Can AI Solve the Peer Review Crisis? A Large-Scale Experiment on LLM's Performance and Biases in Evaluating Economics Papers - Pat Pataranutaporn; Nattavudh Powdthavee; Pattie Maes (Institute of Labor Economics (IZA), 2025)
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.
Keywords: A11; C63; O33; I23; 330; Artificial Intelligence; peer review; large language model (LLM); bias in academia; economics publishing; equity-efficiency trade-off