Adopting new technologies changes how people once worked and performed, guaranteeing they get the best out of it while maintaining their leadership in their respective industries. Recently, the HR industry has started using machine learning (ML) as a way to become more innovative in their work. ML has been incorporated into many organizations to support data-based decisions in various areas, including the recruitment process. Traditional methods include the long process of filtering and analyzing resumes in order to identify suitable candidates. Furthermore, personal biases play a role in these selection processes, which may hinder their compatibility with the position. Another factor to consider is the inconsistency of evaluation criteria during the hiring process. Using ML-based techniques, the evaluation of all candidates is done in a shorter time, with more structured and evidence-based approaches utilized.

The objective of this chapter is to give a deep understanding of applying ML technology in the candidate selection process and how it enhances the stages of the hiring process. It also highlights the benefits and challenges of using ML. A suggested model of how ML can be applied in practice to reduce bias in candidate selection will also be discussed. It also offers practical recommendations for HR professionals when applying ML techniques, training, and monitoring.

ElSherif, S. (2026). AI in hiring: Leveraging machine learning for fair, efficient recruitment (Chap. 10). In J. Kaur (Ed.), Human 2.0: Reimagining HR in the age of transhumanism. Emerald Publishing Limited. https://doi.org/10.1108/978-1-83708-166-0