Challenges of Social Sensing From a Safety and Security Perspective

Social sensing is a data collection paradigm that leverages human participation through connected devices in AIoT systems. It enhances real-time decision-making in areas like disaster response, healthcare, and transportation. However, it poses serious challenges related to data privacy, misinformation, identity verification, and system reliability. Threats such as data poisoning, replay attacks, and the opacity of black-box AI models increase the risk. Ethical concerns also arise from continuous data collection without user consent or transparency. Addressing these issues requires a multi-faceted strategy involving anomaly detection, encryption, Explainable AI, and human oversight. Case studies from Beirut, Hong Kong, and COVID-19 highlight the double-edged nature of social sensing. Future systems must combine technological innovation with ethical safeguards to ensure trust, security, and inclusivity in the digital era.