Generation Next: Experimentation with AI

Gary Charness, Brian Jabarian and John List

No 31679, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: We investigate the potential for Large Language Models (LLMs) to enhance scientific practice within experimentation by identifying key areas, directions, and implications. First, we discuss how these models can improve experimental design, including improving the elicitation wording, coding experiments, and producing documentation. Second, we delve into the use of LLMs in experiment implementation, with an emphasis on bolstering causal inference through creating consistent experiences, improving instruction comprehension, and real-time monitoring of participant engagement. Third, we underscore the role of LLMs in analyzing experimental data, encompassing tasks like pre-processing, data cleaning, and assisting reviewers and replicators in examining studies. Each of these tasks improves the probability of reporting accurate findings. Lastly, we suggest a scientific governance framework that mitigates the potential risks of using LLMs in experimental research while amplifying their advantages. This could pave the way for open science opportunities and foster a culture of policy and industry experimentation at scale.

JEL-codes: C0 C1 C80 C82 C87 C9 C90 C92 C99 (search for similar items in EconPapers)
Date: 2023-09
New Economics Papers: this item is included in nep-ain, nep-exp, nep-hrm and nep-ltv
Note: LS PE
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Citations: View citations in EconPapers (4)

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