AI Policy Making: The 28 Day Plan Wrap-up
Charting New Territory: AI's Role in District Planning
ChatGPT-4 generated DALL·E prompt: An abstract concept of artificial intelligence merging with urban planning.
Introduction
Over the past five months, I have been steering an experiment that merges the esoteric world of Resource Management Planning with the dynamic capabilities of AI, particularly ChatGPT. The objective was ambitious: to formulate an alternative District Plan for Kaipara that could stand the test of complexity inherent in New Zealand's planning systems.
Setting the Stage
In Week 1, we dived into the intricacies of the Resource Management Act (RMA), setting up a project that was rooted in real-world data and current processes, while being mindful of the impending RMA reforms.
Building the Framework
As the weeks unfolded, the project's methodology evolved. Week 2 was characterised by strategic planning, prompt engineering, and overcoming the limitations of AI with quick thinking and adaptability.
Refining the Approach
Week 3 delved deeper into policy development, employing a 'Tree of Thought' approach to ensure cohesiveness and relevance as the plan took shape. The interplay between policies and methods was scrutinised through a preliminary s32 evaluation, demonstrating the AI's capability to generate nuanced and context-aware content.
Reflections and Learnings
The journey was as much about the destination—the 'Bootleg Kaipara Plan'—as it was about the insights gained. The lean and agile project management methods proved indispensable in harnessing the power of AI. These methods contrasted starkly with more traditional approaches, highlighting the importance of flexibility and iterative development in policy-making.
The Road Ahead
Though the 'Bootleg Kaipara Plan' remains unfinished, its existence is a testament to the potential of AI in urban planning. The plan will serve as a springboard for future experiments, with the exciting prospect of AI completing the task at the click of a button, looming on the horizon.
Conclusion
The Kaipara experiment has laid down a marker for what's possible. As I move on to new ventures, the insights gleaned from this project will inform not just my future work but also the broader conversation on AI's evolving role in urban planning.