Technological Disruption in Human Resource Management: A Review of Machine Learning Algorithms for Strategic Decision-Making

The rapid advancement of technology has brought significant disruption to traditional Human Resource Management (HRM) practices, reshaping how organizations manage their workforce. This article provides a comprehensive literature review on the role of machine learning (ML) algorithms in strategic decision-making within HRM. By analyzing key functions such as recruitment, performance evaluation, and talent management, this review highlights the transformative potential of ML in enhancing HR processes. The various ML algorithms, including decision trees, neural networks, and support vector machines, identifying their strengths and limitations in addressing HR challenges. The paper discusses ethical concerns related to bias, data privacy, and the implications of automation on employee well-being. The findings suggest that while ML offers significant opportunities for innovation in HRM, careful consideration of ethical and strategic factors is crucial for its successful implementation. Future research directions and practical recommendations are also proposed to support the integration of ML in HR decision-making.