%0 Report
        %A Pataranutaporn, Pat
%A Powdthavee, Nattavudh
%A Maes, Pattie
        %T Can AI Solve the Peer Review Crisis? A Large-Scale Experiment on LLM's Performance and Biases in Evaluating Economics Papers
        %D 2025
        %8 2025 Jan
        %I Institute of Labor Economics (IZA)
        %C Bonn
        %7 IZA Discussion Paper
        %N 17659
        %U https://www.iza.org/publications/dp17659
        %X 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.
        %K Artificial Intelligence
%K peer review
%K large language model (LLM)
%K bias in academia
%K economics publishing
%K equity-efficiency trade-off