People versus machines: introducing the HIRE framework

Paris Will, Dario Krpan and Grace Lordan

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: The use of Artificial Intelligence (AI) in the recruitment process is becoming a more common method for organisations to hire new employees. Despite this, there is little consensus on whether AI should have widespread use in the hiring process, and in which contexts. In order to bring more clarity to research findings, we propose the HIRE (Human, (Artificial) Intelligence, Recruitment, Evaluation) framework with the primary aim of evaluating studies which investigate how Artificial Intelligence can be integrated into the recruitment process with respect to gauging whether AI is an adequate, better, or worse substitute for human recruiters. We illustrate the simplicity of this framework by conducting a systematic literature review on the empirical studies assessing AI in the recruitment process, with 22 final papers included. The review shows that AI is equal to or better than human recruiters when it comes to efficiency and performance. We also find that AI is mostly better than humans in improving diversity. Finally, we demonstrate that there is a perception among candidates and recruiters that AI is worse than humans. Overall, we conclude based on the evidence, that AI is equal to or better to humans when utilised in the hiring process, however, humans hold a belief of their own superiority. Our aim is that future authors adopt the HIRE framework when conducting research in this area to allow for easier comparability, and ideally place the HIRE framework outcome of AI being better, equal, worse, or unclear in the abstract.

Keywords: artificial intelligence; recruitment; hiring; diversity; Diversity; The Inclusion Initiative (search for similar items in EconPapers)
JEL-codes: J50 (search for similar items in EconPapers)
Pages: 30 pages
Date: 2023-02-01
New Economics Papers: this item is included in nep-big
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Citations: View citations in EconPapers (3)

Published in Artificial Intelligence Review, 1, February, 2023, 56(2), pp. 1071 - 1100. ISSN: 0269-2821

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:115006

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