IoT technologies simplify remote access and continuous-time data sourcing from connected devices. Remote management and control capabilities, combined with AI/ML techniques, help organizations to improve industrial efficiency and operational predictability. However, the increasing use of black-box techniques for predictive diagnostics about the health of machines in factories or spare capacity in smart city transportation systems introduces a new element in decision making. How trustworthy is a predicted failure if it leads an engineer to shut down a production line? Or, how dependable are predictions about car parking spaces or shared bikes for commuters planning their city centre journeys? Explainable AI (XAI) is at the cutting edge of new techniques to increase confidence in predictions and to reason about diagnostics from AI and ML systems. XAI is also a key enabler to enforce digital rights under GDPR-like regulations. This panel will consider new developments in the field of AI, their implications for data life-cycle management and, the interplay between IoT and AI/ML disciplines when designing more dependable and trustworthy systems.