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Comparative Analysis of Generative Artificial Intelligence in Evaluating Candidate Attributes for Public Employment: East Versus West

Chih-Wei Hsieh,Daan Wang,Yujin He,Dennis William Hsieh

Published 2026 in Review of Public Personnel Administration

ABSTRACT

This study examines the applicability of generative artificial intelligence (GenAI) in public employee selection. Our interest in how cultural differences influence GenAI outputs leads us to compare OpenAI’s ChatGPT and Baidu’s ERNIE. We designed a conjoint experiment wherein these GenAI models evaluated pairs of hypothetical candidates with varying meritocratic attributes (including pre-employment assessment results, education level, and relevant experience) and non-meritocratic attributes (including gender, ethnicity, and age). Experimental vignettes were created based on two distinct settings—police and teacher—to control for job type. Our mixed-methods findings indicate that GenAI’s hiring recommendations are more swayed by meritocratic than non-meritocratic attributes. However, compared to ERNIE, ChatGPT’s candidate selections are slightly more influenced by considerations of gender diversity. Additionally, both models exhibit inconsistencies when non-meritocratic factors are involved. Based on these findings, we recommend a cautiously optimistic approach incorporating training and oversight if GenAI is to be used in employee selection processes.

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