Industry Adapting the Machine Learning Scenario in Recruitment and Selection of Employees

Abstract

This study examines the expanding practice of applying machine learning (ML) algorithms to hiring and selection procedures across a range of industries. Organizations’ increasingly depend on automated systems to sort through massive volumes of applicant data and find qualified candidates as big data and machine learning techniques advance. This study examines the state of machine learning applications in hiring, such as applicant ranking, resume parsing, and predictive analytics for worker performance. It also covers the advantages and difficulties of using machine learning (ML) in hiring, including issues with algorithm transparency, data privacy, and bias mitigation. This paper offers insights into how organizations’ can effectively integrate machine learning (ML) technologies into their recruitment strategies to improve efficiency, effectiveness, and fairness by looking at case studies and industry examples.

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