Datafied Selves at Work: Ethical Boundaries of Surveillance in People Analytics

In the age of algorithmic decision-making, People Analytics has emerged as a powerful tool for enhancing efficiency and strategic alignment in human resource management (HRM). Yet, its growing influence also introduces profound ethical concerns—particularly regarding surveillance, consent, fairness, and employee autonomy. This paper offers a normative framework that critically examines the ethical boundaries of People Analytics by integrating four foundational pillars: data sovereignty, algorithmic fairness, informed consent, and ethical accountability. Drawing from deontological ethics, virtue ethics, and stakeholder theory, the study constructs a conceptual model that positions HR not merely as a technological function, but as a moral actor responsible for upholding dignity, justice, and trust in datafied workplaces. The paper further explores how People Analytics reshapes employees into "datafied selves," enabling new forms of epistemic injustice and organizational control. A visual framework and practical tables support the translation of ethical theory into applied HR governance. Through this lens, the paper identifies four critical boundary zones—consent, purpose limitation, emotional labor, and accountability gaps—where ethical failure is most likely to occur. Finally, the study provides strategic recommendations, including ethics-by-design principles, inclusive governance, and data charters, while proposing a research agenda to empirically validate and refine the normative model. This contribution urges HR leaders, scholars, and policymakers to treat ethics not as an afterthought but as the guiding architecture of algorithmic people management.