What does AI Localism look like in action? A new series examining use cases on how cities govern AI

Rapid urbanization and expansion have paved the way for increased technological innovation in cities. One such technological innovation is Artificial Intelligence (AI), a popular tool for detecting patterns and simulating human behavior from a deluge of data. While nations and supranational blocs increasingly debate how to govern AI, cities are increasingly leading the charge by developing governance frameworks and implementing policies at a quicker, more direct, and more impactful level: something we call AI Localism. Furthermore, a number of cities have proposed smart urbanism visions that move away from a techno-centric approach and toward a more human-centric one.

Localism, what’s in a name?

Numerous types of ‘localism’ exist as it relates to technology to both address specific, local needs that national policy is not fit to address, or to fill policy gaps in communities overlooked by national governments, or because of the proliferation of certain types of technology, such as facial recognition, within a community. Instances of broadband localism can be found in Sandy, Oregon, wherein the local government created ‘SandyNet’ to provide high-speed DSL and wireless internet connection at low costs. Examples of similar efforts can be seen in Hong Kong, Perth, Australia, Barcelona, Spain, and Seoul, South Korea. Examples of privacy localism include New York City’s local ordinance to regulate the collection and use of citizen data by governments and law enforcement agencies, and Barcelona’s efforts to regulate and involve citizens in the co-design and deployment of data-driven public services.

More and more, states and cities are advancing policy agendas to endorse the responsible and legitimate use of AI. For example, in May of 2019, San Francisco became the first U.S. city to ban police and local government agencies from using facial recognition technology to surveil and identify residents, addressing a growing threat to Fourth Amendment protections against unreasonable search and seizure, as well as increasing worries about AI-enabled racial discrimination and profiling.

We call local instances of AI governance ‘AI Localism.’ AI Localism refers to the governance actions—which include, but are not limited to, regulations, legislations, task forces, public committees, and locally-developed tools—taken by local decision-makers to address the use of AI within a city or regional state.

It is necessary to note, however, that the presence of AI Localism does not mean that robust national- and state-level AI policy are not needed. Whereas local governance seems fundamental in addressing local, micro-level issues, tailoring, for instance, by implementing policies for specific AI use circumstances, national AI governance should act as a key tool to complement local efforts and provide cities with a cohesive, guiding direction.

Finally, it is important to mention how AI Localism is not necessarily good governance of AI at the local level. Indeed, there have been several instances where local efforts to regulate and employ AI have encroached on public freedoms and hurt the public good. Toronto’s Harbourfront Centre neighborhood has received widespread criticism from the public for its decision to commission Sidewalk Labs to collect information about locals through sensors and cameras to ‘optimize’ the urban environment. After several public forums between the city of Toronto, Sidewalk Labs, and residents, a lack of transparency and public trust around who will have access to the data generated, how it will be analyzed, and for what specific purposes, coupled with the uncertainty stemming from the COVID19 pandemic, led to the cancellation of the project in May 2020.

Examining the current state of play in AI localism

To this end, The Governance Lab (The GovLab) has created the AI Localism project to collect a knowledge base and inform a taxonomy on the dimensions of local AI governance (see below). This initiative began in 2020 with the AI Localism canvas, which captures the frames under which local governance methods are developing. This series presents current examples of AI localism across the seven canvas frames: 

• Principles and Rights: foundational requirements and constraints of AI and algorithmic use in the public sector;
• Laws and Policies: regulation to codify the above for public and private sectors;
• Procurement: mandates around the use of AI in employment and hiring practices; 
• Engagement: public involvement in AI use and limitations;
• Accountability and Oversight: requirements for periodic reporting and auditing of AI use;
• Transparency: consumer awareness about AI and algorithm use; and
• Literacy: avenues to educate policymakers and the public about AI and data.

In this eight-part series, released weekly, we will present current examples of each frame of the AI localism canvas to identify themes among city- and state-led legislative actions. We end with ten lessons on AI localism for policymakers, data and AI experts, and the informed public to keep in mind as cities grow increasingly ‘smarter.’

In December 2022, we produced a report titled "AI Localism in Practice: Examining how Cities Govern AI" based on this blog series. Read the full report here. 

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We are deeply grateful to our colleagues for reviewing these blogs and lending their insight and input throughout the process. To learn more about AI localism, visit ailocalism.org. You can also explore over 100 case studies of AI localism across the world at the AI localism repository and submit your own examples here. Professionals interested in pursuing thought leadership on the subject are invited to contact Stefaan Verhulst at sverhulst@thegovlab.org.