Mapping AI Narratives at the Local Level

Curated on Posted on by Stefaan Verhulst

Article for Urban AI: “In May 2024, Nantes Métropole (France) launched a pioneering initiative titled “Nantes Débat de l’IA” (meaning “Nantes is Debating AI”). This year-long project is designed to curate the organization of events dedicated to artificial intelligence (AI) across the territory. The primary aim of this initiative is to foster dialogue among local stakeholders, enabling them to engage in meaningful discussions, exchange ideas, and develop a shared understanding of AI’s impact on the region.

Over the course of one year, the Nantes metropolitan area will host around sixty events focused on AI, bringing together a wide range of participants, including policymakers, businesses, researchers, and civil society. These events provide a platform for these diverse actors to share their perspectives, debate critical issues, and explore the potential opportunities and challenges AI presents. Through this collaborative process, the goal is to cultivate a common culture around AI, ensuring that all relevant voices are heard as the city navigates to integrate this transformative technology…(More)”.

Utilizing big data without domain knowledge impacts public health decision-making

Curated on Posted on by Stefaan Verhulst

Paper by Miao Zhang, Salman Rahman, Vishwali Mhasawade and Rumi Chunara: “…New data sources and AI methods for extracting information are increasingly abundant and relevant to decision-making across societal applications. A notable example is street view imagery, available in over 100 countries, and purported to inform built environment interventions (e.g., adding sidewalks) for community health outcomes. However, biases can arise when decision-making does not account for data robustness or relies on spurious correlations. To investigate this risk, we analyzed 2.02 million Google Street View (GSV) images alongside health, demographic, and socioeconomic data from New York City. Findings demonstrate robustness challenges; built environment characteristics inferred from GSV labels at the intracity level often do not align with ground truth. Moreover, as average individual-level behavior of physical inactivity significantly mediates the impact of built environment features by census tract, intervention on features measured by GSV would be misestimated without proper model specification and consideration of this mediation mechanism. Using a causal framework accounting for these mediators, we determined that intervening by improving 10% of samples in the two lowest tertiles of physical inactivity would lead to a 4.17 (95% CI 3.84–4.55) or 17.2 (95% CI 14.4–21.3) times greater decrease in the prevalence of obesity or diabetes, respectively, compared to the same proportional intervention on the number of crosswalks by census tract. This study highlights critical issues of robustness and model specification in using emergent data sources, showing the data may not measure what is intended, and ignoring mediators can result in biased intervention effect estimates…(More)”

AI Localism Repository: A Tool for Local AI Governance

Curated on Posted on by Stefaan Verhulst

About: “In a world where AI continues to be ever more entangled with our communities, cities, and decision-making processes, local governments are stepping up to address the challenges of AI governance. Today, we’re excited to announce the launch of the newly updated AI Localism Repository—a curated resource designed to help local governments, researchers, and citizens understand how AI is being governed at the state, city, or community level.

What is AI Localism?

AI Localism refers to the actions taken by local decision-makers to address AI governance in their communities. Unlike national or global policies, AI Localism offers immediate solutions tailored to specific local conditions, creating opportunities for greater effectiveness and accountability in the governance of AI.

What’s the AI Localism Repository?

The AI Localism Repository is a collection of examples of AI governance measures from around the world, focusing on how local governments are navigating the evolving landscape of AI. This resource is more than just a list of laws—it highlights innovative methods of AI governance, from the creation of expert advisory groups to the implementation of AI pilot programs.

Why AI Localism Matters

Local governments often face unique challenges in regulating AI, from ethical considerations to the social impact of AI in areas like law enforcement, housing, and employment. Yet, local initiatives are frequently overlooked by national and global AI policy observatories. The AI Localism Repository fills this gap, offering a platform for local policymakers to share their experiences and learn from one another…(More)”

Reviving the commons: A scoping review of urban and digital commoning

Curated on Posted on by Stefaan Verhulst

Report by James Henderson and Oliver Escobar: “The review aims to contribute to the growing discourse on the commons, highlighting its significance in contemporary societies and its potential as an alternative to traditional forms of socioeconomic and political organisation via the state and/or the market. Practitioners in the field argue that we are witnessing a revival of the commons in the 21st century. This report interrogates the nature of that revival and explores key concepts, examples, trends and debates in theory and practice, while outlining an emerging research agenda…(More)”.

Place identity: a generative AI’s perspective

Curated on Posted on by Stefaan Verhulst

Paper by Kee Moon Jang et al: “Do cities have a collective identity? The latest advancements in generative artificial intelligence (AI) models have enabled the creation of realistic representations learned from vast amounts of data. In this study, we test the potential of generative AI as the source of textual and visual information in capturing the place identity of cities assessed by filtered descriptions and images. We asked questions on the place identity of 64 global cities to two generative AI models, ChatGPT and DALL·E2. Furthermore, given the ethical concerns surrounding the trustworthiness of generative AI, we examined whether the results were consistent with real urban settings. In particular, we measured similarity between text and image outputs with Wikipedia data and images searched from Google, respectively, and compared across cases to identify how unique the generated outputs were for each city. Our results indicate that generative models have the potential to capture the salient characteristics of cities that make them distinguishable. This study is among the first attempts to explore the capabilities of generative AI in simulating the built environment in regard to place-specific meanings. It contributes to urban design and geography literature by fostering research opportunities with generative AI and discussing potential limitations for future studies…(More)”.

Atlas of Intangibles

Curated on Posted on by Stefaan Verhulst

About: “Atlas of Intangibles is a data experience designed to highlight the rich, interconnected web of sensory information that lies beneath our everyday encounters. Showcasing sensory data collected by me around the city of London through score-based data walks, the digital experience allows viewers to choose specific themes and explore related data as views — journeys, connections, and typologies. Each data point is rich in context, encompassing images and audio recordings…(More)”.

Data sovereignty for local governments. Considerations and enablers

Curated on Posted on by Stefaan Verhulst

Report by JRC Data sovereignty for local governments refers to a capacity to control and/or access data, and to foster a digital transformation aligned with societal values and EU Commission political priorities. Data sovereignty clauses are an instrument that local governments may use to compel companies to share data of public interest. Albeit promising, little is known about the peculiarities of this instrument and how it has been implemented so far. This policy brief aims at filling the gap by systematising existing knowledge and providing policy-relevant recommendations for its wider implementation…(More)”.

Modeling Cities and Regions as Complex Systems

Curated on Posted on by Stefaan Verhulst

Book by Roger White, Guy Engelen and Inge Uljee: “Cities and regions grow (or occasionally decline), and continuously transform themselves as they do so. This book describes the theory and practice of modeling the spatial dynamics of urban growth and transformation. As cities are complex, adaptive, self-organizing systems, the most appropriate modeling framework is one based on the theory of self-organizing systems—an approach already used in such fields as physics and ecology. The book presents a series of models, most of them developed using cellular automata (CA), which are inherently spatial and computationally efficient. It also provides discussions of the theoretical, methodological, and philosophical issues that arise from the models. A case study illustrates the use of these models in urban and regional planning. Finally, the book presents a new, dynamic theory of urban spatial structure that emerges from the models and their applications.

The models are primarily land use models, but the more advanced ones also show the dynamics of population and economic activities, and are integrated with models in other domains such as economics, demography, and transportation. The result is a rich and realistic representation of the spatial dynamics of a variety of urban phenomena. The book is unique in its coverage of both the general issues associated with complex self-organizing systems and the specifics of designing and implementing models of such systems…(More)”.

Governments Empower Citizens by Promoting Digital Rights

Curated on Posted on by Stefaan Verhulst

Article by Julia Edinger: “The rapid rise of digital services and smart city technology has elevated concerns about privacy in the digital age and government’s role, even as cities from California to Texas take steps to make constituents aware of their digital rights.

Earlier this month, Long Beach, Calif., launched an improved version of its Digital Rights Platform, which shows constituents their data privacy and digital rights and information about how the city uses technologies while protecting digital rights.

“People’s digital rights are no different from their human or civil rights, except that they’re applied to how they interact with digital technologies — when you’re online, you’re still entitled to every right you enjoy offline,” said Will Greenberg, staff technologist at the Electronic Frontier Foundation (EFF), in a written statement. The nonprofit organization defends civil liberties in the digital world.


Long Beach’s platform initially launched several years ago, to mitigate privacy concerns that came out of the 2020 launch of a smart city initiative, according to Long Beach CIO Lea Eriksen. When that initiative debuted, the Department of Innovation and Technology requested the City Council approve a set of data privacy guidelines to ensure digital rights would be protected, setting the stage for the initial platform launch. Its 2021 beta version has now been enhanced to offer information on 22 city technology uses, up from two, and an enhanced feedback module enabling continued engagement and platform improvements…(More)”.

Training LLMs to Draft Replies to Parliamentary Questions

Curated on Posted on by Stefaan Verhulst

Blog by Watson Chua: “In Singapore, the government is answerable to Parliament and Members of Parliament (MPs) may raise queries to any Minister on any matter in his portfolio. These questions can be answered orally during the Parliament sitting or through a written reply. Regardless of the medium, public servants in the ministries must gather materials to answer the question and prepare a response.

Generative AI and Large Language Models (LLMs) have already been applied to help public servants do this more effectively and efficiently. For example, Pair Search (publicly accessible) and the Hansard Analysis Tool (only accessible to public servants) help public servants search for relevant information in past Parliamentary Sittings relevant to the question and synthesise a response to it.

The existing systems draft the responses using prompt engineering and Retrieval Augmented Generation (RAG). To recap, RAG consists of two main parts:

  • Retriever: A search engine that finds documents relevant to the question
  • Generator: A text generation model (LLM) that takes in the instruction, the question, and the search results from the retriever to respond to the question
A typical RAG system. Illustration by Hrishi Olickel, taken from here.

Using a pre-trained instruction-tuned LLM like GPT-4o, the generator can usually generate a good response. However, it might not be exactly what is desired in terms of verbosity, style and writing prose, and additional human post-processing might be needed. Extensive prompt engineering or few-shot learning can be done to mold the response at the expense of incurring higher costs from using additional tokens in the prompt…(More)”