioeMHarkinsSDesaiPKumarSKimJHermannA, et al. (2023) Women’s perspectives on the use of artificial intelligence (AI)-based technologies in mental healthcare. JAMIA Open6: ooad048.
52.
RobertsonCWoodsABergstrandKFindleyJBalserCSlepianMJ (2023) Diverse patients’ attitudes towards artificial intelligence (AI) in diagnosis. PLoS Digital Health2(5): e0000237.
53.
SaidNWendtSPotinteuAE (2025) Application or functionality?The type of information matters: The impact of information about artificial intelligence on AI, usage intention and AI aversion. Available at: https://doi.org/10.31219/osf.io/p3sc2
54.
SaundersPT (2000) Use and abuse of the precautionary principle. Submission to US Advisory Committee on International Economic Policy (ACIEP) Biotech. Working Group, 13July. London: King’s College.
55.
SchepmanARodwayP (2020) Initial validation of the general attitudes towards Artificial Intelligence Scale. Computers in Human Behavior Reports1: 100014.
56.
SchiffDS (2024) Framing contestation and public influence on policymakers: evidence from US artificial intelligence policy discourse. Policy and Society43(3): 255–288.
57.
SchiffKJSchiffDSAdamsITMcCrainJMourtgosSM (2023) Institutional factors driving citizen perceptions of AI in government: Evidence from a survey experiment on policing. Public Administration Review Puar85(2): 13754.
58.
SidotiOParkEGottfriedJ (2025) About a Quarter of US Teens Have Used Chatgpt for Schoolwork–double the Share in 2023. Washington, DC: Pew Research Center.
59.
SpaccatiniFRichetinJRivaPPancaniLAriccioSSacchiS (2022) Trust in science and solution aversion: Attitudes toward adaptation measures predict flood risk perception. International Journal of Disaster Risk Reduction76: 103024.
60.
StypinskaJ (2023) AI ageism: A critical roadmap for studying age discrimination and exclusion in digitalized societies. AI & Society38(2): 665–677.
61.
SuleymanM (2023) The Coming Wave: Technology, Power, and the Twenty-First Century’s Greatest Dilemma. New York, NY: Crown.
62.
TaylorSToddP (1995) Decomposition and crossover effects in the theory of planned behavior: A study of consumer adoption intentions. International Journal of Research in Marketing12(2): 137–155.
WestSMWhittakerMCrawfordK (2019) Discriminating systems: Gender, race and power in AI. AI Now Institute: 1–33.
65.
WilsonGGatesJRVijaykumarSMorganDJ (2023) Understanding older adults’ use of social technology and the factors influencing use. Ageing and Society43: 222–245.
66.
YangSKrauseNMBaoLCaliceMNNewmanTPScheufeleDA, et al. (2023) In AI we trust: The interplay of media use, political ideology, and trust in shaping emerging AI attitudes. Journalism & Mass Communication Quarterly102(2): 10776990231190868.
67.
ZhangBDafoeA (2020) Public opinion on the governance of artificial intelligence. In: Proceedings of the AAAI/ACM conference on AI, ethics, and society, New York, NY, 7–9 February, pp. 187–193. New York, NY: ACM.
68.
ZhangPTurG (2024) A systematic review of ChatGPT use in K-12 education. European Journal of Education59(2): e12599.
69.
ZhaoQChenYLiangJ (2024) Attitudes and usage patterns of educators towards large language models: Implications for professional development and classroom innovation. Academia Nexus Journal3(2).
70.
ZhouMAbhishekVDerdengerTKimJSrinivasanK (2024) Bias in generative AI. arXiv preprint arXiv: 2403.02726.
71.
ZualkernanI (2025) Adoption of generative AI and large language models in education: A short review. In: 2025 International Conference on Electronics, Information, and Communication (ICEIC), Osaka, Japan, 19–22 January, pp. 1–5. New York, NY: IEEE.
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