Chapter 6: HR machine learning in recruiting
Laumer S, Maier C, Weitzel T (2022)
Publication Language: English
Publication Type: Book chapter / Article in edited volumes
Publication year: 2022
Publisher: Edward Elgar Publishing
Edited Volumes: Handbook of Research on Artificial Intelligence in Human Resource Management
City/Town: Cheltenham, UK
Pages Range: 105–126
ISBN: 9781839107528
URI: https://www.elgaronline.com/view/edcoll/9781839107528/9781839107528.00015.xml
Abstract
HR recruiting has been the focus of artificial intelligence (AI) based approaches for years. It is expected that applying AI - and especially machine learning - provides opportunities to improve HR processes, simplify, and automate finding, selecting, and evaluating candidates. This chapter reports a literature review to develop the HR recruiting machine learning model. It indicates whether expectations are met, reveals different person-environment fit dimensions focused, identifies challenges coming with these approaches, and discusses future research opportunities. It shows that most studies use decision trees or deep neural networks to predict person-job fit. Given the reported accuracy levels, the use of machine learning can increase the recruiting process's efficiency. Still, the focus is mainly on automatically predicting HR employees' or business managers' assessment of person-environment fit dimensions. The focus is not on fit in terms of an aptitude-diagnostic sense as a base for better decision making. Moreover, several approaches use discriminatory characteristics such as age, gender, or race in their machine learning models to predict an individual's appropriateness for a vacancy. Hence, various future research opportunities are discussed in terms of focusing on the different person-environment fit dimensions and not limiting the analysis to the person-job one. Besides, research should share datasets that enable the comparison of the different machine learning-based approaches, address fairness and transparency by focusing on approaches to eliminate discrimination in the models used, and focus on fit not only to increase efficiency but also to increase decision quality in an aptitude-diagnostic sense.
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APA:
Laumer, S., Maier, C., & Weitzel, T. (2022). Chapter 6: HR machine learning in recruiting. In Handbook of Research on Artificial Intelligence in Human Resource Management. (pp. 105–126). Cheltenham, UK: Edward Elgar Publishing.
MLA:
Laumer, Sven, Christian Maier, and Tim Weitzel. "Chapter 6: HR machine learning in recruiting." Handbook of Research on Artificial Intelligence in Human Resource Management. Cheltenham, UK: Edward Elgar Publishing, 2022. 105–126.
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