Even at 100 Percent Accuracy, Facial Recognition Would Still Be Dangerous to Liberty

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Over the last couple of years, there has been a strong grassroots push to prohibit or limit facial recognition technology. The fact that facial recognition is extremely inaccurate, especially in identifying African Americans and other ethnicities with dark complexions, has become a central talking point in the case against the technology. But even if facial recognition was 100 percent accurate, it would still pose significant risks to privacy and would still need to be limited or banned.

Facial recognition at the state and local level feeds into the rapidly expanding national surveillance state. The federal government has been developing a massive, nationwide facial recognition system for years. The FBI rolled out a nationwide facial-recognition program in the fall of 2014, with the goal of building a giant biometric database with pictures provided by the states and corporate friends.

In 2016, the Center on Privacy and Technology at Georgetown Law released “The Perpetual Lineup,” a massive report on law enforcement use of facial recognition technology in the U.S. You can read the complete report at perpetuallineup.org. The organization conducted a year-long investigation and collected more than 15,000 pages of documents through more than 100 public records requests. The report paints a disturbing picture of intense cooperation between the federal government, and state and local law enforcement to develop a massive facial recognition database.

“Face recognition is a powerful technology that requires strict oversight. But those controls, by and large, don’t exist today,” report co-author Clare Garvie said. “With only a few exceptions, there are no laws governing police use of the technology, no standards ensuring its accuracy, and no systems checking for bias. It’s a wild west.”

A key talking point that has come front-and-center in the debate over facial recognition is its inaccuracy, particularly when reading the facial features of non-caucasian populations. During a test run by the ACLU of Northern California, facial recognition misidentified 26 members of the California legislature as people in a database of arrest photos.

This is certainly troubling, but it isn’t the best argument to use against facial recognition. At some point, the technology will undoubtedly improve and become more accurate. If accuracy serves as the primary argument against facial recognition, how do we fight its proliferation once it improves?

Even if facial recognition tech was 100 percent accurate, it would still pose serious threats to our privacy. It would still need to be limited and even banned. Relying primarily on the accuracy argument could undermine efforts to stop the proliferation of this tech down the road once government agencies adequately tweak the algorithms.

The Bigger Problem

This is not to discount the problems with accuracy. This is certainly a major concern considering policing and surveillance disproportionately target minority communities. But there are bigger issues with facial recognition that threaten everybody’s privacy, regardless of their ethnicity. In a nutshell, facial recognition empowers police, and ultimately the Feds, to track and spy on everybody without suspicion, probable cause, or warrants. It is the very definition of dragnet surveillance.

With facial recognition technology, police and other government officials have the capability to track individuals in real-time. These systems allow law enforcement agents to use video cameras and continually scan everybody who walks by. According to the report, several major police departments have expressed an interest in this type of real-time tracking. Documents revealed agencies in at least five major cities, including Los Angeles, either claimed to run real-time face recognition off of street cameras, bought technology with the capability, or expressed written interest in buying it.

In all likelihood, the federal government heavily involves itself in helping state and local agencies obtain this technology. The feds provide grant money to local law enforcement agencies for a vast array of surveillance gear, including ALPRs, stingray devices and drones. The federal government essentially encourages and funds a giant nationwide surveillance net and then taps into the information via fusion centers and the Information Sharing Environment (ISE).

Fusion centers were sold as a tool to combat terrorism, but that is not how they are being used. The ACLU pointed to a bipartisan congressional report to demonstrate the true nature of government fusion centers: “They haven’t contributed anything meaningful to counterterrorism efforts. Instead, they have largely served as police surveillance and information sharing nodes for law enforcement efforts targeting the frequent subjects of police attention: Black and brown people, immigrants, dissidents, and the poor.”

Fusion centers operate within the broader ISE. According to its website, the ISE “provides analysts, operators, and investigators with information needed to enhance national security. These analysts, operators, and investigators…have mission needs to collaborate and share information with each other and with private sector partners and our foreign allies.” In other words, ISE serves as a conduit for the sharing of information gathered without a warrant. Known ISE partners include the Office of Director of National Intelligence which oversees 17 federal agencies and organizations, including the NSA. ISE utilizes these partnerships to collect and share data on the millions of unwitting people they track.

Reports that the Berkeley Police Department in cooperation with a federal fusion center deployed cameras equipped to surveil a “free speech” rally and Antifa counterprotests provided the first solid link between the federal government and local authorities in facial recognition surveillance.

Facial recognition empowers the government to ignore basic privacy rights with impunity. It should be limited or banned – no matter how accurate it becomes.


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