Artificial Intelligence & Planning Practice
It is really hard to be comprehensive and approachable on complex and multidisciplinary topics such as Artificial Intelligence (AI). My coauthor Dr. Michael Flaxman and I tried to equip planners with an understanding of AI concepts and their potential uses for practice in Planning Advisory Service Memo 111. 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.
I personally was motivated to complete this article because there are key areas where I feel AI and related technologies can augment practice to address key contemporary challenges such as community affordability, safe systems, digital governance, and adapting human settlements to the climate crisis. Some of the most inspiring examples to me mentioned in this vein include American Forests use of high-resolution tree canopy data to build Tree Equity Score. At the same time, I was motivated to highlight what other researchers have identified as potential risks such as cementing historical analytical frames, exacerbating inequality, and enabling delusions of certainty.
If you are interested, my website's publications page for the article is below, but you can find the Memo at APA’s Website here. If you are not an APA member, you can just reach out to me.
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Acknowledgements
This has been something in the works for the for the past 6 months, with many people I am grateful to for influencing and enabling this publication.
Hi David, Open Systems Lab is working in this area starting with our equivalent of England’s national planning code at the moment. You said to reach out, can we have a copy of PAS memo 111 please? Of course we’ve got a long way to go in the UK with our discretionary system. Enjoyed your extract, in built bias is very important in this context. So learning about policy outcomes and avoiding mistakes of the past is the deep work. Thanks for your contribution.
John Trieu Rafid Morshedi Christine Seeliger David Rawlinson
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