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Can AI Solve the Peer Review Crisis? A Large Scale Experiment on LLM's Performance and Biases in Evaluating Economics Papers

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  • Pat Pataranutaporn
  • Nattavudh Powdthavee
  • Pattie Maes

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.

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  • Pat Pataranutaporn & Nattavudh Powdthavee & Pattie Maes, 2025. "Can AI Solve the Peer Review Crisis? A Large Scale Experiment on LLM's Performance and Biases in Evaluating Economics Papers," Papers 2502.00070, arXiv.org.
    • Handle: RePEc:arx:papers:2502.00070
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