Artificial Intelligence advancements potentially boosting government efforts to meet housing demands?
In the realm of urban development, AI is making a significant impact, particularly in data-driven and administrative tasks. By automating routine tasks, AI is easing workloads, allowing more time for strategic planning [1].
Bradford is setting a blueprint for low carbon heating, but the planning system, structured around nationally led policy, local plans, neighbourhood planning, planning applications, and an appeal system, is also undergoing a transformation. The use of a chat bot for application enquiries in small scale development is proposed, with a planning professional only needing to review the final recommendation in the case of automated processing of minor planning applications [2].
AI-powered project management tools can automate task creation, forecast resource needs, and detect potential delays early. Predictive analytics help anticipate project outcomes and recommend proactive solutions, improving transparency and reducing errors. AI also facilitates real-time information sharing across teams, ensuring better coordination [1][2][4].
However, there are challenges to consider. The computational complexity of AI planning, especially in large or dynamic environments, can demand substantial processing power and time. AI systems may struggle to handle uncertainty and unpredictable changes fully, impacting the robustness of plans. Overreliance on AI may also reduce human oversight, potentially causing missed nuances or ethical issues [3][4].
Engagement with the community is vital in the strategic planning process and should be regular, iterative, and relevant. AI can aid public engagement by targeting specific groups, but there's a risk of artificial narrowing of options or reinforcement of filter bubbles [5]. Downsides of AI include potential data inaccuracies, replication of unintended biases, and lack of democratic oversight [6].
In the housing sector, AI can review consultation responses and automatically categorize them, picking out key themes and identifying trends. Seven English local planning authorities may be asked to revise their home targets due to a gap with Labour's revised housing need assessment [7]. The UK government aims to build 1.5 million homes over the next Parliament, but the planning system's efficiency is often hindered by staffing shortages in local authorities [8].
The Renters' Rights Bill could potentially cost letting agents nearly £400m, while householder applications, Certificates of Lawfulness, or conditions discharge could potentially be automated by a computer program [9]. The DLHUC's PropTech engagement fund is being used by 13 local authorities to pilot the use of AI for public consultation on Local Plans [10].
Care must be taken to ensure that consultation responses have been summarized correctly and the auto-generated parts of a report make sense. AI has no intrinsic agency and no accountability; its output must be evaluated by an accountable human [11]. It's crucial to maintain a balance between the benefits of AI and the need for human oversight to avoid unintended consequences.
Policy-makers in the neighbourhood planning process are exploring the integration of technology, such as AI, to streamline tasks. In the housing sector, AI can be employed to analyze consultation responses and identify key themes, potentially helping to revise home targets more effectively. For instance, AI can automate the processing of minor housing applications in Bradford's neighbourhood planning, reducing workloads and increasing efficiency.
