Towards Responsible Urban Geospatial AI: Insights From the White and Grey Literatures

• DOI: 10.1007/s41651-024-00184-2
• Publication Date: 2024-06-26T18:03:48Z
• Authors: Raveena Marasinghe; Tan Yigitcanlar; Severine Mayere; Tracy Washington; Mark Limb
• Keywords: White (mutation); Grey Literature

Abstract Artificial intelligence (AI) has increasingly been integrated into various domains, significantly impacting geospatial applications. Machine learning (ML) and computer vision (CV) are critical in urban decision-making. However, AI implementation faces unique challenges. Academic literature on responsible largely focuses general principles, with limited emphasis the domain. This important gap scholarly work could hinder effective integration Our study employs a multi-method approach, including systematic academic review, word frequency analysis insights from grey literature, to examine potential challenges propose strategies for (GeoAI) integration. We identify range of practices relevant complexities using planning its implementation. The review provides comprehensive actionable framework adoption domain, offering roadmap researchers practitioners. It highlights ways optimise benefits while minimising negative consequences, contributing sustainability equity.