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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