People versus machines: introducing the HIRE framework
Artificial Intelligence Review, ISSN: 1573-7462, Vol: 56, Issue: 2, Page: 1071-1100
2023
- 17Citations
- 115Captures
- 26Mentions
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
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Article Description
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.
Bibliographic Details
Springer Science and Business Media LLC
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