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The Emergence of AI Localism: Governing Artificial Intelligence at the Local and City Level

Last month, several experts and practitioners gathered on-line to reflect on the use of AI in managing pandemics as part of The Responsible AI Forum (Preview) — organized by the Institute for Ethics in Artificial Intelligence (TUM, Munich), in close cooperation with the Global AI Ethics Consortium.

I had the pleasure to join a panel that was comprised of Dirk Brand (Stellenbosch), Christian Djeffal (Munich), mark findlay (Singapore), Christoph Lütge (Munich), and Jeannie Marie Paterson (Melbourne) that focused on “Governing Responsible AI in Health Crises”.

Delighted to share below the short presentation I shared on the emergence of AI Localism [SLIDE DECK].

WHAT AND WHY: The growing use of AI within cities has fueled a phenomenon that we have coined as “AI Localism”. AI Localism refers to the initiatives and experiments taken by local decision-makers to establish innovative approaches to AI governance. It seeks to fill in gaps left by incomplete governance at the national level as well as by the private sector.

FRAME OF ANALYSIS: Our AI Localism Canvas provides a framework and a tool to identify, assess and guide policymakers and researchers on the steps and mechanisms local decision-makers can consider when developing responsible approaches to AI within their city or community. It also allows for cataloging innovative approaches developed by cities.

The AI Localism Canvas

In particular, the AI Localism Canvas identifies the following areas of AI governance innovation at the local level:

  • Developing Common Principles,
  • Engaging with Citizens, Local Universities, and other stakeholders,
  • Providing for AI Literacy programs among officials and the public-at-large,
  • Drafting dedicated Laws, Bans, and Policies,
  • Providing for Accountability and Oversight,
  • Assuring Transparency, and
  • Leveraging Procurement toward Responsible AI.

EXAMPLES: For the remainder of the talk I focused on a few examples in each category:

Principles: Cities, Barcelona, and Montreal for example have produced their own AI principles as guidelines for responsible development and usage of AI within their local boundaries.

Engagement (Citizens & Universities): Citizen engagement, such as the Data Assembly in NYC can engage citizens toward understanding expectations and co-creating solutions. Engaging with universities and programs, like the Montreal AI Ethics Institute, can enable cities to tap into local expertise.

AI Literacy: The Ethics and Algorithms Toolkit and the Queens Public Library technology training are tools that can be developed and offered to establish AI Literacy across cities.

Laws and Policies: Local laws and policies, such as the Portland facial recognition ban or the proposed NYC employment AI regulation , can provide city leaders the means to enforce restrictions on unethical technology.

Accountability and Oversight: Some cities have experimented with various oversight bodies such as the NYC Automated Decision Systems Task Force and the Seattle Surveillance Advisory Working Group, to perform various decision-making processes and hold responsibility and/or accountability.

Transparency: Several tools have been created, like the Amsterdam and Helsinki AI registries, to promote transparency of algorithms used in the city.

Procurement: Local procurement guidelines, like the ordinance coming out of the City of Berkeley , can enforce ethical and standardized AI adoption and use.

These areas are all signs that cities have become laboratories of innovation in AI governance. Moving forward, the goal of our AI Localism program is to deepen and leverage the Canvas, and tailor or expand it to fit local needs and values, and turn it into a mapping tool to responsibly improve city technology and regulation of AI.

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