AI for more sustainable cities

AI is hard to define, both because it covers a wide range of offerings and because it is essentially a moving target—constantly learning and evolving is intrinsic to its purpose. At the most basic level, AI leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind. Essentially, it turns human-defined goals into mathematical ones.

AI has long been touted as the technological tool that possesses both the greatest potential for advancement and the greatest level of risk. The leading risk regards data privacy. Smart cities rely on data provided by citizens to function, but if that data were to be accessed by a party with sinister intentions, problems would arise. The state, too, can potentially misuse AI, harvesting and exploiting data in ways that infringe on citizens’ privacy. More overtly, if a hacker were to gain access to smart traffic control systems, they could cause mayhem.

So how can smart cities be sure they’re using AI correctly, advancing the sustainability agenda in a responsible and equitable way?

TAI has the potential to impact nearly every aspect of the smart city. It bolsters security with incident detection and intelligent CCTV. It increases efficiency with traffic and parking management on roads, as well as automated updates and tracking options on public transportation. It monitors air quality, manages waste, analyzes energy usage—and that barely scratches the surface.

To do all this, AI relies on data. Processing data, recognizing patterns, and devising solutions based on those patterns—even predicting potential future difficulties that can be mitigated—are AI’s fundamental pillars. As such, any city that recognizes and wants to capitalize on AI’s potential must ensure that its urban services are collecting data as effectively as possible. That’s where connected lighting can play an important part.

Connected city street lighting can serve as a valuable platform for a secure, distributed sensor network that can collect the necessary data AI requires, even on a citywide scale. Systems like Interact provide the best lighting experiences while also acting as the framework for enabling a whole host of smart city applications.

Sensors in streetlights can monitor air quality and temperature. They can also detect sounds—such as gunshots or smashed windows— and then alert first responders in real time, reducing crime and helping citizens feel more secure. Additionally, they can be used to streamline traffic management by offering real-time traffic information and smart parking. This information can be shared with city traffic managers or directly with drivers via an app.

Connected lighting is pivotal from a sustainability standpoint too. If every business and city around the world converted all their conventional light points to connected LED, it would reduce annual carbon emissions by more than 553 million tons of CO₂. That’s equivalent to the amount of carbon that 25 billion trees could sequester in a year.

Smart cities that are serious about sustainability need to consider the benefits of connected lighting both as an enabler of AI capabilities and a sustainable solution in its own right.

AI will be key to addressing social, economic, and ecological challenges at a global scale. However, its limitations must also be acknowledged.

AI & Cities: Risks, Applications and Governance, a report published by the United Nations Human Settlements Programme (UN-Habitat) in collaboration with the Mila-Quebec Artificial Intelligence Institute, points to some of these risks. “In order for an algorithm to reason, it must gain an understanding of its environment,” the authors write. “This understanding is provided by the data. Whatever assumptions and biases are represented in the dataset will be reproduced in how the algorithm reasons and what output it produces.”

As noted earlier, AI turns human-defined goals into mathematical ones. But if the human-defined goals are based on existing preconceptions, then the data will end up reinforcing those assumptions.

AI also falls short in evaluating its own performance. As the UN-Habitat report notes, “While it may be tempting to see algorithms as neutral ’thinkers,’ they are neither neutral nor thinkers.” AI has no grasp of wider context, and so can only produce results based on its pre-defined optimization goals, which may be at odds with wider considerations— or worse, serve a misleading agenda.

AI systems are mathematical and cannot integrate nuance. This means AI can sometimes end up excluding or underrepresenting subjective, qualitative information from its findings.

There are ways to mitigate the risks associated with artificial intelligence’s shortcomings. Key among them are governance and accountability.

Accountability ensures that some entity is always held responsible—and more importantly, always feels responsible—for AI’s impact. Algorithmic systems evolve, often unpredictably. A change in purpose will change their effects. Proper accountability can help negate mission creep, where technologies are intentionally repurposed for surveillance and other extraneous purposes. It can also help ensure that bad faith actors aren’t able to willfully mishandle AI’s goals, or to repurpose them over time.

AI governance refers to the sum of AI regulations, ethics, norms, administrative procedures, and social processes. Governance helps ensure AI is used in an inclusive and equitable way, and that preconceptions or lack of awareness in the early stages don’t allow AI findings to widen the digital divide or exacerbate existing inequalities. Governance lets local authorities evaluate the opportunities and risks afforded by AI, so they can then apply it in accordance with local context.

Consulting citizens and communities is vital, too. The public is every city’s primary stakeholder; they need to have a voice in how a tool as powerful as AI is being used in their community. This helps ensure AI is fixing local problems, not aggravating them.

AI’s capacity for generating and expanding the possibilities of smart cities is considerable, especially in advancing sustainable causes. There are risks, but also ways around them. Conscientious decision-making that factors in local communities and consults local authorities will help ensure cities get the best from AI.