Human-Centered Digital Twins in IoT: Balancing Personalization, Privacy, and Ethics

Abstract

The integration of Human-Centered Digital Twins (HCDTs) and the Internet of Things (IoT) is revolutionizing industries by allowing personalized, real-time decision-making through the use of continuous data streams. These systems utilize IoT sensors and AI-driven models to produce digital copies of individuals, environments, or systems, providing improved predictive capabilities in healthcare, smart cities, and industrial applications. The increasing use of HCDTs sparks significant ethical issues, such as data privacy, confidentiality, discriminatory practices, and consent based on complete information. A significant gap persists in research, particularly in the establishment of uniform ethical frameworks and the implementation of dependable AI systems that safeguard user autonomy while optimising the advantages of digital twins. The purpose of this investigation is to investigate the ethical consequences of personalization in HCDTs and suggest a framework for reconciling data-driven personalization with user privacy and cybersecurity in IoT environments.