Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/314556 
Year of Publication: 
2025
Series/Report no.: 
IZA Discussion Papers No. 17659
Publisher: 
Institute of Labor Economics (IZA), Bonn
Abstract: 
We investigate whether artificial intelligence can address the peer review crisis in economics by analyzing 27,090 evaluations of 9,030 unique submissions using a large language model (LLM). The experiment systematically varies author characteristics (e.g., affiliation, reputation, gender) and publication quality (e.g., top-tier, mid-tier, low-tier, AI-generated papers). The results indicate that LLMs effectively distinguish paper quality but exhibit biases favoring prominent institutions, male authors, and renowned economists. Additionally, LLMs struggle to differentiate high-quality AI-generated papers from genuine top-tier submissions. While LLMs offer efficiency gains, their susceptibility to bias necessitates cautious integration and hybrid peer review models to balance equity and accuracy.
Subjects: 
Artificial Intelligence
peer review
large language model (LLM)
bias in academia
economics publishing
equity-efficiency trade-off
JEL: 
A11
C63
O33
I23
Document Type: 
Working Paper

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