Explainable AI in Human Resource Analytics: A Pathway to Sustainable Work–Life Balance and Stress Mitigation

Abstract

The rapid integration of Artificial Intelligence (AI) into Human Resource Analytics has transformed organizational decision-making by enabling data-driven insights into employee performance, engagement, productivity, and workforce well-being. However, conventional AI models often operate as opaque "black-box" systems, limiting managerial trust, employee acceptance, and ethical accountability. Explainable Artificial Intelligence (XAI) addresses these challenges by providing transparent, interpretable, and justifiable decision-making processes that enhance fairness, accountability, and organizational confidence. This study explores the role of Explainable AI in Human Resource Analytics as a strategic mechanism for promoting sustainable work–life balance and mitigating workplace stress. The paper conceptualizes an explainable HR analytics framework that integrates transparent machine learning techniques with employee-centric decision support for workload optimization, stress prediction, flexible work allocation, and well-being monitoring. Furthermore, the study discusses the implications of explainability for organizational sustainability, ethical AI governance, employee trust, and responsible human resource management. The proposed framework demonstrates how explainable analytical models can support equitable HR decisions while fostering healthier work environments, improving employee satisfaction, and strengthening long-term organizational resilience.