The U.S. Department of Defense is requesting $30.3 million over five years for a program called «Polygraph+»—an initiative aimed at modernizing traditional polygraph technology with artificial intelligence and machine learning capabilities. The project, first reported by [source], would also explore «standoff sensing,» a technique that measures physiological responses without physical contact with the subject.
According to budget documents not yet approved by Congress, the Defense Counterintelligence and Security Agency (DCSA) would oversee the program, which aims to «modernize federal polygraph and credibility assessment technologies» for improved accuracy and reliability. The technology would be deployed for employee vetting and «insider threat detection.»
However, critics argue this represents yet another attempt to solve an inherently unsolvable problem. «It’s a misguided effort to reduce the complex to something that is tangible,» says Kyri Kotsoglou, a legal scholar at Northumbria University in the UK who researches polygraph use in justice systems.
**A Troubled History**
Polygraph technology has faced scrutiny since its invention in the 1920s. The basic approach remains largely unchanged: examiners monitor blood pressure, pulse, respiration, and perspiration to assess truthfulness by comparing physiological responses to baseline questions («Is the sky blue?») versus target questions («Have you ever committed a crime?»).
The federal government administers tens of thousands of these tests annually for employee screening, yet their reliability has been repeatedly questioned, and results are rarely admissible in court. In 1983, Congress’s Office of Technology Assessment found limited evidence supporting polygraph use for employee screening. Two decades later, a 2003 National Research Council report characterized the evidence for its effectiveness as «weak at best.»
Research suggests humans detect lies correctly just over half the time without technical assistance. While the American Polygraph Association claims 80% to 94% accuracy, the NRC noted that even such accuracy rates could produce significant errors when applied across the Defense Department’s 2.8 million employees—potentially resulting in tens of thousands of false accusations.
Additional concerns include subjective interpretations that can disadvantage minority groups, and the fact that trained individuals can employ countermeasures to beat the test—such as artificially elevating baseline responses by stepping on a hidden pin.
«If you know how it works, you can beat it,» says Sophie van der Zee, an associate professor studying deception at Erasmus University in Rotterdam. She notes the machine’s primary effect is deterrence—subjects often confess before testing begins. «But that only works if people think a polygraph works.»
**The AI Promise and Its Limits**
Proponents suggest AI could identify patterns in physiological data that human examiners might miss. Machine learning could also enable «multi-modal» deception detection, combining multiple measurements into a comprehensive deception score that’s harder to manipulate.
Van der Zee explains that lie detection attempts to capture three underlying phenomena: physiological stress, cognitive load, and conscious efforts to conceal deception. Current polygraph technology addresses only one of these. «The more you can have combined methods that approach it from these three different angles, the more successful you will be,» she says.
Previous attempts at multi-modal detection—including the UK’s Silent Talker system, the EU-funded iBorderCtrl project, and the U.S. AVATAR program for border crossings—have all quietly discontinued.
**The Fundamental Problem**
Kotsoglou describes combining AI with polygraph technology as «the worst of both worlds,» adding uncertainty to an already invalid tool. Even if AI detects previously unseen patterns in physiological data, it cannot reliably link them to deception because no ground truth exists.
«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 research with Kotsoglou on polygraph use in justice systems.
The core issue remains unresolved: there is no universal physiological signal that reliably indicates lying across all people and situations. «There is still no Pinocchio’s nose,» van der Zee notes.
**Intimidation Versus Investigation**
The Pentagon’s increased reliance on polygraph testing comes amid heightened internal tensions. Under Defense Secretary Pete Hegseth, the department has increasingly used polygraph tests to identify sources of alleged press leaks. In September, the New York Times reported that Joint Staff members underwent polygraph testing following news coverage about depleted U.S. weapons stockpiles in the Iran conflict.
Oswald fears new lie detection methods will follow the polygraph’s pattern—serving more as psychological pressure than scientific instrument.
«It seems very much a response to the concern of the current administration to leaks and perceived lack of loyalty,» she 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.»
The DCSA did not respond to requests for additional information about which specific technologies Polygraph+ would employ. However, past Pentagon efforts offer clues. In 2023, the Defense Innovation Unit solicited companies with deception detection products, ultimately selecting Presage Technologies—which claims to measure heart rate and breathing via standard cameras—and Altec Research, a medical sensor company developing non-contact sensing technology. A DIU-released video shows Altec’s prototype tracking head movement, facial skin temperature, and pore activity. Neither company responded to comment requests, and the DIU declined to comment.