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The All-Seeing Eyes of New York’s 15,000 Surveillance Cameras

The All-Seeing Eyes of New York’s 15,000 Surveillance Cameras

A new video from human rights organization Amnesty International maps the locations of more than 15,000 cameras used by the New York Police Department, both for routine surveillance and in facial-recognition searches. A 3D model shows the 200-meter range of a camera, part of a sweeping dragnet capturing the unwitting movements of nearly half of the city’s residents, putting them at risk for misidentification. The group says it is the first to map the locations of that many cameras in the city.

Amnesty International and a team of volunteer researchers mapped cameras that can feed NYPD’s much criticized facial-recognition systems in three of the city’s five boroughs—Manhattan, Brooklyn, and the Bronx—finding 15,280 in total. Brooklyn is the most surveilled, with over 8,000 cameras.

A video by Amnesty International shows how New York City surveillance cameras work.

“You are never anonymous,” says Matt Mahmoudi, the AI researcher leading the project. The NYPD has used the cameras in almost 22,000 facial-recognition searches since 2017, according to NYPD documents obtained by the Surveillance Technology Oversight Project, a New York privacy group.

“Whether you’re attending a protest, walking to a particular neighborhood, or even just grocery shopping, your face can be tracked by facial-recognition technology using imagery from thousands of camera points across New York,” Mahmoudi says.

The cameras are often placed on top of buildings, on street lights, and at intersections. The city itself owns thousands of cameras; in addition, private businesses and homeowners often grant access to police.

Police can compare faces captured by these cameras to criminal databases to search for potential suspects. Earlier this year, the NYPD was required to disclose the details of its facial-recognition systems for public comment. But those disclosures didn’t include the number or location of cameras, or any details of how long data is retained or with whom data is shared.

The Amnesty International team found that the cameras are often clustered in majority nonwhite neighborhoods. NYC’s most surveilled neighborhood is East New York, Brooklyn, where the group found 577 cameras in less than 2 square miles. More than 90 percent of East New York’s residents are nonwhite, according to city data.

Facial-recognition systems often perform less accurately on darker-skinned people than lighter-skinned people. In 2016, Georgetown University researchers found that police departments across the country used facial recognition to identify nonwhite potential suspects more than their white counterparts.

In a statement, an NYPD spokesperson said the department never arrests anyone “solely on the basis of a facial-recognition match,” and only uses the tool to investigate “a suspect or suspects related to the investigation of a particular crime.”
 
“Where images are captured at or near a specific crime, comparison of the image of a suspect can be made against a database that includes only mug shots legally held in law enforcement records based on prior arrests,” the statement reads.

Amnesty International is releasing the map and accompanying videos as part of its #BantheScan campaign urging city officials to ban police use of the tool ahead of the city’s mayoral primary later this month. In May, Vice asked mayoral candidates if they’d support a ban on facial recognition. While most didn’t respond to the inquiry, candidate Dianne Morales told the publication she supported a ban, while candidates Shaun Donovan and Andrew Yang suggested auditing for disparate impact before deciding on any regulation.


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Dumbed Down AI Rhetoric Harms Everyone

Dumbed Down AI Rhetoric Harms Everyone

When the European Union Commission released its regulatory proposal on artificial intelligence last month, much of the US policy community celebrated. Their praise was at least partly grounded in truth: The world’s most powerful democratic states haven’t sufficiently regulated AI and other emerging tech, and the document marked something of a step forward. Mostly, though, the proposal and responses to it underscore democracies’ confusing rhetoric on AI.

Over the past decade, high-level stated goals about regulating AI have often conflicted with the specifics of regulatory proposals, and what end-states should look like aren’t well-articulated in either case. Coherent and meaningful progress on developing internationally attractive democratic AI regulation, even as that may vary from country to country, begins with resolving the discourse’s many contradictions and unsubtle characterizations.

The EU Commission has touted its proposal as an AI regulation landmark. Executive vice president Margrethe Vestager said upon its release, “We think that this is urgent. We are the first on this planet to suggest this legal framework.” Thierry Breton, another commissioner, said the proposals “aim to strengthen Europe’s position as a global hub of excellence in AI from the lab to the market, ensure that AI in Europe respects our values and rules, and harness the potential of AI for industrial use.”

This is certainly better than many national governments, especially the US, stagnating on rules of the road for the companies, government agencies, and other institutions. AI is already widely used in the EU despite minimal oversight and accountability, whether for surveillance in Athens or operating buses in Málaga, Spain.

But to cast the EU’s regulation as “leading” simply because it’s first only masks the proposal’s many issues. This kind of rhetorical leap is one of the first challenges at hand with democratic AI strategy.

Of the many “specifics” in the 108-page proposal, its approach to regulating facial recognition is especially consequential. “The use of AI systems for ‘real-time’ remote biometric identification of natural persons in publicly accessible spaces for the purpose of law enforcement,” it reads, “is considered particularly intrusive in the rights and freedoms of the concerned persons,” as it can affect private life, “evoke a feeling of constant surveillance,” and “indirectly dissuade the exercise of the freedom of assembly and other fundamental rights.” At first glance, these words may signal alignment with the concerns of many activists and technology ethicists on the harms facial recognition can inflict on marginalized communities and grave mass-surveillance risks.

The commission then states, “The use of those systems for the purpose of law enforcement should therefore be prohibited.” However, it would allow exceptions in “three exhaustively listed and narrowly defined situations.” This is where the loopholes come into play.

The exceptions include situations that “involve the search for potential victims of crime, including missing children; certain threats to the life or physical safety of natural persons or of a terrorist attack; and the detection, localization, identification or prosecution of perpetrators or suspects of the criminal offenses.” This language, for all that the scenarios are described as “narrowly defined,” offers myriad justifications for law enforcement to deploy facial recognition as it wishes. Permitting its use in the “identification” of “perpetrators or suspects” of criminal offenses, for example, would allow precisely the kind of discriminatory uses of often racist and sexist facial-recognition algorithms that activists have long warned about.

The EU’s privacy watchdog, the European Data Protection Supervisor, quickly pounced on this. “A stricter approach is necessary given that remote biometric identification, where AI may contribute to unprecedented developments, presents extremely high risks of deep and non-democratic intrusion into individuals’ private lives,” the EDPS statement read. Sarah Chander from the nonprofit organization European Digital Rights described the proposal to the Verge as “a veneer of fundamental rights protection.” Others have noted how these exceptions mirror legislation in the US that on the surface appears to restrict facial recognition use but in fact has many broad carve-outs.

Humans Need to Create Interspecies Money to Save the Planet

Humans Need to Create Interspecies Money to Save the Planet

The greatest failure of the digital age is how far removed it is from nature. The microchip has no circadian rhythm, nor has the computer breath. The network is incorporeal. This may represent an existential risk for life on Earth. I believe we have to make a decision: Succumb to pushing more of our brain time and economy into unnatural online constructs, or build the digital anew in a way that is rooted in nature.

Nature is excessive, baroque. Its song is not ours alone. We share this planet with 8 million nonhuman species, yet we scarcely think of how they move through the world. There is no way for wild animals, trees, or other species to make themselves known to us online or to express their preferences to us. The only value most of them have is the sum value of their processed body parts. Those that are not eaten are forgotten, or perhaps never remembered: Only 2 million of them are recorded by science.

This decade will be the most destructive for nonhuman life in recorded history. It could also be the most regenerative. Nonhuman life-forms may soon gain some agency in the world. I propose the invention of an Interspecies Money. I’m not talking about Dogecoin, the meme of a Shiba Inu dog that’s become a $64 billion cryptocurrency (as of today). I’m talking about a digital currency that could allow several hundred billion dollars to be held by other beings simply on account of being themselves and no other and being alive in the world. It is possible they will be able to spend and invest this digital currency to improve their lives. And because the services they ask for—recognition, security, room to grow, nutrition, even veterinary care—will often be provided by poor communities in the tropics, human lives will also be improved.

Money needs to cross the species divide. Whoa, I know. King Julien with a credit card. Flower grenades into the meaning of life. Bear with me. If money, as some economic theorists suggest, is a form of memory, it is obvious that nonhuman species are unseen by the market economy because no money has ever been assigned by them. In order to preserve the survival of some species it is necessary in some situations, usually when they are in direct competition with humans, to give them economic advantage. An orchid, a baobab tree, a dugong, an orangutan, even at some future point the trace lines of a mycelial network—all of these should hold money.

We have the technology to start building Interspecies Money now. Indeed, it sometimes seems to me that the living system (Gaia or otherwise) is in fact producing the tools needed to protect complex life at precisely the moment it is most needed: fintech solutions in mobile money, digital wallets, and cryptocurrencies, which have shown that it is possible to address micropayments accurately and cheaply; cloud computing firms, which have demonstrated that large amounts of data can be stored and processed, even in countries that favor data sovereignty; hardware, which has become smarter and cheaper. Single-board computers (Raspberry Pis), camera traps, microphones, and other cheap sensors, energy solutions in solar arrays and batteries, internet connectivity, flying and ground robots, low-orbit satellite systems, and the pervasiveness of smartphones make it plausible to build a verification system in the wild that is trusted by the markets.

The first requirement of Interspecies Money is to provide a digital identity of an individual animal, or a herd, or a type (depending on size, population dynamics, and other characteristics of the organisms). This can be done through many methods. Birds may be identified by sound, insects by genetics, trees by probability. For most wild animals it will be done by sight. Some may be observed constantly, others only glimpsed. For instance, the digital identity of rare Hirola antelopes in Kenya and Somalia, of which there are only 500 in existence, will be minted from images gathered on mobile phones, camera traps, and drones by community rangers. The identity serves as a digital twin, which in legal and practical terms holds the money and releases it based on the services the life-form requires.