The United States government is proposing to allocate $30.3 million over five years toward developing an advanced lie detection system, according to budget documents. The initiative, dubbed «Polygraph+» or «Polygraph Next,» aims to modernize credibility assessment through artificial intelligence and machine learning algorithms, along with a technique known as «standoff sensing»—the capacity to capture physiological data without physical contact with the subject.
Budget records indicate the project’s goal is to «modernize federal polygraph and credibility assessment technologies» to enhance their precision and dependability. However, this endeavor may simply represent another chapter in a lengthy history of unsuccessful attempts to employ technology for deception detection. «It’s a misguided effort to reduce the complex to something that is tangible,» remarks Kyri Kotsoglou, a legal researcher at Northumbria University in the UK who examines polygraph applications within the justice system.
This development occurs amid significant internal tension at the department. Under Defense Secretary Pete Hegseth, the Pentagon has increasingly relied on polygraph examinations in efforts to identify sources of purported media leaks. In September, the *New York Times* reported that personnel on the Joint Staff underwent polygraph testing following news stories about the reduction of US weapons stockpiles during the conflict with Iran.
The Polygraph+ program would be overseen by the Defense Counterintelligence and Security Agency (DCSA), which handles background investigations for the federal government. Per the budget proposal—which Congress has not yet sanctioned—the new technology would support vetting of potential hires and «insider threat detection.» Specific technologies remain unspecified, and the DCSA did not provide responses to inquiries for additional details.
Other Pentagon initiatives may offer hints. In 2023, the department’s Defense Innovation Unit (DIU) conducted an open call for companies with products suitable for deception detection.
It chose two firms: Presage Technologies, which asserts it can gauge heart rate and respiration using ordinary cameras, and Altec Research, a medical sensor company expanding into contactless sensing methods. A video of Altec’s prototype technology published by the DIU demonstrates it monitoring head motion, facial skin temperature, and pore activity. Neither Presage Technologies nor Altec Research responded to requests for comment. The DIU declined to provide a statement.
Current lie detection methods have seen minimal evolution since the polygraph’s introduction. Examiners depend on blood pressure, pulse, respiration, and perspiration readings to assess truthfulness. They evaluate respondents’ veracity by comparing physiological reactions to control questions such as «Is the sky blue?» against relevant questions like «Have you ever committed a crime?»
The federal government administers tens of thousands of these tests annually during employee screening, yet the technology’s reliability has faced repeated scrutiny—and its findings are seldom accepted in court. In 1983, Congress’s Office of Technology Assessment concluded there was scant evidence backing the polygraph’s use for employee screening, and in 2003, the US National Research Council (NRC) described the evidence for its effectiveness as «weak at best.»
Research indicates humans can detect lies only slightly better than chance without technical aids. The American Polygraph Association claims the polygraph achieves 80% to 94% accuracy. However, the 2003 NRC report noted that even a screening test with such accuracy could produce numerous errors. The Department of Defense employs 2.8 million individuals; an imperfect system at that scale might wrongly accuse tens of thousands.
Additional concerns exist. Polygraph interpretations are frequently subjective, and individuals from minority backgrounds are more likely to be classified as deceptive. Moreover, with training, interviewees can learn various countermeasures to defeat the test—for instance, by artificially amplifying their physiological responses to control questions by stepping on a concealed pin.
«If you know how it works, you can beat it,» states Sophie van der Zee, an associate professor specializing in deception research at Erasmus University in Rotterdam. She notes the machine’s primary impact is deterrence—frequently, subjects confess before testing starts. «But that only works if people think a polygraph works,» she observes.
Numerous novel lie detection approaches have been explored over time, spanning thermal cameras, pupil trackers, and brain imaging. None have produced dependable outcomes beyond laboratory settings. The core challenge is the absence of a universal, consistent indicator of deception. «There is still no Pinocchio’s nose,» van der Zee says.
AI might theoretically enhance detection by identifying data patterns beyond human examiners’ capabilities. AI algorithms are also better suited for «multi-modal» deception detection, which integrates various measurements into a composite deception «score» that is more difficult to manipulate. According to van der Zee, three underlying factors drive lie detection efforts: physiological stress, cognitive load, and deliberate attempts to hide deception. Existing polygraph technology addresses only 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 explains. This idea isn’t novel—in the 2000s, researchers at Manchester Metropolitan University in the UK created a system called Silent Talker that derived a deception score from video footage. It was subsequently integrated into iBorderCtrl, an EU-funded pilot. In the US, the AVATAR project merged eye tracking, voice analysis, and body movement detection into a border-crossing tool. All these initiatives have quietly disappeared.
Kotsoglou argues that merging AI with the polygraph is «the worst of both worlds» since it introduces uncertainty atop an already invalid method. Even if AI or machine learning uncovers new patterns in physiological data, it cannot reliably connect them to lying due to the lack of a true baseline.
«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 law professor who has co-authored with Kotsoglou on polygraph use in justice. She worries that emerging lie detection methods will, like the polygraph, serve more as psychological crutches than scientific instruments.
«It seems very much a response to the concern of the current administration to leaks and perceived lack of loyalty,» Oswald says. «[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.