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People versus machines: introducing the HIRE framework

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  • Will, Paris
  • Krpan, Dario
  • Lordan, Grace

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.

Suggested Citation

  • Will, Paris & Krpan, Dario & Lordan, Grace, 2023. "People versus machines: introducing the HIRE framework," LSE Research Online Documents on Economics 115006, London School of Economics and Political Science, LSE Library.
  • Handle: RePEc:ehl:lserod:115006
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    File URL: http://eprints.lse.ac.uk/115006/
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    Cited by:

    1. Marie-Pierre Dargnies & Rustamdjan Hakimov & Dorothea Kübler, 2022. "Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence," CESifo Working Paper Series 9968, CESifo.

    More about this item

    Keywords

    artificial intelligence; recruitment; hiring; diversity; Diversity; The Inclusion Initiative; Springer deal;
    All these keywords.

    JEL classification:

    • J50 - Labor and Demographic Economics - - Labor-Management Relations, Trade Unions, and Collective Bargaining - - - General

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