Published On: February 12, 2020 11:57 AM NPT By: Stefaan G Verhulst/Mona Sloane
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
NEW YORK – 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 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 maximize 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, usually with an added 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, localized 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.
Feedback loops can have a massive impact, particularly when it comes to AI. In some cases, local AI policies could have far-reaching effects on how technology is designed and deployed elsewhere. For example, by establishing an Algorithms Management and Policy Officer, New York City has created a model that can be emulated worldwide.
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 (Google’s parent company) 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. Similarly, in Brooklyn, New York, the tenant association of the Atlantic Plaza Towers (in collaboration with academic researchers and non-governmental organizations) succeeded in blocking a plan to use facial recognition technology in lieu of keys. Moreover, this effort offered important cues for how AI should be regulated more broadly, particularly in the context of housing.
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 decentralized 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 recognize 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. At The GovLab, for example, we are deepening our knowledge base and building the information-sharing mechanisms needed to make city-based initiatives a success. We plan to create a database of all instances of AI localism, from which to draw insights and a comparative list of campaigns, principles, regulatory tools, and governance structures.
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.
Stefaan G. Verhulst is Co-Founder of The GovLab at NYU’s Tandon School of Engineering and Editor-in-Chief of Data and Policy. Mona Sloane, a fellow at The GovLab, NYU’s Institute for Public Knowledge, and NYU’s Alliance for Public Interest Technology, is an adjunct professor at NYU’s Tandon School of Engineering
© 2020 Project Syndicate
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