Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/339347 
Year of Publication: 
2026
Series/Report no.: 
ECONtribute Discussion Paper No. 384
Publisher: 
University of Bonn and University of Cologne, Reinhard Selten Institute (RSI), Bonn and Cologne
Abstract: 
A large body of research across management, psychology, accounting, and economics shows that subjective performance evaluations are systematically biased: ratings cluster near the midpoint of scales and are often excessively lenient. As organizations increasingly adopt large language models (LLMs) for evaluative tasks, little is known about how these systems perform when assessing human performance. We document that, in the absence of clear objective standards and when individuals are rated independently, LLMs reproduce the familiar patterns of human raters. However, LLMs generate greater dispersion and accuracy when evaluating multiple individuals simultaneously. With noisy but objective performance signals, LLMs provide substantially more accurate evaluations than human raters, as they (i) are less subject to biases arising from concern for the evaluated employee and (ii) make fewer mistakes in information processing closely approximating rational Bayesian benchmarks.
Subjects: 
Performance Evaluation
Large Language Models
Signal Objectivity
Algorithmic Judgment
Gen-AI
JEL: 
J24
J28
M12
M53
Document Type: 
Working Paper

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