Sage Journals: Discover world-class research

Abstract

Generative AI tools help illustrate complex scientific concepts, though concerns about accuracy persist. This study examined how AI-generated Instagram posts about CRISPR, with and without disclaimers indicating AI involvement, affect perceived credibility. In an online survey experiment with 742 U.S. adults, participants were randomly assigned to view content that varied by message frame (natural, neutral, or technology-focused) and the presence or absence of an AI disclaimer. Overall, AI disclaimers did not significantly affect credibility across frames. However, participants more familiar with AI perceived AI-disclaimed posts as more credible, while those less familiar perceived them as less credible.

Keywords

AI disclaimer message credibility digital communication generative AI AI familiarity

Get full access to this article

View all access options for this article.

References

Altay

Gilardi

(2024). People are skeptical of headlines labeled as AI-generated, even if true or human-made because they assume full AI automation. PNAS Nexus, 3(10), pgae403. https://doi.org/10.1093/pnasnexus/pgae403

Alvarez

Caliskan

Crockett

M. J.

S. S.

Messeri

West

(2024). Science communication with generative AI. Nature Human Behaviour, 8(4), 625–627.

Amazon. (2023). Amazon Ads introduces AI-powered image generation. Amazon Ads. https://advertising.amazon.com/blog/ai-image-generation

Aziz

Rahman

Safi

S. A.

Abbasi

A. Z.

(2024). Visual verity in AI-generated imagery: Computational metrics and human-centric analysis. arXiv preprint. arXiv:2408.12762.

Baek

T. H.

Kim

J. H.

(2024). Effect of disclosing AI-generated content on prosocial advertising evaluation. International Journal of Advertising, 1–22. https://doi.org/10.1080/02650487.2024.2401319

Bandura

(1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–215. https://doi.org/10.1037/0033-295X.84.2.191

Baxter

(2003). The art of the helix. Nature Reviews Genetics, 4(4), 244–244.

Bickert

(2024, April 5). Our approach to labeling AI-generated content and manipulated media. Meta. https://about.fb.com/news/2024/04/metas-approach-to-labeling-ai-generated-content-and-manipulated-media/

Biyela

Dihal

Gero

K. I.

Ippolito

Menczer

Schäfer

M. S.

Yokoyama

H. M.

(2024). Generative AI and science communication in the physical sciences. Nature Reviews Physics, 6(3), 162–165.

10.

Blau

Cerf

V. G.

Enriquez

Francisco

J. S.

Gasser

Gray

M. L.

Witherell

(2024). Protecting scientific integrity in an age of generative AI. Proceedings of the National Academy of Sciences, 121(22), e2407886121.

11.

Borah

Xiao

(2018). The importance of ‘likes’: The interplay of message framing, source, and social endorsement on credibility perceptions of health information on Facebook. Journal of Health Communication, 23(4), 399–411. https://doi.org/10.1080/10810730.2018.1455770

12.

Bray

S. D.

Johnson

S. D.

Kleinberg

(2023). Testing the human ability to detect “deepfake” images of human faces. Journal of Cybersecurity, 9(1), tyad011.

13.

Briscoe

M. H.

(1996). Preparing scientific illustrations. Springer.

14.

Carbonell

Sánchez-Esguevillas

Carro

(2016). The role of metaphors in the development of technologies. In the case of artificial intelligence. Futures, 84, 145–153.

15.

Chen

Shu

(2024). Combating misinformation in the age of LLMs: Opportunities and challenges. AI Magazine, 45, 354–368. https://doi.org/10.1002/aaai.12188

16.

Choi

J. K.

Y. G.

(2015). Investigating the importance of trust on adopting an autonomous vehicle. International Journal of Human-computer Interaction, 31(10), 692–702.

17.

Compeau

D. R.

Higgins

C. A.

(1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189–211. https://doi.org/10.2307/249688

18.

Cox

I. J.

Kilian

Leighton

Shamoon

(1996). A secure, robust watermark for multimedia. In Information Hiding: First International Workshop (pp. 185–206). Springer.

19.

Davies

(2020). Editing humanity: The CRISPR revolution and the new era of genome editing. Simon and Schuster.

20.

Davis

F. D.

(1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319–340.

21.

Deck

(2024). Newsweek is making generative AI a fixture in its newsroom. Nieman Lab. https://www.niemanlab.org/2024/04/inside-newsweek-ai-experiment/

22.

Entman

R. M.

(1993). Framing: Towards clarification of a fractured paradigm. Journal of Communication, 43, 51–58.

23.

Feng

(2025). From ChatGPT to Sora: Analyzing public opinions and attitudes on generative artificial intelligence in social Media. IEEE Access.

24.

Finkel

Krämer

N. C.

(2022). Humanoid robots–Artificial. Human-like. Credible? Empirical comparisons of source credibility attributions between humans, humanoid robots, and non-human-like devices. International Journal of Social Robotics, 14(6), 1397–1411. https://doi.org/10.1007/s12369-022-00879-w

25.

Frolov

Hinz

Raue

Hees

Dengel

(2021). Adversarial text-to-image synthesis: A review. Neural Networks, 144, 187–209.

26.

Gamliel

Kreiner

(2013). Is a picture worth a thousand words? The interaction of visual display and attribute representation in attenuating framing bias. Judgment and Decision Making, 8(4), 482–491.

27.

Gillath

Branicky

M. S.

Keshmiri

Davison

R. B.

Spaulding

(2021). Attachment and trust in artificial intelligence. Computers in Human Behavior, 115, Article 106607. https://doi.org/10.1016/j.chb.2020.106607

28.

Greszki

Meyer

Schoen

(2015). Exploring the effects of removing “too fast” responses and respondents from web surveys. Public Opinion Quarterly, 79(2), 471–503.

29.

Hardy

B. W.

Tallapragada

Besley

J. C.

Yuan

(2019). The effects of the “war on science” frame on scientists’ credibility. Science Communication, 41(1), 90–112.

30.

Hausken

(2024). Photorealism versus photography. AI-generated depiction in the age of visual disinformation. Journal of Aesthetics & Culture, 16(1), 2340787.

31.

Isenberg

Dachselt

Isenberg

(2022). BeauVis: A validated scale for measuring the aesthetic pleasure of visual representations. IEEE Transactions on Visualization and Computer Graphics. https://doi.org/10.1109/TVCG.2022.3209390

32.

Heath

(2024, February 8). Inside the battle to label digital content as AI-generated media spreads. Axios. https://www.axios.com/2024/02/08/google-adobe-label-artificial-intelligence-deepfakes

33.

Heikkilä

(2022, December 20). How AI-generated text is poisoning the internet. MIT Technology Review. https://www.technologyreview.com/2022/12/20/1065667/how-ai-generated-text-is-poisoning-the-internet/

34.

Hornberger

Bewersdorff

Nerdel

(2023). What do university students know about Artificial Intelligence? Development and validation of an AI literacy test. Computers and Education: Artificial Intelligence, 5, 100165.

35.

Horowitz

M. C.

Kahn

Macdonald

Schneider

(2023). Adopting AI: How familiarity breeds both trust and contempt [Early Access]. AI & Society, 15. https://doi.org/10.1007/s00146-023-01666-5

36.

Hsu

Myers

S. L.

(2023, April 8). Can we no longer believe anything we see? The New York Times. https://www.nytimes.com/2023/04/08/business/media/ai-generated-images.html

37.

Introducing the A.I. Initiatives Team. (2024, May 9). The New York Times. https://www.nytco.com/press/introducing-the-a-i-initiatives-team/

38.

Jeng

Chang

Duffy

Lam

Maercklein

(2024). Chains of trust: Combatting synthetic data risks of AI. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4854347

39.

Johnson

K. A.

St John

III . (2021). Transparency in the news: The impact of self-disclosure and process disclosure on the perceived credibility of the journalist, the story, and the organization. Journalism Studies, 22(7), 953–970.

40.

Kang

Huang

Chen

Gao

Fan

(2016). Introducing precise genetic modifications into human 3PN embryos by CRISPR/Cas-mediated genome editing. Journal of Assisted Reproduction and Genetics, 33, 581–588.

41.

Kelly

Kaye

S.-A.

Oviedo-Trespalacios

(2023). What factors contribute to the acceptance of artificial intelligence? A systematic review. Telematics and Informatics, 77, 101925. https://doi.org/https://doi.org/10.1016/j.tele.2022.101925

42.

Kennedy

E. M.

Cullen

B. R.

(2015). Bacterial CRISPR/Cas DNA endonucleases: A revolutionary technology that could dramatically impact viral research and treatment. Virology, 479, 213–220.

43.

Klepper

(2024, March 21). New bipartisan bill would require online identification, labeling of AI-generated videos and audio. AP News. https://apnews.com/article/artificial-intelligence-deepfakes-congress-biden-7026e6223c64a042bd434b11c211b753

44.

Lee

Cho

(2022). The (in) congruency effects of message framing and image valence on consumers’ responses to green advertising: Focus on issue involvement as a moderator. Journal of Marketing Communications, 28(6), 617–636.

45.

Brossard

Scheufele

D. A.

Wilson

P. H.

Rose

K. M.

(2018). Communicating data: Interactive infographics, scientific data and credibility. Journal of Science Communication, 17(02), A06. https://doi.org/10.22323/2.17020206

46.

Zheng

(2024). Social media use and attitudes toward AI: The mediating roles of perceived AI fairness and threat. Human Behavior and Emerging Technologies, 2024(1), 3448083.

47.

Lima-Strong

(2024, October 8). AI disclaimers in political ads backfire on candidates, study finds. The Washington Post. https://www.washingtonpost.com/politics/2024/10/08/ai-disclaimers-political-ads-backfire-candidates-study-finds/

48.

Lowry

P. B.

Wilson

D. W.

Haig

W. L.

(2014). A picture is worth a thousand words: Source credibility theory applied to logo and website design for heightened credibility and consumer trust. International Journal of Human-computer Interaction, 30(1), 63–93.

49.

Martins

Weaver

A. J.

Lynch

(2018). What the public “knows” about media effects research: The influence of news frames on perceived credibility and belief change. Journal of Communication, 68(1), 98–119.

50.

McClain

(2024, March 26). Americans’ use of ChatGPT is ticking up, but few trust its election information. Pew Research Center. https://www.pewresearch.org/short-reads/2024/03/26/americans-use-of-chatgpt-is-ticking-up-but-few-trust-its-election-information/

51.

McCroskey

J. C.

Teven

J. J.

(1999). Goodwill: A reexamination of the construct and its measurement. Communication Monographs, 66, 90–103.

52.

McLeod

Nerlich

(2017). Synthetic biology, metaphors and responsibility. Life Sciences, Society and Policy, 13, 1–13.

53.

Middleton

Shao

Cate

Haugen

(2024). An Enthusiastic but Uncertain Welcome: Coverage of Risks, Benefits, and Social Contexts of CRISPR Technology in US Agricultural News 2012-2022. Journal of Applied Communications, 108(2), 1.

54.

Myers

(1990). The double helix as icon. Science as Culture, 1(9), 49–72.

55.

Nisbet

M. C.

Scheufele

D. A.

(2009). What’s next for science communication? Promising directions and lingering distractions. American Journal of Botany, 96(10), 1767–1778.

56.

Novak

(2023, March 26). That viral image of Pope Francis wearing a white puffer coat is totally fake. Forbes. https://www.forbes.com/sites/mattnovak/2023/03/26/that-viral-image-of-pope-francis-wearing-a-white-puffer-coat-is-totally-fake/

57.

Ormond

Warkentin

Johnston

A. C.

Thompson

S. C.

(2016). Perceived deception: Evaluating source credibility and self-efficacy. Journal of Information Privacy and Security, 12(4), 197–217. https://doi.org/10.1080/15536548.2016.1243857

58.

Peifer

J. T.

Meisinger

(2021). The value of explaining the process: How journalistic transparency and perceptions of news media importance can (sometimes) foster message credibility and engagement intentions. Journalism & Mass Communication Quarterly, 98(3), 828–853. https://doi.org/10.1177/10776990211012953

59.

Ray

(2024, August 27). Americans express real concerns about artificial intelligence. Gallup. https://news.gallup.com/poll/648953/americans-express-real-concerns-artificial-intelligence.aspx

60.

Rodriguez

Dimitrova

D. V.

(2011). The levels of visual framing. Journal of Visual Literacy, 30(1), 48–65. https://doi.org/10.1080/23796529.2011.11674684

61.

Roose

(2022, September 2). An A.I.-generated picture won an art prize. Artists aren’t happy. The New York Times. https://www.nytimes.com/2022/09/02/technology/ai-artificial-intelligence-artists.html

62.

Scheufele

D. A.

Krause

N. M.

Freiling

Brossard

(2021). What we know about effective public engagement on CRISPR and beyond. Proceedings of the National Academy of Sciences, 118. https://doi.org/10.1073/pnas.2004835117

63.

Ñor

(2023). How to stop deepfakes from sinking the internet. Nature, 621, 677.

64.

Selejan

Muresanu

D. F.

Popa

Muresanu-Oloeriu

Iudean

Buzoianu

Suciu

(2016). Credibility judgments in web page design: A brief review. Journal of Medicine and Life, 9(2), 115–119.

65.

Seo

Dillard

J. P.

(2019). A process analysis of message style and persuasion: The effects of gain-loss framing and emotion-inducing imagery. Visual Communication Quarterly, 26(3), 131–145.

66.

Seo

Dillard

J. P.

Shen

(2013). The effects of message framing and visual image on persuasion. Communication Quarterly, 61(5), 564–583.

67.

Septianto

Lee

M. S.

(2020). Emotional responses to plastic waste: Matching image and message framing in encouraging consumers to reduce plastic consumption. Australasian Marketing Journal, 28(1), 18–29.

68.

Subramonyam

Thakkar

Dieber

Sinha

A. K.

(2025, April). Prototyping with prompts: Emerging approaches and challenges in generative ai design for collaborative software teams. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (pp. 1–22).

69.

Tang

K. K.

Kwok

K. O.

Cao

Luong

Tam

(2024). The importance of transparency: Declaring the use of generative artificial intelligence (AI) in academic writing. Journal of Nursing Scholarship, 56(2), 314–318.

70.

Torkzadeh

Pflughoeft

Hall

(1999). Computer self-efficacy, training effectiveness and user attitudes: An empirical study. Behaviour & Information Technology, 18(4), 299–309.

71.

Van Buuren

Groothuis-Oudshoorn

. (2011). Mice: Multivariate imputation by chained equations in R. Journal of Statistical Software, 45, 1–67.

72.

Vorm

E. S.

Combs

D. J.

(2022). Integrating transparency, trust, and acceptance: The intelligent systems technology acceptance model (ISTAM). International Journal of Human–computer Interaction, 38(18–20), 1828–1845.

73.

Watiktinnakorn

Seesai

Kerdvibulvech

(2023). Blurring the lines: How AI is redefining artistic ownership and copyright. Discover Artificial Intelligence, 3(1), 37.

74.

Wittenberg

Epstein

Berinsky

A. J.

Rand

D. G.

(2024). Labeling AI-generated content: Promises, Perils, and future directions. An MIT Exploration of Generative AI. https://doi.org/10.21428/e4baedd9.0319e3a6

75.

Wong

(2024). AI-generated images and video are here: How could they shape research? Nature. https://doi.org/10.1038/d41586-024-00659-8

76.

Wortel

Vanwesenbeeck

Tomas

(2024). Made with artificial intelligence: The effect of artificial intelligence disclosures in Instagram advertisements on consumer attitudes. Emerging Media, 2(3), 547–570.

77.

Y. W.

Cai

R. R.

Gursoy

(2024). When disclosing the artificial intelligence (AI) technology integration into service delivery backfires: Roles of fear of AI, identity threat and existential threat. International Journal of Hospitality Management, 122, 103829.

78.

Yang

Hobbs

J. E.

(2020). The power of stories: Narratives and information framing effects in science communication. American Journal of Agricultural Economics, 102(4), 1271–1296.

79.

Yang

(2024, September 27). China’s plan to make AI watermarks happen. WIRED. https://www.wired.com/story/china-wants-to-make-ai-watermarks-happen/

80.

(2022). Artificial intelligence decision-making transparency and employees’ trust: The parallel multiple mediating effect of effectiveness and discomfort. Behavioral Sciences, 12(5), 127. https://doi.org/10.3390/bs12050127

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.02 MB

Familiarity Matters: Examining the Effects of AI-Generated Images on Science Information Credibility

Abstract

Keywords

Get full access to this article

References

Supplementary Material