Judgments of research co-created by generative AI: experimental evidence
This addresses the problem of public perception and ethical concerns in AI-assisted research for scientists and policymakers, but it is incremental as it builds on existing debates about AI in science.
The study investigated whether using generative AI (LLMs) in research leads to distrust and devaluation of researchers and scientific output, finding that participants judged LLM delegation as less acceptable, less trustworthy, and producing lower-quality results compared to human delegation, with effect sizes around d = -0.8.
The introduction of ChatGPT has fuelled a public debate on the use of generative AI (large language models; LLMs), including its use by researchers. In the current work, we test whether delegating parts of the research process to LLMs leads people to distrust and devalue researchers and scientific output. Participants (N=402) considered a researcher who delegates elements of the research process to a PhD student or LLM, and rated (1) moral acceptability, (2) trust in the scientist to oversee future projects, and (3) the accuracy and quality of the output. People judged delegating to an LLM as less 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.