The Pentagon wants $30 million to build an AI-powered lie detector
The US government wants to spend $30.3 million over the next five years on an improved form of lie detector, according to a Department of Defense budget request. The program, called “Polygraph+” or “Polygraph Next”, will focus on scoring algorithms that use artificial intelligence and machine learning and on a technique called “standoff sensing”, which…
Overview
The US government wants to spend $30.3 million over the next five years on an improved form of lie detector, according to a Department of Defense budget request. The program, called “Polygraph+” or “Polygraph Next”, will focus on scoring algorithms that use artificial intelligence and machine learning and on a technique called “standoff sensing”, which refers to the ability to take physiological readings from a subject without attaching a device to their person.
According to details of the budget document, which were first reported by Inside Defense, the project will “modernize federal polygraph and credibility assessment technologies” to improve their accuracy and reliability. But the project may be just the latest in a long line of failed attempts to use technology to detect lies. “It’s a misguided effort to reduce the complex to something that is tangible,” says Kyri Kotsoglou, a professor at Northumbria Law School in the UK who studies the use of polygraphs in the justice system.
The move comes at a time of high tension within the Department of Defense. Under Defense Secretary Pete Hegseth, the Pentagon has been increasingly turning to polygraph tests in an attempt to find the sources of alleged leaks to the press. In September, the New York Times reported that around 50 officers on the Joint Staff had been given polygraph tests after news coverage reported on depleted US weapons stockpiles in the war with Iran.
Polygraph+ will be run by the Defense Counterintelligence and Security Agency (DCSA), which conducts background checks for the federal government. According to the budget document, which has not yet been approved by Congress, the new technology will be used for vetting prospective employees, and “insider threat detection”. It is not yet clear which specific technologies will be used, and the DCSA did not respond to a request for more information.
But other efforts at the Department of Defense offer potential clues. In 2023, the agency’s Defense Innovation Unit (DIU) ran an open submission process to find companies with products that could be used for deception detection.
It selected two companies to build prototypes: Presage Technologies, which claims to be able to measure heart rate and breathing rate using standard cameras, and Altec Research, a medical sensor company now branching out into non-contact sensing technologies. A screenshot of Altec’s prototype technology released by the DIU shows that it tracks head movement, facial skin temperature, and pore activity. Presage Technologies and Altec Research did not respond to requests for comment. The DIU declined to comment.
Current polygraph technology has barely changed since the device was invented in the 1920s. Examiners rely on blood pressure, pulse, breathing, and sweat measurements to determine if someone is lying. They make judgments about the veracity of a respondent’s replies based on differences in their physiological response to baseline questions like, ‘Is the sky blue?’ and target questions like, ‘Have you ever committed a crime?’.
The federal government conducts tens of thousands of the tests a year while screening employees, but the polygraph has been repeatedly debunked—and its results are rarely admissible in court. In 1983, Congress’s Office of Technology Assessment concluded that there was very limited evidence supporting the polygraph’s use for screening employees, and in 2003, the US National Research Council (NRC) said evidence on its efficacy was “weak at best”.
Details
Research suggests humans can spot a lie just over half the time without any technical assistance. The American Polygraph Association claims the polygraph is between 80 and 94% accurate. But the 2003 NRC report pointed out that a screening test with this level of accuracy could still lead to a lot of mistakes. The DoD employs 2.8 million staff—an imperfect system applied at that scale could end up falsely accusing tens of thousands of people.
There are other issues too. Polygraph interpretations are often subjective: different examiners get wildly different results, and people from minority groups are more likely to be judged as deceptive. What’s more, with training, it’s possible for interviewees to learn a variety of countermeasures that can help beat the test; most commonly, they’llthese usually artificially heighten their body’s physiological response to baseline questions by, for example, stepping on a pin hidden in their shoe.
“If you know how it works, you can beat it,” says Sophie van der Zee, an associate professor who studies deception at Erasmus University in Rotterdam. She says the machine’s biggest effect is deterrence—often, subjects confess before it even begins. “But that only works if people think a polygraph works.”
There have been a number of attempts to build new types of lie detectors over the decades, ranging from thermal cameras to pupil trackers to brain scans. None of them have yielded reliable results outside of the lab. The problem is a structural one: there is no single telltale sign of lying that’s true for everyone all the time. “There is still no Pinocchio’s nose,” says van der Zee.
AI could theoretically improve polygraphs if it could find patterns in the data that examiners can’t. AI algorithms are also more likely to be used for “multi-modal” deception detection, which seeks to combine multiple measurements to create an overall deception “score” that is harder for people to game. There are three things happening under the surface that lie detection tries to zone in on, van der Zee says: physiological stress, cognitive load, and the conscious efforts people make to conceal the fact that they’re lying. Current polygraph technology only tackles one.
“The more you can have combined methods that approach it from these three different angles, the more successful you will be,” van der Zee says. This isn’t a new concept—in the 2000s, Manchester Metropolitan University researchers developed a system called ‘Silent Talker’ that generated a deception score from video footage, and which was later folded into iBorderCtrl, an EU-funded pilot program. In the US, a project called AVATAR combined eye-tracking, voice analysis and body movement detection for use at border crossings. All of these projects have quietly faded away.
Kotsoglou says combining AI and the polygraph is “the worst of both worlds” because it adds uncertainty on top of invalidity. Even if AI or machine learning can spot previously unseen patterns in physiological data, it won’t be able to reliably link them to lying because of a lack of ground truth.
“Even if you have all the records in the world from polygraph tests, you don’t know whether those polygraph tests are right or not,” says Marion Oswald, a professor of law who has written with Kotsoglou on the use of polygraphs in the justice system. She fears that new forms of lie detection will, like the polygraph, be used more as a psychological prop than a scientific tool.
“It seems very much a response to the concern of the current administration to leaks and perceived lack of loyalty,” says Oswald. “[Lie detection is] being used as a threat, to intimidate and force people to confess to things, as opposed to anything that’s actually getting valid information.”
Source
Originally published at www.technologyreview.com.
