Ethical, Legal, and Governance Framework for AI-Enabled Smart Surveillance in Heterogeneous Policing Networks

Abstract

The increasing use of AI-enabled surveillance in policing has created heterogeneous networks integrating IoT sensors, machine learning models, and multi-agency information systems across diverse vendors and jurisdictions. This chapter proposes an ethical, legal, and governance framework for AI-driven smart surveillance in such environments. Drawing on explainable AI (XAI), human-centric trust, and communication science, it argues that human oversight must ensure interpretability, accountability, contestability, and institutional transparency rather than serving as a procedural formality. The chapter identifies a critical regulatory gap at the data fusion layer, where outputs from multiple AI systems are combined without adequate legal oversight. To address this challenge, it presents a transparent governance architecture incorporating accountability mechanisms, risk-tiering, interoperable standards, a risk assessment matrix, and a secure data-flow model. Case studies on facial recognition errors and predictive policing bias illustrate governance challenges and mitigation strategies.