Barriers to the Effective Implementation of AI in Predicting Public Works - Usobiaga Ferrer, Elena; Molina-Costa, Patricia; Ispizua, Begoña; Izkara, José Luis (Street Art & Urban Creativity, 2025)
Public data quality issues often hinder AI applications in urban planning, affecting model applicability, effectiveness, and results. A case study on predicting public works impacted by network infrastructure and its city-wide impact is presented in this paper. This case has served to identify barriers and limitations to AI adoption in this subdomain, allowing to inform a set of recommendations to improve public data production and sharing, paving the way for future AI modelling in public works prediction. By addressing these challenges, cities can unlock the full potential of AI-driven urban planning and decision-making.