Responsible Urban Innovation with Artificial Intelligence Systems for Local Governments

  • Responsible Urban Innovation with Artificial Intelligence Systems for Local Governments

Project Title:

Responsible Urban Innovation with Artificial Intelligence Systems for Local Governments

Project Type:

Australian Research Council Discovery Project (DP220101255)

Funding Body:

Australian Research Council

Project Timeframe:

2022-2025

Project Synopsis: 

Artificial intelligence (AI) is increasingly becoming a cornerstone in urban services, shaping the trajectory of city development and societal evolution. While AI holds immense promise for enhancing urban innovation, it also poses significant risks when not implemented responsibly. The current landscape often lacks a comprehensive understanding of the costs, benefits, risks, and impacts associated with the deployment of AI systems by governments. This knowledge gap can lead to unintended negative consequences, such as reinforcing existing inequalities or compromising privacy and security.

This project aims to bridge these gaps by equipping local governments with the tools needed to engage with AI responsibly. Our primary objective is to develop detailed best practice guidelines for the responsible adoption and implementation of AI by Australian local governments. These guidelines will emphasise the importance of using AI technologies in ways that promote equitable and sustainable urban planning and services.

To achieve these goals, the project’s conceptual framework adopts a multidimensional approach, integrating community perspectives, policy considerations, and technological advancements. This framework is built around the core values of responsible AI: acceptability, accessibility, alignment with societal values, trustworthiness, and sound governance. By adhering to these principles, the project seeks to create an environment where AI can be harnessed to benefit all members of society while mitigating potential risks.

The figure below illustrates the key components and interconnections within this framework, showcasing how these elements work together to facilitate responsible AI integration in urban settings. This conceptual framework highlights the critical role of three key drivers—technology, policy, and community—in developing responsible AI systems within local governments.

At the heart of the framework lies the ‘technology’ aspect, which embodies the essential attributes of responsible AI, including accessibility, acceptability, trustworthiness, alignment, and effective governance. The ‘policy’ aspect acts as a conduit between the development and implementation phases of responsible AI, addressing factors such as stakeholder collaboration, resource management, data governance, and regulatory compliance. Meanwhile, the ‘community’ aspect underscores the importance of understanding and addressing community perceptions and expectations, which may vary based on factors such as trust in local government, exposure to AI, and societal norms regarding AI use.

By effectively integrating and leveraging these three drivers, local governments can successfully promote the responsible integration of AI into the communities they serve, ensuring that technological advancements contribute positively to urban life and governance.

Chief Investigators: 

  • Prof Tan Yigitcanlar (Project lead) (QUT)
  • Prof Kevin Desouza (QUT)
  • Prof Karen Mossberger (Arizona State University)
  • Prof Juan Corchado (University of Salamanca)
  • Prof Rashid Mehmood (King Abdulaziz University)
  • Prof Rita Li (Hong Kong Shue Yan University)
  • Prof Pauline Hope Cheong (Arizona State University)

Research Assistants: 

  • Sajani Senadheera (QUT)
  • Anne David (QUT)
  • Raveena Marasinghe (QUT)
  • Ke Liu (QUT)

Selected Research Outputs: 

  1. Yigitcanlar, T., (2025). Urban artificial intelligence: a guidebook for understanding concepts and technologies. London, UK: CRC Press.
  2. Yigitcanlar, T., (2025). Urban artificial intelligence: a guidebook for understanding perceptions and ethics. London, UK: CRC Press.
  3. Yigitcanlar, T., Corchado, J., Mossberger, K., Cheong, P., & Li, R., (2024). Local governments are using AI without clear rules or policies, and the public has no idea. The Conversation, 11 Dec 2024.
  4. Sanchez, T., Fu, X., Yigitcanlar, T., Ye, X. (2024). The research landscape of AI in urban planning: a topic analysis of the literature with ChatGPT. Urban Science, 8(4), 197.
  5. David, A., Yigitcanlar, T., Desouza, K., Li, R., Cheong, P., Mehmood, R., & Corchado, J., (2024).Understanding local government responsible AI strategy: an international municipal policy document analysis. Cities, 155(1), 105502.
  6. Senadheera, S., Yigitcanlar, T., Desouza, K., Li, R., Corchado, J., Mehmood, R., Mossberger, K., & Cheong, P. (2024). Metaverse as local government communication platform: a systematic review through the lens of publicness theory. Cities, 155(1), 105461.
  7. Yigitcanlar, T., Senadheera, S., Marasinghe, R., Bibri, S., Sanchez, T., Cugurullo, F., & Sieber, R. (2024). Artificial intelligence and the local government: a five-decade scientometric analysis on the evolution, state-of-the-art, and emerging trends. Cities, 152(1), 105151.
  8. Yigitcanlar, T., David, A., Li, W, Fookes, C., Bibri, S., & Ye, X. (2024). Unlocking artificial intelligence adoption in local governments: best practice lessons from real-world implementations, Smart Cities, 7(4), 1576-1625.
  9. Yigitcanlar, T., Degirmenci, K., & Inkinen, T. (2024). Drivers behind the public perception of artificial intelligence: insights from major Australian cities. AI & Society, 39(1), 833-853.
  10. Regona, M., Yigitcanlar, T., Hon, C., & Teo, M. (2024). Artificial intelligence and sustainable development goals: systematic literature review of the construction industry. Sustainable Cities and Society, 108(1), 105499.
  11. Senadheera, S., Yigitcanlar, T., Desouza, K., Mossberger, K., Corchado, J., Mehmood, R., Li, R. & Cheong, P. (2024). Understanding chatbot adoption in local governments: a review and framework. Journal of Urban Technology, 1-35.
  12. Shaamala, A., Yigitcanlar, T., Nili, A., & Nyandega, D. (2024). Strategic tree placement for urban cooling: a novel optimisation approach for desired microclimate outcomes. Urban Climate, 56(1), 102084.
  13. Shaamala, A., Yigitcanlar, T., Nili, A., & Nyandega, D. (2024). Algorithmic green infrastructure optimisation: review of artificial intelligence driven approaches for tackling climate change. Sustainable Cities and Society, 101(1), 105182.
  14. Marasinghe, R., Yigitcanlar, T., Mayere, S., Washington, T., & Limb, M. (2024). Towards responsible urban geospatial AI: insights from the white and grey literatures. Journal of Geovisualization and Spatial Analysis, 8(1), 24. 
  15. Hossain, T., Yigitcanlar, T., Nguyen, K., & Xu, Y. (2024). Local government cybersecurity landscape: a systematic review and conceptual framework. Applied Sciences, 14(13), 5501. 
  16. Hossain, T., Yigitcanlar, T., Nguyen, K., & Xu, Y. (2024). Understanding local government cybersecurity policy: a concept map and framework. Information, 15(6), 342.
  17. Li, F., Yigitcanlar, T., Nepal, M., Nguyen, K., & Dur, F. (2024). A novel urban heat vulnerability analysis: integrating machine learning and remote sensing for enhanced insights. Remote Sensing, 16(16), 3032.
  18. Cugurullo, F., Barns, S., Del Casino Jr, V., Gulsrud, N., Yigitcanlar, T., & Zhang, X. (2023). The governance of artificial intelligence in the “autonomous city”. Frontiers in Sustainable Cities, 5(1), 1285175.
  19. David, A., Yigitcanlar, T., Li, R., Corchado, J., Cheong, P., Mossberger, K., & Mehmood, R. (2023). Understanding local government digital technology adoption strategies: a PRISMA review. Sustainability, 15(12), 9645.
  20. Li, W., Yigitcanlar, T., Browne, W., & Nili, A. (2023). The making of responsible innovation and technology: an overview and framework. Smart Cities, 6(4), 1996-2034.
  21. Li, W., Yigitcanlar, T., Nili, A., & Browne, W. (2023). Tech giants’ responsible innovation and technology strategy: an international policy review. Smart Cities, 6(6), 3454-3492.
  22. Marasinghe, R., Yigitcanlar, T., Mayere, S., Washington, T., & Limb, M. (2023). Computer vision applications for urban planning: a systematic review of opportunities and constraints. Sustainable Cities and Society, 100(1), 105047.
  23. Regona, M., Yigitcanlar, T., Hon, C., & Teo, M. (2023). Mapping two decades of AI in construction research: a scientometric analysis from the sustainability and construction phases lenses. Buildings, 13(9), 2346.
  24. Son, T., Weedon, Z., Yigitcanlar, T., Sanchez, T., Corchado, J., & Mehmood, R. (2023). Algorithmic urban planning for smart and sustainable development: systematic review of the literature. Sustainable Cities and Society, 94(1), 104562.
  25. Yigitcanlar, T., Agdas, D., & Degirmenci, K. (2023). Artificial intelligence in local governments: perceptions of city managers on prospects, constraints and choices. AI & Society, 38(3), 1135-1150.
  26. Yigitcanlar, T., Li, R., Beeramoole, P., & Paz, A. (2023). Artificial intelligence in local government services: public perceptions from Australia and Hong Kong. Government Information Quarterly, 40(3), 101833.
  27. Yigitcanlar, T., Li, R., Inkinen, T., & Paz, A. (2022). Public perceptions on application areas and adoption challenges of AI in urban services. Emerging Sciences Journal, 6(6), 1199-1236.
  28. Nili, A., Desouza, K., & Yigitcanlar, T. (2022). What can the public sector teach us about deploying artificial intelligence technologies? IEEE Software, 39(6), 58-63.
  29. Yigitcanlar, T., Corchado, J., Mehmood, R., Li, R., Mossberger, K., & Desouza, K. (2021). Responsible urban innovation with local government artificial intelligence (AI): a conceptual framework and research agenda. Journal of Open Innovation: Technology, Market, and Complexity, 7(1), 71.