Humans vs GPTs: Bias and validity in hiring decisions

Louis Lippens

No zxf5y, OSF Preprints from Center for Open Science

Abstract: The advent of large language models (LLMs) may reshape hiring in the labour market. This paper investigates how generative pre-trained transformers (GPTs)—i.e. OpenAI’s GPT-3.5, GPT-4, and GPT-4o—can aid hiring decisions. In a direct comparison between humans and GPTs on an identical hiring task, I show that GPTs tend to select candidates more liberally than humans but exhibit less ethnic bias. GPT-4 even slightly favours certain ethnic minorities. While LLMs may complement humans in hiring by making a (relatively extensive) pre-selection of job candidates, the findings suggest that they may miss-select due to a lack of contextual understanding and may reproduce pre-trained human bias at scale.

Date: 2024-07-11
New Economics Papers: this item is included in nep-ain, nep-big and nep-cmp
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Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:zxf5y

DOI: 10.31219/osf.io/zxf5y

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