Abstract
Human Resource Management (HRM) has transformed from a purely administrative function into a strategic capability affecting recruitment quality, employee performance, human capital development, workforce retention and organizational growth. At the same time, the practices of HRM remain largely manual, reactive and subjective. The current applications of artificial intelligence (AI) and machine learning (ML) are limited to specific areas, such as screening, selection, performance and attrition analysis. This paper proposes a conceptual seven-layer framework that employs human capital intelligence and responsible algorithmic decision support throughout an employee’s lifecycle in an organization. The framework includes Data Acquisition, Data Preprocessing, Feature Engineering, Machine Learning Prediction, Explainable AI, an HR Decision-Support Dashboard and Human Validation. It can be applied to recruitment intelligence, employee performance, attrition prediction, skills gap analysis, training recommendation and sentiment analysis. Unlike existing approaches to AI applications in HRM, the framework connects recruitment, performance, development, engagement, retention and planning areas through a unified system. The framework also addresses data privacy, algorithmic bias, fairness, transparency, explainability and human validation. While the sevenlayer model was not implemented and validated experimentally, it can serve as a foundation for subsequent studies that will employ real-world and ethically certified data sets. Finally, while the paper emphasizes the importance of ML in HR decision-making, its application remains limited to supporting and complementing human expertise.
Keywords: Employee Performance Prediction, Explainable Artificial Intelligence, Machine Learning, Recruitment Intelligence, Skill-gap Analysis, Workforce Analytics.