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The term "artificial intelligence" (AI) conjures images of autonomous vehicles maneuvering through streets, smartphone assistants that answer your questions, or androids exploring final frontiers.
At a basic level, however, AI can be understood as the multidisciplinary endeavor to approximate human reasoning with computation. For planners, it represents an emerging toolbox that enables a range of new capabilities. Whether AI primarily benefits entire communities or narrow interests, though, depends on planners' abilities to engage with the challenges and opportunities surrounding its civic applications. Naively applied, these technologies can automate discrimination, create unaccountable processes, and create a false certainty about what the future holds.
This PAS Memo intends to equip planners with an understanding of AI concepts and their potential uses for practice. And because planners have a responsibility to understand the implications of the technologies they choose to deploy and help to ensure that those technologies are used responsibly, it discusses important considerations regarding AI applications and their roles in larger trends connected to digital governance and civic data in planning.
About the Authors
David Wasserman, AICP
David works at the intersection of urban informatics, 3D visualization, geospatial analytics, and visual storytelling. He brings years of experience and passion to applying scientific computing, spatial analysis, and scenario-focused storytelling toward the development of effective transportation planning solutions aimed at improving communities. He is the author/coauthor of multiple articles and webinars with the APA concerning applications of AI and Computer Vision in planning including the Art of Learning by Example, Computer Vision & Community Vision, and APA's Planning Advisory Service Memo on AI. He has experience working on multimodal transportation plans, bicycle master plans, systemic safety studies, python tool & web applications, advanced data visualizations, parking studies, direct ridership models, and station area plans. His current areas of focus are enabling data-informed scenario planning, identifying how to align community goals to metrics to track progress towards them, incorporating civic data science into projects with web-delivery and computer vision derived datasets, and generating accessibility metrics that can identify the possible benefits of projects and who they go to.
Michael Flaxman, PHD, is the spatial data science practice lead at Heavy.ai. After 20 years of working within the domain of spatial environmental planning, he now actively works to develop the next generation of geospatial computing technologies at Heavy.ai. His main goal is to continue to develop spatial scenario planning tools, ultimately to bring the benefits of sustainable environmental planning to a much wider global audience.