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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

Author

Listed:
  • Pataranutaporn, Pat

    (Massachusetts Institute of Technology)

  • Powdthavee, Nattavudh

    (Nanyang Technological University, Singapore)

  • Maes, Pattie

    (Massachusetts Institute of Technology)

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.

Suggested Citation

  • Pataranutaporn, Pat & Powdthavee, Nattavudh & Maes, Pattie, 2025. "Can AI Solve the Peer Review Crisis? A Large-Scale Experiment on LLM's Performance and Biases in Evaluating Economics Papers," IZA Discussion Papers 17659, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp17659
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    References listed on IDEAS

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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Artificial Intelligence; peer review; large language model (LLM); bias in academia; economics publishing; equity-efficiency trade-off;
    All these keywords.

    JEL classification:

    • A11 - General Economics and Teaching - - General Economics - - - Role of Economics; Role of Economists
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • I23 - Health, Education, and Welfare - - Education - - - Higher Education; Research Institutions

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