Artificial intelligence is rapidly becoming part of the operating layer of modern cities—from infrastructure monitoring and traffic optimization to energy management, digital public services, and predictive decision-making. As these systems become more autonomous and interconnected, the central challenge is no longer simply whether an AI model performs well. Cities must also determine whether its decisions can be trusted, governed, monitored, explained, and safely operated over time.

This session presents a practical framework for deploying trustworthy AI in mission-critical smart-city environments. Drawing on lessons from regulated financial systems, enterprise AI transformation, data governance, and research into AI/ML-enabled IoT for smart-city infrastructure, the presentation will examine how proven controls from highly regulated environments can be adapted to urban technology systems.

Attendees will learn how to structure AI governance across data quality, model validation, explainability, human oversight, continuous monitoring, accountability, and operational resilience. The session will also explore how these principles apply to connected infrastructure, predictive analytics, urban data platforms, digital twins, and AI-supported public services.

Rather than treating responsible AI as a compliance exercise, the session will show how governance can become an engineering discipline that enables cities to scale AI innovation while maintaining public trust, reliability, transparency, and measurable community outcomes.