AI-Driven Big Data Governance in Smart Cities

Abstract

Smart city big data governance is encumbered by data siloing, poor quality, and inefficient sharing, restricting ambient computing integration. This study applies artificial intelligence technologies—federated learning, knowledge graphs, and deep learning—to data cleaning, cross-departmental model sharing, and decision making. It proposes a technology–institution–context integrated model. Empirically analyzing three Chinese pilot cities from 2020 to 2023 shows that the AI strategy significantly reduced data cleaning time, elevated anomaly detection accuracy to over 95%, improved data standardization from approximately 30% to over 80% (with City C reaching 90.5%), increased cross-departmental shared data by approximately 5.5 to over 6 times, cut governance costs by approximately 62%, and lowered decision errors to approximately 1/3 of that of traditional methods. Challenges persist: inconsistent cross-regional standards, a lack of unified AI norms, and weak algorithm interpretability. This study enriches ambient computing governance theory and provides a practical framework for urban managers.