It may make it more dangerous and error-prone as well.
Clearview has collected billions of photos from across websites that include Facebook, Instagram, and Twitter and uses AI to identify a particular person in images. Police and government agents have used the company’s face database to help identify suspects in photos by tying them to online profiles.
The company’s cofounder and CEO, Hoan Ton-That, tells WIRED that Clearview has now collected more than 10 billion images from across the web—more than three times as many as has been previously reported.
Ton-That says the larger pool of photos means users, most often law enforcement, are more likely to find a match when searching for someone. He also claims the larger data set makes the company’s tool more accurate.
Ton-That demonstrated the technology through a smartphone app by taking a photo of the reporter. The app produced dozens of images from numerous US and international websites, each showing the correct person in images captured over more than a decade. The allure of such a tool is obvious, but so is the potential for it to be misused.
Clearview’s actions sparked public outrage and a broader debate over expectations of privacy in an era of smartphones, social media, and AI. Critics say the company is eroding personal privacy. The ACLU sued Clearview in Illinois under a law that restricts the collection of biometric information; the company also faces class action lawsuits in New York and California. Facebook and Twitter have demanded that Clearview stop scraping their sites.
The pushback has not deterred Ton-That. He says he believes most people accept or support the idea of using facial recognition to solve crimes. “The people who are worried about it, they are very vocal, and that's a good thing, because I think over time we can address more and more of their concerns,” he says.
Some of Clearview’s new technologies may spark further debate. Ton-That says it is developing new ways for police to find a person, including “deblur” and “mask removal” tools. The first takes a blurred image and sharpens it using machine learning to envision what a clearer picture would look like; the second tries to envision the covered part of a person’s face using machine learning models that fill in missing details of an image using a best guess based on statistical patterns found in other images.
These capabilities could make Clearview’s technology more attractive but also more problematic. It remains unclear how accurately the new techniques work, but experts say they could increase the risk that a person is wrongly identified and could exacerbate biases inherent to the system.
“Without careful control over the data set and training process I would expect a plethora of unintended bias to creep in.”
ALEKSANDER MADRY, PROFESSOR, MIT
“I would expect accuracy to be quite bad, and even beyond accuracy, without careful control over the data set and training process I would expect a plethora of unintended bias to creep in,” says Aleksander Madry, a professor at MIT who specializes in machine learning. Without due care, for example, the approach might make people with certain features more likely to be wrongly identified.
Even if the technology works as promised, Madry says, the ethics of unmasking people is problematic. “Think of people who masked themselves to take part in a peaceful protest or were blurred to protect their privacy,” he says.
Ton-That says tests have found the new tools improve the accuracy of Clearview’s results. “Any enhanced images should be noted as such, and extra care taken when evaluating results that may result from an enhanced image,” he says.
Ton-That says investigators already sometimes modify images to help find a match, for instance by changing the brightness or cropping out certain details. He says deblurring an image or removing a mask may make errors more likely but says Clearview’s results are used only to generate leads that police then use in their investigations. “My intention with this technology is always to have it under human control,” Ton-That says. “When AI gets it wrong it is checked by a person.”
Clearview is far from the only company selling facial recognition technology, and law enforcement and federal agents have used the technology to search through collections of mug shots for years. NEC has developed its own system to identify people wearing masks by focusing on parts of a face that are not covered, using a separate algorithm for the task.
“My intention with this technology is always to have it under human control.”
HOAN TON-THAT, CEO, CLEARVIEW AI
Clearview’s tech potentially improves authorities’ ability to match faces to identities, by letting officers scour the web with facial recognition. The technology has been used by hundreds of police departments in the US, according to a confidential customer list acquired by BuzzFeed News; Ton-That says the company has 3,100 law enforcement and government customers. US government records list 11 federal agencies that use the technology, including the FBI, US Immigration and Customs Enforcement, and US Customs and Border Protection.
According to BuzzFeed News, several police departments that tested Clearview’s software through a free trial decided against paying to use it. The New York Police Department, which is listed among those with a Clearview license, said in a statement that it introduced new rules around use of facial recognition last year. The rules state that: “the chief of detectives or deputy commissioner of intelligence and counterterrorism may specifically authorize the comparison of an unidentified suspect's image against images other than mug shots, if there is a legitimate need to do so.”
Clearview’s technology has reportedly been tested by foreign police departments and governments as well as private companies, including Macy’s and Walmart, according to documents obtained by BuzzFeed News. The company says it is not currently pitching the technology outside of the US or to private industry. “We're focusing on the United States, because we want to get it right here,” Ton-That says. “We never want this to be abused in any way.”
Ton-That is himself a controversial figure. An investigation by the Huffington Post found ties between the entrepreneur and alt-right operatives and provocateurs, some of whom have reportedly had personal access to the Clearview app.
Ton-That says he is “not a political person at all.” The company too, is “not political,” he adds. “There's no left-wing way or right-wing way to catch a criminal. And we engage with people from all sides of the political aisle.”
Clearview is keen to focus on ways the technology has helped police. Ton-That shared examples of investigations that had benefitted from the technology, including a child abuse case and the hunt for those involved in the Capitol insurection. “A lot of times, [the police are] solving a crime that would have never been solved otherwise,” he says.
Even if that were so, some question whether it is worth sacrificing so much privacy.
“It is not a great world—especially when you think about all governments and entities in the world—if someone points a camera at you and any photo of you is forevermore linked to your identity,” says Jonathan Zittrain, director of Harvard’s Berkman Center for Internet & Society.
Zittrain says companies like Facebook should do more to protect users from aggressive scraping by outfits like Clearview. Just as companies remove metadata from images that could reveal where and when they are taken, Zittrain says tech firms could scupper facial recognition algorithms by modifying images in ways that are imperceptible to the human eye using adversarial machine learning techniques.
Jason Grosse, a Facebook spokesperson, says “Clearview AI’s actions invade people’s privacy, which is why we banned their founder from our services and sent them a legal demand to stop accessing any data, photos, or videos from our services."
Zittrain believes the greatest danger of Clearview is that it will normalize the idea of using facial recognition to routinely identify people. “And we know how this play enters its next act,” he says. “LinkedIn and Facebook continue to object and throw up enough chaff, but then they just license access and it becomes about sharing the money.”