Abstract: Integrating Artificial Intelligence (AI) into urban planning transforms resource allocation and sustainable development. Nevertheless, the lack of transparency in some AI models raises questions about accountability and public trust. This paper investigates the role of Explainable AI (XAI) in urban planning, focusing on its ability to improve transparency and build trust between stakeholders. The study comprehensively examines approaches to achieving explainability, encompassing rule-based systems and interpretable machine learning models. Case studies illustrate the effective application of XAI in practical urban planning situations and highlight the critical role of transparency in the decision-making flow. This study examines the barriers that hinder the smooth integration of XAI into urban planning methodologies. These challenges include ethical concerns, the complexity of the models used, and the need for explanations tailored to specific areas.

Shan TONG and Shaokang LI. “Explainable Artificial Intelligence for Urban Planning: Challenges, Solutions, and Future Trends from a New Perspective”. International Journal of Advanced Computer Science and Applications (IJACSA) 15.7 (2024). http://dx.doi.org/10.14569/IJACSA.2024.0150777

@article{TONG2024,
title = {Explainable Artificial Intelligence for Urban Planning: Challenges, Solutions, and Future Trends from a New Perspective},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150777},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150777},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {7},
author = {Shan TONG and Shaokang LI}
}