Explainable Machine Learning Models for Trustworthy Decision-Making in Smart City Applications
This review synthesizes the state of the art in explainable machine learning as applied to smart city decision-making and outlines research directions toward causally grounded, human-centered, and regulation-aligned explainable AI systems capable of sustaining trustworthy algorithmic governance in future smart cities.