AI AND HUMAN RESOURCE MANAGEMENT: A MULTIDISCIPLINARY PERSPECTIVE ON EFFICIENCY AND ETHICS

Abstract

The rapid integration of Artificial Intelligence (AI) into Human Resource Management (HRM) has redefined the traditional boundaries of workforce administration, talent acquisition, and employee engagement. As organizations increasingly rely on algorithmic systems for decision-making, the convergence of technological efficiency and ethical responsibility has emerged as a pivotal concern within modern management discourse. This research explores the multifaceted role of AI in reshaping HR functions through a multidisciplinary lens, combining insights from management science, behavioral psychology, data ethics, and organizational sociology. The study examines how AI-driven tools such as predictive analytics for recruitment, natural language processing in performance evaluation, and automated sentiment analysis for employee well-being have enhanced operational precision and strategic decision-making in HRM. Methodologically, the research employs a mixed approach, synthesizing empirical case studies, policy reviews, and theoretical frameworks to evaluate both the efficiency gains and ethical complexities arising from AI adoption. Findings indicate that AI significantly reduces administrative redundancy, enhances predictive accuracy in workforce planning, and enables a more data-driven understanding of employee behavior. However, these advancements are counterbalanced by profound ethical and social challenges, including algorithmic bias, loss of transparency, privacy intrusions, and the erosion of human discretion in evaluative processes. The analysis reveals that while AI augments managerial capabilities, its unregulated application risks transforming human resources into mere data entities, thereby undermining the human-centric foundations of employment relations. The paper argues that a sustainable integration of AI in HRM must reconcile the competing imperatives of efficiency and ethics through an adaptive governance framework. This includes instituting algorithmic accountability, transparent data management policies, and cross-disciplinary collaboration between technologists, ethicists, and HR professionals. Furthermore, the study emphasizes the importance of cultivating digital literacy and ethical awareness among HR practitioners to ensure that AI complements rather than replaces human judgment. Ultimately, the research contributes to the growing discourse on responsible AI by demonstrating that technological progress in HRM must be guided not only by efficiency metrics but also by normative principles that preserve fairness, dignity, and inclusivity in the workplace. It calls for a paradigm shift toward a balanced, ethically informed, and human-centered approach to managing the future of work.