Focus on the UN-Habitat / Mila report: “AI & Cities: Risks, Applications and Governance”

UN-Habitat, the United Nations program responsible for promoting sustainable urbanization in more than 90 countries, has published its report “AI & Cities: Risks, Applications and Governance”. Resulting from a collaboration with Mila, Quebec AI Institute, this document presents observations and recommendations on how AI systems could be exploited to support the development of socially sustainable cities and human settlements. and ecological.

The report “AI & Cities: Risks, Applications and Governance” presents an overview of some of the urban applications of AI, the risks they can generate, defines specific approaches and tools for the governance of urban AI. The objective is to provide local authorities with the tools to assess the relevance of the use of AI rather than to instruct on what is or is not the right opportunity for a given context.

It is part of UN-Habitat’s strategy to guide local authorities in a people-centric digital transformation process in their cities or settlements.

The document is structured in five main parts: an introduction to AI, guidelines for AI governance, an overview of AI applications in urban areas, a risk assessment framework and a guide to AI strategy.

1st and 2nd part: definition and governance of AI

First of all, the report defines AI, responsible AI, discusses different applications such as ML or DL, the opportunities that AI offers for cities, and its current limits. It then briefly discusses the importance of AI governance, which should be context-aware, human-respectful and centered on the public interest, as well as some of its key challenges.

Part 3: Overview of AI applications in urban areas

AI is evolving at such a rapid pace that the potential number of applications in the urban context is now enormous. Part 3 identifies key sectors where it is particularly useful for cities and gives examples of AI applications for each of these sectors.

The key sectors described include energy, mobility, public safety, water and
waste management, health care, urban planning and urban governance.

Each application presented is a concrete example of an existing technology, this example explicitly supports sustainable development, inclusive development and carbon sobriety.

Each application area is linked to the Sustainable Development Goals (SDGs). A series of labels are used to indicate high impact, locally relevant, and long-term efforts.

On the other hand, the applications are linked to specific risks of the risk management framework, although these links are provided for illustrative purposes.

Part 4: AI Risk Assessment Framework

The risk management framework provides an overview of AI risks, along with questions to assess them. It can be skimmed for a general understanding, but also provides the details needed to support technical teams starting with a general assessment of AI systems. The risks presented are not exhaustive.

The framework focuses on raising awareness of common issues with AI technology and its social implications. Its objective is to enable cities to develop their own strategies in order to implement responsible use of AI for sustainable urban development.

It highlights the different risks in the entire life cycle of an AI system which is divided into five phases: scoping, design, implementation, deployment and maintenance. Each risk is presented with a simple definition and concrete examples from different geographical locations. Graphs and links show the relationships between risks.

Each risk comes with reflective questions that work like an assessment toolbox. Emphasizing awareness rather than ‘techno-solutionism’, the questions provide direction for appropriate mitigation strategies. They thus highlight the possible places of intervention in order to mitigate the risks while still being based in the specific context.

The assessment of potential risks from AI systems should be done from a holistic perspective, encompassing both technical and societal considerations. Only when stakeholders clearly understand the structure and limitations of an AI system will they be able to take full advantage of it and optimize the functioning of the system in each particular context.

Part 5: Urban AI Strategy

This Urban AI Strategy is a practical guide to help cities and local authorities develop AI systems aligned with inclusive and sustainable development goals and a way to articulate context-specific local goals, as well as plan steps to exploit them.

This section focuses on concrete practical recommendations and suggestions for local authorities to develop an AI strategy and governance framework.

It leads a reflection on the creation of a favorable environment, promotes cooperation and the strengthening of local capacities. In addition, key tools particularly useful to support urban AI strategies, such as algorithmic registers or algorithmic impact assessments, are highlighted in a short case study.

You can access the full report by clicking on this link.

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