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AI is making modern cities more efficient

Stefaan G. Verhulst & Mona Sloane (Virtual Scape)
Filed on February 9, 2020 | Last updated on February 9, 2020 at 09.13 pm

Municipal, local jurisdictions in different cities are using the technology to address various issues.

Every new technology rides a wave from hype to dismay. But even by the usual standards, artificial intelligence has had a turbulent run. Is AI (artificial intelligence) a society-renewing hero or a jobs-destroying villain? As always, the truth is not so categorical.

As a general-purpose technology, AI will be what we make of it, with its ultimate impact determined by the governance frameworks we build. As calls for new AI policies grow louder, there is an opportunity to shape the legal and regulatory infrastructure in ways that maximise AI's benefits and limit its potential harms.

Until recently, AI governance has been discussed primarily at the national level. But most national AI strategies - particularly China's - are focused on gaining or maintaining a competitive advantage globally. They are essentially business plans designed to attract investment and boost corporate competitiveness, with an emphasis on enhancing national security.

This singular focus on competition has meant that framing rules and regulations for AI has been ignored. But cities are increasingly stepping into the void, with New York, Toronto, Dubai, Yokohama, and others serving as 'laboratories' for governance innovation. Cities are experimenting with a range of policies, from bans on facial-recognition technology and certain other AI applications to the creation of data collaboratives. They are also making major investments in responsible AI research, localised high-potential tech ecosystems, and citizen-led initiatives.

This 'AI localism' is in keeping with the broader trend in 'New Localism' as described by public-policy scholars Bruce Katz and the late Jeremy Nowak. Municipal and other local jurisdictions are increasingly taking it upon themselves to address a broad range of environmental, economic, and social challenges, and the domain of technology is no exception.

For example, New York, Seattle, and other cities have embraced what Ira Rubinstein of New York University calls 'privacy localism', by filling significant gaps in federal and state legislation, particularly when it comes to surveillance. Similarly, in the absence of a national or global broadband strategy, many cities have pursued 'broadband localism' by taking steps to bridge the service gap left by private-sector operators.

As a general approach to problem solving, localism offers both immediacy and proximity.

Because it is managed within tightly defined geographic regions, it affords policymakers a better understanding of the tradeoffs involved. By calibrating algorithms and AI policies for local conditions, policymakers have a better chance of creating positive feedback loops that will result in greater effectiveness and accountability.

AI localism also lends itself to greater policy coordination and increased citizen engagement. In Toronto, a coalition of academic, civic, and other stakeholders came together to ensure accountability for Sidewalk Labs, an initiative launched by Alphabet to improve services and infrastructure through citywide sensors. In response to this civic action, the company has agreed to follow six guidelines for "responsible artificial intelligence."

As this example shows, reform efforts are more likely to succeed when local groups, pooling their expertise and influence, take the lead. But AI localism is not a panacea. The same tight local networks that offer governance advantages can also result in a form of regulatory capture. As such, AI localism must be subject to strict oversight and policies to prevent corruption and conflicts of interest.

AI localism also poses a risk of fragmentation. While national approaches have their shortcomings, technological innovation (and the public good) can suffer if AI localism results in uncoordinated and incompatible policies. Both local and national regulators must account for this possibility by adopting a decentralised approach that relies less on top-down management and more on coordination. This, in turn, requires a technical and regulatory infrastructure for collecting and disseminating best practices and lessons learned across jurisdictions.

Regulators are only just beginning to recognise the necessity and potential of AI localism. But academics, citizens, journalists, and others are already improving our collective understanding of what works and what doesn't.

Building up our knowledge is the first step toward strengthening AI localism. Robust governance capacities in this domain are the best way to ensure that the remarkable advances in AI are put to their best possible uses.

- Project Syndicate

Stefaan G. Verhulst is Co-Founder of The GovLab at New York University's Tandon School of Engineering and Editor-in-Chief of Data and Policy. Mona Sloane, a fellow at The GovLab at New York University's Tandon School of Engineering, NYU's Institute for Public Knowledge, and NYU's Alliance for Public Interest Technology, is an adjunct professor at NYU.

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