Stefaan Verhulst (NYU), Andrew Young (NYU), and Mona Sloane (NYU) have posted “The AI Localism Canvas” on SSRN. Here is the abstract:
The proliferation of artificial intelligence (AI) technologies continues to illuminate challenges and opportunities for policymakers – particularly in cities (Allam/Dhunny 2019; Kirwan/Zhiyong 2020). As the world continues to urbanize, cities grow in their importance as hubs of innovation, culture, politics and commerce. More recently, they have also grown in significance as innovators in governance of AI, and AI-related concerns. Prominent examples on how cities are taking the lead in AI governance include the Cities Coalition for Digital Rights, the Montreal Declaration for Responsible AI, and the Open Dialogue on AI Ethics. Cities have also seen an uptick of new laws and policies, such as San Francisco’s ban of facial recognition technology or New York City’s push for regulating the sale of automated hiring systems. The same applies for new oversight initiatives and organizational roles focused on AI, such as New York City’s Algorithms Management and Policy Officer, and numerous local AI Ethics initiatives in various institutes, universities and other educational centers.
Considered together, all of these initiatives and developments add up to an emerging paradigm of governance localism, marked by a shift toward cities and other local jurisdictions in order to address a wide range of environmental, economic and societal challenges (Davoudi/Madanipour 2015). This article examines this field of AI Localism – a global move toward innovative governance of AI at the subnational level. The piece introduces the current state of play in the field, and introduces an “AI Localism Canvas” to help decision-makers identify, categorize and assess instances of AI Localism specific to a city or region. It provides several examples of AI governance innovation on the local level and provides an “AI Localism Canvas” as a framework to help guide the thinking of scholars and policymakers in identifying categorizing, and assessing the different areas of AI Localism within a city or region.