@TechReport{iza:izadps:dp17659,
            	author={Pataranutaporn, Pat and Powdthavee, Nattavudh and Maes, Pattie},
            	title={Can AI Solve the Peer Review Crisis? A Large-Scale Experiment on LLM's Performance and Biases in Evaluating Economics Papers},
            	year={2025},
	month={Jan},
            	institution={Institute of Labor Economics (IZA)},
            	address={Bonn},
            	type={IZA Discussion Paper},
            	number={17659},
            	url={https://www.iza.org/publications/dp17659},
	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.},
	keywords={Artificial Intelligence;peer review;large language model (LLM);bias in academia;economics publishing;equity-efficiency trade-off},
            }