Published Paper Details:

The incorporation of methods for machine learning (ML) into procedures for human resource management (HRM) has gained a large amount of interest owing to the fact that it has the potential to completely transform the manner in which businesses manage their human capital. The purpose of this study is to investigate the many facets of the role that machine learning plays in the optimization of HRM processes, specifically digging into the problems and possibilities that machine learning brings. Applications of machine learning in human resource management span a variety of phases, including talent management, employee engagement, employee performance assessment, and recruiting. By using machine learning algorithms, businesses are able to improve the speed and accuracy of candidate selection, therefore lowering the risk of bias and increasing the number of diverse applicants. In addition, predictive analytics may be used to aid in the identification of top-performing personnel, which paves the way for improved succession planning and efforts that focus on focused skill development. The implementation of machine learning into HRM, on the other hand, does not come without its share of difficulties. The ramifications of automated decision-making on ethics, data privacy, and algorithmic prejudice are all significant challenges that need careful examination. It is essential to ensure that machine learning models are both fair and transparent in order to avoid biased results and keep people's confidence