Algorithmic Decision Making in Recruitment and HR Analytics

Abstract

The rapid development of artificial intelligence and data analytics has transformed traditional human resource management practices, particularly recruitment and employee selection processes. Algorithmic decision-making enables organizations to analyze large volumes of applicant data, predict candidate suitability, and improve recruitment efficiency through machine learning and predictive analytics. HR analytics systems provide evidence-based insights that support strategic workforce planning and reduce human decision-making limitations. However, algorithm-based recruitment also creates challenges related to transparency, fairness, privacy, and potential algorithmic bias. This article examines the role of algorithmic decision-making in recruitment and HR analytics, focusing on its contribution to talent acquisition, predictive decision-making, and organizational performance. The study highlights the importance of combining technological capabilities with human judgment to ensure ethical and effective HR decisions. Organizations adopting responsible AI practices can enhance recruitment quality while maintaining fairness and employee trust.