In the past decade, HR Analytics has evolved from descriptive dashboards to advanced algorithmic decision-systems that now influence hiring, shift allocation, performance evaluation, and even dismissal—often with diminished human agency. This paper critically investigates how such algorithmic management in HRM—the integration of AI-driven tools into core HR functions—is reshaping worker autonomy, managerial legitimacy, and the identity of the HR profession itself. Based on a hermeneutic review of 48 peer-reviewed studies (2019–2025) and 7 recent policy reports, we trace the evolution from data-driven insight to data-driven control.While algorithmic systems offer efficiency and scalability, they also introduce opacity, lack of transparency, and psychological strain—particularly when decision-making is fully delegated to machines. Anchored in Self-Determination Theory and Labour Process Theory, we propose a novel three-stage DDD framework—Datafication (expansion of data capture), Delegation (increased algorithmic authority), and Displacement (removing human intervention)—to assess the intensity of algorithmic control in organisational settings.We offer a conceptual model to help HR scholars and practitioners diagnose risks of algorithmic governance and develop ethical countermeasures. We conclude with an agenda for Responsible Algorithmic HRM and outline actionable research pathways—including design-based, comparative, and qualitative studies—for future PhD-level work on people analytics, worker well-being, and digital governance.
Lakshman Satish (Sat,) studied this question.