Infrastructure is the backbone of any community, and in New Zealand, local councils are responsible for ensuring that roads, water systems, and public facilities remain safe and functional. Artificial Intelligence (AI) is proving supportive in enabling councils to move from reactive maintenance to proactive, data-driven infrastructure planning. This article delves into AI’s role in infrastructure planning and maintenance for local councils, illustrating real-life applications and the critical governance considerations required for responsible AI use.
AI Use Cases in Infrastructure Planning and Maintenance
- Predictive Maintenance of Roads and Pavements Example: Auckland Transport uses AI-powered predictive maintenance systems to monitor the condition of roads and pavements (irisGO?). By analysing data from road sensors and historical maintenance records, these AI systems identify areas likely to degrade and require repair before issues become severe. This predictive approach reduces maintenance costs and minimises disruption for residents. Governance Considerations: Councils must ensure transparency in data collection and use. Governance policies should define data privacy practices and address potential biases in predictions, ensuring equitable maintenance across different neighbourhoods (e.g., protecting underserved areas from neglect).
- Smart Water Management Example: Wellington Water employs AI to monitor water infrastructure, detecting leaks and forecasting potential pipe failures based on environmental data and usage patterns (GHD Digital). By identifying high-risk areas, the AI-driven system enables Wellington Water to prioritise repairs, optimise resource allocation, and avoid costly disruptions. Governance Considerations: With water being a critical resource, AI-driven decisions must undergo regular audits to ensure accuracy and fairness. Governance protocols should include data validation procedures and require human oversight to confirm AI recommendations in high-stakes scenarios.
- AI-Enabled Fire Protection for Early Detection Example: Christchurch City Council has partnered with Attentis Technology to deploy IoT and AI systems for early fire detection, especially in at-risk areas. These systems monitor environmental factors and detect unusual patterns that could indicate the onset of a fire, allowing for swift intervention before fires can spread. This proactive approach aids in preventing fires that could cause devastating damage to communities and natural environments (Attentis Technology, 2023). Governance Considerations: Fire detection technologies that monitor environmental and thermal data raise important considerations around data privacy and use. Councils should establish clear guidelines to limit data collection to essential areas and ensure the data is used responsibly. Additionally, periodic audits and transparency with local communities can help build trust in AI-enhanced safety initiatives.
- Traffic Flow Management and Smart Lighting Example: In Tauranga, AI technology optimises traffic light timings based on real-time traffic flow data, reducing congestion and minimising carbon emissions (smart traffic lights). By analysing data from sensors and CCTV cameras, the system adapts to changing traffic patterns, contributing to smoother, more efficient city travel. Governance Considerations: Traffic flow data collection introduces privacy considerations, especially when CCTV cameras are involved. Councils should maintain transparency regarding data usage and retention, balancing efficiency with privacy by avoiding data collection beyond what is necessary for improving public services.
Governance Considerations: Balancing Efficiency and Public Trust
While AI offers powerful tools for infrastructure planning and maintenance, these applications require a balanced governance approach that maintains public trust. Here are key governance considerations for councils:
- Accountability in AI Decision-Making AI systems often rely on predictive models that may not always reflect local realities. For example, data used in road maintenance may inadvertently prioritise certain areas. Regular review and auditing are essential to ensure AI recommendations align with community needs and that council staff retain decision-making oversight.
- Data Privacy and Consent As councils gather more data to feed AI systems, it is crucial to respect resident privacy and comply with the Privacy Act 2020. Councils should establish guidelines that define how data is collected, stored, and used, especially for systems monitoring high-density public areas or private properties.
- Fairness and Equity in Service Provision Infrastructure investments and maintenance should serve all community segments fairly. AI can unintentionally amplify disparities if not carefully governed. Councils should ensure that AI algorithms are tested and validated to avoid bias and distribute resources equitably across neighbourhoods.
- Transparency and Public Communication Transparency in AI-driven infrastructure initiatives is vital to building public trust. Local councils should proactively communicate with residents about AI’s role in infrastructure planning, how data is used, and how decisions are made. Clear, accessible public reporting can enhance trust and promote community support for these AI initiatives.
AI is reshaping how local councils in New Zealand approach infrastructure planning and maintenance, offering smarter, more cost-effective solutions for managing roads, water systems, fire protections, and traffic. However, the potential of AI must be balanced with robust governance practices that prioritise accountability, transparency, and fairness. By addressing these considerations, councils can harness AI responsibly, strengthening infrastructure and enhancing community trust.
- Attentis Technology. (2023). Christchurch City Council deploys IoT technology to aid in early fire detection and helps prevent devastating fires that can impact environments and communities. Retrieved from https://attentistechnology.com/news/christchurch-city-council-deploys-iot-technology-to-aid-in-early-fire-detection-and-helps-prevent-devastating-fires-that-can-impact-environments-and-communities/
- Reid, J., & Aliani, L. (2021). Infrastructure monitoring with artificial intelligence in public service: Opportunities and ethical challenges. Public Administration Review, 81(5), 840-850. https://doi.org/10.1111/puar.13305
- Schumann, M., Shikata, D., & Liu, Z. (2022). Smart urban infrastructure planning: Lessons and recommendations for local government. Journal of Urban Technology, 29(4), 1-15. https://doi.org/10.1080/10630732.2022.2045704
