AI surveillance

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At the Olympics, AI is watching you

“It’s the eyes of the police multiplied” —

New system foreshadows a future where there are too many CCTV cameras for humans to physically watch.

Police observe the Eiffel Tower from Trocadero ahead of the Paris 2024 Olympic Games.

Enlarge / Police observe the Eiffel Tower from Trocadero ahead of the Paris 2024 Olympic Games on July 22, 2024.

On the eve of the Olympics opening ceremony, Paris is a city swamped in security. Forty thousand barriers divide the French capital. Packs of police officers wearing stab vests patrol pretty, cobbled streets. The river Seine is out of bounds to anyone who has not already been vetted and issued a personal QR code. Khaki-clad soldiers, present since the 2015 terrorist attacks, linger near a canal-side boulangerie, wearing berets and clutching large guns to their chests.

French interior minister Gérald Darmanin has spent the past week justifying these measures as vigilance—not overkill. France is facing the “biggest security challenge any country has ever had to organize in a time of peace,” he told reporters on Tuesday. In an interview with weekly newspaper Le Journal du Dimanche, he explained that “potentially dangerous individuals” have been caught applying to work or volunteer at the Olympics, including 257 radical Islamists, 181 members of the far left, and 95 from the far right. Yesterday, he told French news broadcaster BFM that a Russian citizen had been arrested on suspicion of plotting “large scale” acts of “destabilization” during the Games.

Parisians are still grumbling about road closures and bike lanes that abruptly end without warning, while human rights groups are denouncing “unacceptable risks to fundamental rights.” For the Games, this is nothing new. Complaints about dystopian security are almost an Olympics tradition. Previous iterations have been characterized as Lockdown London, Fortress Tokyo, and the “arms race” in Rio. This time, it is the least-visible security measures that have emerged as some of the most controversial. Security measures in Paris have been turbocharged by a new type of AI, as the city enables controversial algorithms to crawl CCTV footage of transport stations looking for threats. The system was first tested in Paris back in March at two Depeche Mode concerts.

For critics and supporters alike, algorithmic oversight of CCTV footage offers a glimpse of the security systems of the future, where there is simply too much surveillance footage for human operators to physically watch. “The software is an extension of the police,” says Noémie Levain, a member of the activist group La Quadrature du Net, which opposes AI surveillance. “It’s the eyes of the police multiplied.”

Near the entrance of the Porte de Pantin metro station, surveillance cameras are bolted to the ceiling, encased in an easily overlooked gray metal box. A small sign is pinned to the wall above the bin, informing anyone willing to stop and read that they are part of a “video surveillance analysis experiment.” The company which runs the Paris metro RATP “is likely” to use “automated analysis in real time” of the CCTV images “in which you can appear,” the sign explains to the oblivious passengers rushing past. The experiment, it says, runs until March 2025.

Porte de Pantin is on the edge of the park La Villette, home to the Olympics’ Park of Nations, where fans can eat or drink in pavilions dedicated to 15 different countries. The Metro stop is also one of 46 train and metro stations where the CCTV algorithms will be deployed during the Olympics, according to an announcement by the Prefecture du Paris, a unit of the interior ministry. City representatives did not reply to WIRED’s questions on whether there are plans to use AI surveillance outside the transport network. Under a March 2023 law, algorithms are allowed to search CCTV footage in real-time for eight “events,” including crowd surges, abnormally large groups of people, abandoned objects, weapons, or a person falling to the ground.

“What we’re doing is transforming CCTV cameras into a powerful monitoring tool,” says Matthias Houllier, cofounder of Wintics, one of four French companies that won contracts to have their algorithms deployed at the Olympics. “With thousands of cameras, it’s impossible for police officers [to react to every camera].”

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London Underground is testing real-time AI surveillance tools to spot crime

tube tracking —

Computer vision system tried to detect crime, weapons, people falling, and fare dodgers.

Commuters wait on the platform as a Central Line tube train arrives at Liverpool Street London Transport Tube Station in 2023.

Thousands of people using the London Underground had their movements, behavior, and body language watched by AI surveillance software designed to see if they were committing crimes or were in unsafe situations, new documents obtained by WIRED reveal. The machine-learning software was combined with live CCTV footage to try to detect aggressive behavior and guns or knives being brandished, as well as looking for people falling onto Tube tracks or dodging fares.

From October 2022 until the end of September 2023, Transport for London (TfL), which operates the city’s Tube and bus network, tested 11 algorithms to monitor people passing through Willesden Green Tube station, in the northwest of the city. The proof of concept trial is the first time the transport body has combined AI and live video footage to generate alerts that are sent to frontline staff. More than 44,000 alerts were issued during the test, with 19,000 being delivered to station staff in real time.

Documents sent to WIRED in response to a Freedom of Information Act request detail how TfL used a wide range of computer vision algorithms to track people’s behavior while they were at the station. It is the first time the full details of the trial have been reported, and it follows TfL saying, in December, that it will expand its use of AI to detect fare dodging to more stations across the British capital.

In the trial at Willesden Green—a station that had 25,000 visitors per day before the COVID-19 pandemic—the AI system was set up to detect potential safety incidents to allow staff to help people in need, but it also targeted criminal and antisocial behavior. Three documents provided to WIRED detail how AI models were used to detect wheelchairs, prams, vaping, people accessing unauthorized areas, or putting themselves in danger by getting close to the edge of the train platforms.

The documents, which are partially redacted, also show how the AI made errors during the trial, such as flagging children who were following their parents through ticket barriers as potential fare dodgers, or not being able to tell the difference between a folding bike and a non-folding bike. Police officers also assisted the trial by holding a machete and a gun in the view of CCTV cameras, while the station was closed, to help the system better detect weapons.

Privacy experts who reviewed the documents question the accuracy of object detection algorithms. They also say it is not clear how many people knew about the trial, and warn that such surveillance systems could easily be expanded in the future to include more sophisticated detection systems or face recognition software that attempts to identify specific individuals. “While this trial did not involve facial recognition, the use of AI in a public space to identify behaviors, analyze body language, and infer protected characteristics raises many of the same scientific, ethical, legal, and societal questions raised by facial recognition technologies,” says Michael Birtwistle, associate director at the independent research institute the Ada Lovelace Institute.

In response to WIRED’s Freedom of Information request, the TfL says it used existing CCTV images, AI algorithms, and “numerous detection models” to detect patterns of behavior. “By providing station staff with insights and notifications on customer movement and behaviour they will hopefully be able to respond to any situations more quickly,” the response says. It also says the trial has provided insight into fare evasion that will “assist us in our future approaches and interventions,” and the data gathered is in line with its data policies.

In a statement sent after publication of this article, Mandy McGregor, TfL’s head of policy and community safety, says the trial results are continuing to be analyzed and adds, “there was no evidence of bias” in the data collected from the trial. During the trial, McGregor says, there were no signs in place at the station that mentioned the tests of AI surveillance tools.

“We are currently considering the design and scope of a second phase of the trial. No other decisions have been taken about expanding the use of this technology, either to further stations or adding capability.” McGregor says. “Any wider roll out of the technology beyond a pilot would be dependent on a full consultation with local communities and other relevant stakeholders, including experts in the field.”

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