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Humans vs GPTs: Bias and validity in hiring decisions

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  • Lippens, Louis

    (Ghent University)

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.

Suggested Citation

  • Lippens, Louis, 2024. "Humans vs GPTs: Bias and validity in hiring decisions," OSF Preprints zxf5y, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:zxf5y
    DOI: 10.31219/osf.io/zxf5y
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    References listed on IDEAS

    as
    1. Patrick Kline & Evan K Rose & Christopher R Walters, 2022. "Systemic Discrimination Among Large U.S. Employers [“Teachers and Student Achievement in the Chicago Public High Schools,”]," The Quarterly Journal of Economics, Oxford University Press, vol. 137(4), pages 1963-2036.
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