Artificial intelligence enabled participatory planning: a review

Abstract

ABSTRACTParticipatory planning is a democratic spatial decision-making process involving multiple stakeholders. The integration of artificial intelligence (AI) methods in participatory planning has the potential to improve the decision-making process. However, there are challenges and limitations that need to be addressed. In this paper, we systematically review the progress of AI-enabled participatory planning, identifying strengths and weaknesses. We used a Strengths, Weaknesses, Opportunities, and Threats (SWOT) framework for our analysis, highlighting the opportunities for advancing AI in participatory planning and the potential threats that may arise. Our study provides valuable insights into the current state of AI-enabled participatory planning, paving the way for future developments and improvements.HighlightsDeep learning elevates participatory spatial decisions.AI’s strengths in urban planning are on data, communication, and automation.Emerging AI tools support richer urban research contexts.Challenges remain on digital divide, trust, privacy, and accountability.AI’s potential is an ethical urban asset rather than a controversial adversary.KEYWORDS: Artificial intelligenceGISparticipatory planningspatial decision support‌AI challenges and limitationsdemocratic decision-making Disclosure statementNo potential conflict of interest was reported by the author(s). We wish to extend our sincere gratitude to the anonymous reviewers for their insightful comments, constructive criticisms, and invaluable suggestions, all of which significantly improved the quality of this paper. We also thank the editor for their guidance and support throughout the review process. Their collective expertise and dedication have greatly enhanced our work. Additionally, we are grateful for the discussions with Dr. Walter Peacock and Dr. Michelle Meyer from Texas A&M University.Additional informationFundingThis work was supported by USA National Science Foundation [grant number 2122054, 2232533].