Artificial Intelligence and Planning Practice
Cite as: Wasserman, D. Flaxman, M. (2022). Artificial Intelligence and Planning Practice. PAS Memo 111. American Planning Association. Retrieved from https://www.planning.org/pas/memo/111/artificial-intelligence-and-planning-practice/
Download at: Planning Advisory Service Memo 111
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Excerpt Figure: Civic analytics can assist with a broad spectrum of challenges within operations, policy, and planning for cities and regions (adapted from Kontokosta 2021).
Excerpt from Artificial Intelligence & Planning Practice
CONSIDERATIONS FOR AI AND PLANNING
AI opens new possibilities for planning practice, but it requires awareness of the technological foundations underpinning planning methods and practice and acknowledgement of the risks associated with AI and related emerging technologies . Responsible and effective applications of AI in urban planning practice will depend on planners' understanding of these issues.
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When Past Should Not Be Prologue
Planning requires thinking about how policies and public investments shape potential pathways for community futures (Wright 2019). Statistics and machine learning will develop predictive models for the future based on past data, and by so doing the insights created from them use the past as prologue (ITF 2019; Mayson 2018). There are two major concerns, however, regarding the application of these models:
Cementing past mistakes. Basing decisions on predictions from historical data is likely to repeat and reinforce the outcomes of the past (Mayson 2018). This can often follow the use of metrics or data that are convenient or at hand, with outputs reinforcing historic values or creating unintended outcomes (Crawford 2021). Predictive models can mirror how we have historically addressed problems rather than reflecting the lens we bring to them now (Mayson 2018). In other words, we risk automating processes that were problematic to begin with because they will “inherit” the analytical frame of the system they originate from.
Managing change. Predictions based solely on historical data will not adapt to changing conditions. For example, a planning challenge likely to define the 21st century will be planning for a changing climate. As of 2019, carbon dioxide concentrations not seen for two million years are clear examples of how pure machine-learning models may not provide as much value in an environment where conditions change (IPCC 2021).
For these reasons, care should be taken when these algorithms are applied in situations with high degrees of uncertainty or unprecedented circumstances, or where they are likely to reinforce undesirable historical outcomes.