Judgements of research co-created by Generative AI: Experimental evidence

Niszczota Paweł () and Conway Paul ()
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Niszczota Paweł: Poznań University of Economics and Business, Humans & AI Laboratory (HAI Lab), Department of International Finance, al. Niepodległości 10, 61-875 Poznań, Poland
Conway Paul: University of Southampton, Department of Psychology, B44 University Road, SO17 1PS Southampton, United Kingdom

Economics and Business Review, 2023, vol. 9, issue 2, 101-114

Abstract: The introduction of ChatGPT has fuelled a public debate on the appropriateness of using Generative AI (large language models; LLMs) in work, including a debate on how they might be used (and abused) by researchers. In the current work, we test whether delegating parts of the research process to LLMs leads people to distrust researchers and devalues their scientific work. Participants (N = 402) considered a researcher who delegates elements of the research process to a PhD student or LLM and rated three aspects of such delegation. Firstly, they rated whether it is morally appropriate to do so. Secondly, they judged whether—after deciding to delegate the research process—they would trust the scientist (that decided to delegate) to oversee future projects. Thirdly, they rated the expected accuracy and quality of the output from the delegated research process. Our results show that people judged delegating to an LLM as less morally acceptable than delegating to a human (d = –0.78). Delegation to an LLM also decreased trust to oversee future research projects (d = –0.80), and people thought the results would be less accurate and of lower quality (d = −0.85). We discuss how this devaluation might transfer into the underreporting of Generative AI use.

Keywords: trust in science; metascience; ChatGPT; GPT; large language models; Generative AI; experiment (search for similar items in EconPapers)
JEL-codes: G00 J24 O33 O34 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:ecobur:v:9:y:2023:i:2:p:101-114:n:1

DOI: 10.18559/ebr.2023.2.744

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