Artificial intelligence in America’s digital city

Cities are an engine for human prosperity. By putting people and businesses in close proximity, cities serve as the vital hubs to exchange goods, services, and even ideas. Each year, more and more people move to cities and their surrounding metropolitan areas to take advantage of the opportunities available in these denser spaces.

Technology is essential to make cities work. While putting people in close proximity has certain advantages, there are also costs associated with fitting so many people and related activities into the same place. Whether it’s multistory buildings, aqueducts and water pipes, or lattice-like road networks, cities inspire people to develop new technologies that respond to the urban challenges of their day.

Today, we can see the responses made possible by the advances of the second industrial revolution, namely steel and electricity. Multistory buildings and skyscrapers responded to our demand for proximity to do business in the same locations. Electrified and subterranean railways offered faster travel for more people in tight, urban quarters. The elevator, escalator, and advanced construction equipment allowed our buildings to grow taller and our subways to burrow deeper. Electric lighting turned our cities, suburbs, and even small towns into 24-hour activity centers. Air conditioning greatly improved livability in warmer locations, unlocking a population boom. Radios and television extended how far we can communicate and the fidelity of the messages we sent.

We are now in the midst of a new industrial era: the digital age. And like the industrial revolutions to precede it, the digital age doesn’t represent a single set of new products. Instead, the digital age represents an entirely new platform on top of which many everyday activities operate. Making all this possible are rapid advances in the power, portability, and price of computing and the emergence of reliable, high-volume digital telecommunications.

Some of the most important developments are taking place in the area of artificial intelligence (AI). At its most essential level, AI is a collection of programmed algorithms to mimic human decisionmaking. Definitions can vary widely on exactly what constitutes AI, what its applications will look like in the real world, the solutions AI applications will provide, and the new challenges those same applications will introduce. What is not in question is the heightened curiosity and eagerness to better understand AI to maximize its value to humanity and our planet.

Like every form of technology to proceed it, society must be intentional with the exact challenges we want AI to solve and be considerate of the social groups and industries who stand to benefit from the applications we deliver.

How AI will function in the built environment certainly fits into that category—and for good reason. Even though AI is still in its infant stages, we already encounter it on a daily basis. When your video conference shifts the microphone to pick up the speaker’s voice, when your smartphone automatically reroutes you around traffic, when your thermostat automatically lowers the air conditioning on a cool day—that’s all AI in action.

This brief explores how AI and related applications can address some of the most pressing challenges facing cities and metropolitan areas. Like every form of technology to proceed it, society must be intentional with the exact challenges we want AI to solve and be considerate of the social groups and industries who stand to benefit from the applications we deliver. While AI is just in its early development, now is the ideal time to bring that intentionality to urban applications.

Defining artificial intelligence in an urban context

Data has always been central to how practitioners plan, construct, and operate built environment systems. At its core, constructing those physical systems requires extensive knowledge of various engineering, geographic, and design principles, all of which are powered by mathematics. Quantitative information and mathematical principles are essential to successfully bring large-scale projects from their blueprints to physical reality, and that was as true in the ancient world as it is today.

The digital age only intensifies the need to use data to manage the built environment. Seemingly every human activity in the 21st century creates a data trail: business transactions, phone calls and text messages, turn-by-turn navigation. If you own a cellphone, simply moving from neighborhood to neighborhood creates a data trail as you jump from one cell tower to the next. Meanwhile, the equipment that constructs our buildings and infrastructure is now digitized, many of which can export data wirelessly. The computing industry also continues to innovate, creating ever-more processing power, storage capacity, and analytical software. We’re simply awash in data and processing power.The question is how to how to maximize data’s value. As the production cost of environmental sensors and network devices continues to drop, the ability to use reliable mobile telecommunications and cloud computing is bringing the concept of the Internet of Things (or IoT) to life. Effectively, IoT represents the systems that will enable sensors deployed across various built environment systems and equipment to speak to one another, increasing both the volume and velocity of data movement and creating new opportunities to interconnect physical operations.

The emerging result is a new kind of data-driven approach to urban management, what many communities commonly refer to as smart city programs. While there is no single definition of a smart city program—and online listicles aside, there’s really no way to judge whether an entire municipality or metropolitan area is “smart”—the common element is the use of interconnected sensors, data management, and analytical platforms to enhance the quality and operation of built environment systems.

This is where artificial intelligence and machine learning come into play. My Brookings colleague Chris Meserole authored a piece that explains machine learning in greater detail, including how statistics inform algorithms’ estimates of probability. The goal of machine learning is to replicate how humans would assess a given problem set using the best available data, primarily by building a layered network of small, discrete steps into a larger whole known as a neural network. As the algorithms continue to process more and more data, they learn which data better suits a given task. It’s beyond the scope of this brief to describe machine learning in greater detail, but you can learn more through Brookings’s Blueprint for the Future of AI.

In conjunction with machine learning, AI is well-suited to form the analytical foundation of smart city programs. Machine learning can process the enormous data volumes spit-off by built environment systems, creating automated, real-time reactions where appropriate and delivering manageable analytics for humans to consider. And since data volumes will continue to grow exponentially, local governments and their partners will be able to use AI to maximize opportunities from the data deluge. For these reasons, Gartner expects AI to become a critical feature of 30% of smart city applications by 2020, up from just 5% a few years prior.

In conjunction with machine learning, AI is well-suited to form the analytical foundation of smart city programs.

But AI is relatively worthless without a set of intentional goals to complement it. Organizing, processing, analyzing, and even automatically acting on data is only a secondary set of actions. Instead, the initial task facing the individuals who plan, build, and manage physical systems is to determine the kind of outcomes they want machine-learning algorithms to pursue.

If technology is a solution, what are the digital age challenges AI must help solve?

No city is the same. Across the United States, some places face the strain of swelling populations, often due to a mix of new job opportunities or attractive weather. Many older cities face the dim prospect of little to negative population growth. The majority of cities find themselves somewhere in the middle. Yet no matter the growth trajectory, local leadership must design interventions that increase the quality of life for those who do live there, help local businesses grow and attract new ones, and promote environmental resilience.

AI can help achieve those shared outcomes. But to do so, AI must put shared challenges at the core of each intervention’s design. The following categories delineate some of the most pressing challenges facing cities of all kinds.

Climate change and urban resilience

There is no greater existential threat to our communities—from the smallest farming villages to megacities—than climate-related impacts. As the natural environment continues to transform, every place must prepare for the impacts of climate insecurity. That includes managing the most extreme events, including the devastating flooding, property destruction, and human misery delivered by Hurricanes Katrina, Sandy, and Harvey. Places must also prepare for more consistent climate patterns that bring more sustained threats, whether they be rising sea levels in Florida, flooding in the Midwest, or extreme heat and water scarcity in the Mountain West. Communities simply did not design their decades-old built environment systems, from wastewater infrastructure to land use controls, to manage these kinds of climate realities.

Communities will need a new agenda to prioritize environmental resilience across multiple dimensions. Physical designs will need to consider a broader range of climate scenarios. Financing models will need to explicitly recognize the costs climate change could inflict and the benefits of delivering long-term environmental resilience. Land use policies will need to be more forceful around what land is suitable for human development and what land should be left undisturbed. Communities will even need a modernized workforce to undertake resilience-focused activities.

Growth and attraction of tradable industries

Trade is the lifeblood of urban economies. Selling goods and services beyond a city and metropolitan area’s borders brings fresh income to a community, allowing new income to cycle through the rest of the economy—whether it be local restaurants or local schools. Business profits are also essential to reinvest in new products and people. If done successfully, communities build an industrial ecosystem that creates long-term viability; if trade dries up, entire communities can disappear.

To stay competitive in today’s global marketplace, American businesses must be able to develop products that leverage the capabilities of the newest technological platforms—and that includes a prominent role for local governments. Public infrastructure networks should promote efficient and equitable movement of goods, data, and people. Education and workforce systems should support a pipeline of talent, including the promotion of non-routine skills that can help manage the rise of automation. Laws should help investment capital flow into a community to invest in entrepreneurs and fixed assets. Likewise, laws should promote free-flowing data while protecting consumer privacy.

Rising income and wealth inequality

While many United States macroeconomic indicators point to strong long-term growth—including GDP levels, total household wealth, even average incomes—the effects are not equally felt among households. In inflation-adjusted terms, median household income in the U.S. barely grew between 1999 and 2017. The Federal Reserve’s research team found that only 40% of households have enough money saved to manage an unexpected cost of $400 or more. There are persistent gaps in wage levels by race. Even intergenerational mobility is down, including alarming limitations related to the neighborhood where someone grows up. Urban economies that do not work for all people—that do not create truly shared pathways to prosperity—are not places reaching their full economic potential.

Urban economies that do not work for all people—that do not create truly shared pathways to prosperity—are not places reaching their full economic potential.

Cities and their public, private, and civic leadership must address economic inequality head-on. Beyond facing earnings issues related to automation, it also includes a significant set of targets related to the built environment. Housing should be affordable for all people. The same applies to essential infrastructure services like local transportation, water, energy, and broadband. Government services should promote access to public services, including digital skills training, digital financial services, and auto-enrolled programming tied to identification cards. And since many built environment projects can take years if not decades to reach full maturity—think large housing efforts or a new energy grid—it’s essential to codify these shared values early.

Outdated governance models

Political and economic geography do not align in the United States. We may colloquially use the term “city” to reference local economies, but those economies now extend far beyond the municipal borders of central cities and counties. Instead, local economies touch an expansive set of cities, towns, villages, counties, and regional governments to manage the built environment. With such a fragmented governance design, it can be difficult to set common objectives across an entire metropolitan area. For example, American metro areas have struggled to implement road pricing policies due to tension between suburban and central city interests. Similarly, certain government units tend to have more preparedness for a digital future than their metropolitan peers, whether it’s the budget to hire data scientists or a willingness to experiment with new products and services.

Addressing climate instability, industrial competitiveness, and household inequality requires coordinated action, much of it multidisciplinary in nature. Metropolitan areas need a governance platform that promotes collaboration between different local governments and reduces the friction caused by parochialism.

Fiscal constraint and risk tolerance

Every local government confronts fiscal capacity issues. No matter local population and economic growth rates, local governments must be responsive to current revenues, future revenue projections, state and federal support levels, and what private capital markets will bear in terms of borrowing. As a result, limited fiscal resources can reduce local leadership’s tolerance to invest in future technologies, many of which are unproven and may not deliver positive results. All told, this creates friction around investing in future technology, which typically requires higher up-front spending to generate long-term operational savings.

Local governments need ways to generate confidence in digital technology services, including AI. This can include new financing models that spread risk among technology developers, private equity, and government purchasers. Civic programs to support information sharing among local governments, some of which already exist, are essential.

Addressing AI-related challenges within the urban context

While AI and machine learning are uniquely well-suited to help manage the challenges facing cities and metropolitan areas, AI is not a panacea. There is a unique set of challenges related to the design and deployment of AI systems, many of which already appear in cities across the United States. To ensure smart city programs and their related AI interventions deliver economic, social, and environmental value while protecting individual privacy, these challenges must be faced head-on.

While AI and machine learning are uniquely well-suited to help manage the challenges facing cities and metropolitan areas, AI is not a panacea.

What ties each of these AI-related challenges together is the idea of urban ethics. Developing AI services and their related algorithms will require local governments—as well as their peers in state and federal government—to codify a set of shared moral principles. Sometimes those will be specific to a given place, sometimes they should be national standards. But in every instance, we as a society must be explicit and purposeful about our morals and use them to inform both AI algorithms themselves and the management principles that govern the algorithms.

Redundancy and security

Today, a city power outage effectively means modern life grinds to a halt. Buildings without backup generators will see their HVAC systems shut down, lights can’t turn on, computers turn off, elevators won’t work, even security systems could become inoperable. The same applies to telecommunications networks if they don’t have backup generators. But much continues working. Cars, bikes, and non-electrified transit can still operate—and humans can navigate streets without traffic lights. If you have a key to a house or building, it opens.

This will not be the same situation in a city governed by AI. Autonomous vehicles will switch into manual mode if there’s no centralized computing to govern their actions, but some fleet-based vehicles may not allow a passenger to take over (to say nothing of all the empty vehicles that will quickly fill the side of roads). AI-informed water infrastructure would also switch into manual mode, potentially requiring extra workers to manage systems. Other essential services, like health care, could face the same challenges in a power outage. As AI continues to grow in importance, electricity and staffing redundancy becomes even more important.

But it’s the very threat of outright service failure that makes security especially important in a digitalized city. Recent stories of cyberattacks impacting entire municipal operations, including Baltimore and Atlanta, show how information security is essential to keeping cities operational in a digital, connected era. Moreover, it reveals a new kind of global security threat from global adversaries.

Privacy issues

The emergence of digitally connected technologies has invigorated a global debate around information privacy. As it becomes possible to know every single physical movement a person makes, to know every website they visit and every web service they use, to monitor the inner-workings of their homes and workplaces, enormous questions emerge around who should own the data, how the government should regulate data collection and use, and what are the accepted standards to anonymize and encrypt the data.

These tensions are already playing out in public. Location-tracking systems via our smartphones and vehicles make it possible to know frighteningly personal information—including the ability to triangulate a person’s identity with relatively little data. But it’s also impossible to enable location-specific services, from cellular calls to ride-sharing services, without the data trail. Likewise, accurate movement data can enable local governments to make better informed urban planning decisions, from where to put a ride-share pickup spot to where to promote taller buildings.

With industry power closely tied to controlling personal information, and with even more opportunities for personal information to leak, we must strike the right balance between making data and algorithms open to the public and enforcing personal protections. Democratic societies may initially reject surveillance state applications like those found in China, but one only has to look to London to find a city awash in AI-assisted video monitoring. Codifying legal ethics is the only way to protect the right amount of privacy in the digital age.

Algorithmic bias

All AI systems rely on algorithms, which are effectively a set of instructions on how to organize and manage data. The issue is that algorithms themselves can formalize biases, whether via the individuals who write the algorithms or biased data the algorithms compute against. And once biases are written into code, the use of layered code within algorithms can make them even harder to locate over time. As a result, it’s essential that cities have a set of bias detection strategies to protect against AI-created inequities.

We can already see algorithmic bias playing-out in public view. Academic research by Inioluwa Deborah Raji and Joy Buolamwini found Amazon’s facial recognition software biased against individuals with darker skin tones, leading to protests from other researchers. In Chicago, a policing “heat list” system for identifying at-risk individuals failed to significantly reduce violent crime and also increased police harassment complaints by the very populations it was meant to protect.

These instances are only likely to increase as more AI systems come online and more skilled onlookers develop ways to measure for systemic bias. For example, concerned residents could check whether urban services like snow removal are more responsive to complaints from advantaged communities. Such criticism is another reason to promote open algorithms. Allowing public access to an algorithm’s underlying code makes it easier to review for bias, whether one can read the code itself or you would rely on an intermediary to explain how the code works. This is a core argument within the Obama administration’s National Artificial Intelligence Research and Development Strategic Plan.

Where urban AI goes next

We don’t need to guess when AI systems will appear in our cities—they’re already here and growing in number.

We don’t need to guess when AI systems will appear in our cities—they’re already here and growing in number. In Montreal, the regional public transportation agency and Transit, the maker of a well-subscribed smartphone application, are using machine learning to better predict future bus arrivals. In New Orleans, the city’s Office of Performance and Accountability used machine learning and public data to predict where fire-related deaths were most likely to occur, helping the fire department better target operations. In New York City and Washington, both cities use a system called ShotSpotter and public data to better locate and assess gun fire. Some cities are even creating exact, digital replicas of their cities—known as digital twins—to create an environment for AI to model future interventions.

As AI services continue to grow in number, it’s also clear that complementary policies will need to develop in tandem. The open-source movement will continue to promote open data availability and shared standards for organization and data analysis, but debates will be had over what data should stay in private hands. Cities, states, and national governments will continue to debate the appropriate amount of personal privacy in a digitized world, as is the ongoing case with the Sidewalk Toronto project. We’re likely to see more cyberattacks against public infrastructure systems as cities continue their digital security build-out.

Continued experimentation with pilot AI projects and complementary policies are essential to build digital cities that benefit all people. But to deliver such shared prosperity, AI is only a secondary intervention. The first step is the same as it always was, no matter the technological era: Local leadership, from civic groups to elected officials to the business community, must collaborate to codify the shared challenges cities want technology to address. It’s only with a common sense of purpose that cities can tap AI’s full promise.

Lara Fishbane provided invaluable research and writing assistance to this brief.

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