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Do people trust humans more than ChatGPT?

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  • Buchanan, Joy
  • Hickman, William

Abstract

We explore whether people trust the accuracy of statements produced by large language models (LLMs) versus those written by humans. While LLMs have showcased impressive capabilities in generating text, concerns have been raised regarding the potential for misinformation, bias, or false responses. In this experiment, participants rate the accuracy of statements under different information conditions. Participants who are not explicitly informed of authorship tend to trust statements they believe are human-written more than those attributed to ChatGPT. However, when informed about authorship, participants show equal skepticism towards both human and AI writers. Informed participants are, overall, more likely to choose costly fact-checking. These outcomes suggest that trust in AI-generated content is context-dependent.

Suggested Citation

  • Buchanan, Joy & Hickman, William, 2024. "Do people trust humans more than ChatGPT?," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 112(C).
    • Handle: RePEc:eee:soceco:v:112:y:2024:i:c:s2214804324000776
    DOI: 10.1016/j.socec.2024.102239
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    References listed on IDEAS

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    1. Margarita Leib & Nils Kobis & Rainer Michael Rilke & Marloes Hagens & Bernd Irlenbusch, 2023. "Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty," Papers 2301.01954, arXiv.org.
    2. Jingnan Chen & Daniel Houser, 2017. "Promises and lies: can observers detect deception in written messages," Experimental Economics, Springer;Economic Science Association, vol. 20(2), pages 396-419, June.
    3. Leib, Margarita & Köbis, Nils & Rilke, Rainer Michael & Hagens, Marloes & Irlenbusch, Bernd, 2023. "Corrupted by Algorithms? How AI-Generated and Human-Written Advice Shape (Dis)Honesty," IZA Discussion Papers 16293, Institute of Labor Economics (IZA).
    4. Erik Brynjolfsson & Danielle Li & Lindsey Raymond, 2023. "Generative AI at Work," Papers 2304.11771, arXiv.org, revised Nov 2024.
    5. Margarita Leib & Nils Köbis & Rainer Michael Rilke & Marloes Hagens & Bernd Irlenbusch, 2023. "Corrupted by Algorithms? How AI-generated and Human-written Advice Shape (Dis)honesty," ECONtribute Discussion Papers Series 251, University of Bonn and University of Cologne, Germany.
    6. Marta Serra-Garcia & Uri Gneezy, 2021. "Mistakes, Overconfidence, and the Effect of Sharing on Detecting Lies," American Economic Review, American Economic Association, vol. 111(10), pages 3160-3183, October.
    7. Anton Korinek, 2023. "Generative AI for Economic Research: Use Cases and Implications for Economists," Journal of Economic Literature, American Economic Association, vol. 61(4), pages 1281-1317, December.
    8. Buchanan, J., 2022. "Willingness to be paid: Who trains for tech jobs?," Labour Economics, Elsevier, vol. 79(C).
    9. March, Christoph, 2021. "Strategic interactions between humans and artificial intelligence: Lessons from experiments with computer players," Journal of Economic Psychology, Elsevier, vol. 87(C).
    10. Gordon Pennycook & Ziv Epstein & Mohsen Mosleh & Antonio A. Arechar & Dean Eckles & David G. Rand, 2021. "Shifting attention to accuracy can reduce misinformation online," Nature, Nature, vol. 592(7855), pages 590-595, April.
    11. Fumagalli, Elena & Rezaei, Sarah & Salomons, Anna, 2022. "OK computer: Worker perceptions of algorithmic recruitment," Research Policy, Elsevier, vol. 51(2).
    12. Chugunova, Marina & Sele, Daniela, 2022. "We and It: An interdisciplinary review of the experimental evidence on how humans interact with machines," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 99(C).
    13. Vernon L. Smith, 1994. "Economics in the Laboratory," Journal of Economic Perspectives, American Economic Association, vol. 8(1), pages 113-131, Winter.
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    More about this item

    Keywords

    Artificial intelligence; Machine learning; Trust; Belief; Experiments;
    All these keywords.

    JEL classification:

    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty

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