The U.S. Department of Defense is requesting $30.3 million over five years to develop an upgraded lie detection system, according to budget documents. The initiative, named «Polygraph+» or «Polygraph Next,» aims to modernize federal polygraph and credibility assessment technologies through artificial intelligence and machine learning scoring algorithms, along with a technique known as «standoff sensing»—the ability to capture physiological readings without physical contact with the subject.
The project, first reported by [source], would be managed by the Defense Counterintelligence and Security Agency (DCSA), which handles background checks for the federal government. According to the budget proposal—which Congress has not yet approved—the technology would be deployed for vetting job applicants and «insider threat detection.» Specific technologies remain unspecified, and the DCSA did not respond to inquiries for additional details.
This effort arrives amid heightened tensions within the Defense Department. Under Defense Secretary Pete Hegseth, the Pentagon has increasingly relied on polygraph examinations in attempts to identify sources of alleged media leaks. In September, the New York Times reported that staff members on the Joint Staff underwent polygraph testing following news coverage about depleted U.S. weapons stockpiles during the conflict with Iran.
Previous Pentagon initiatives may offer hints about the direction of Polygraph+. In 2023, the Defense Innovation Unit (DIU) conducted an open solicitation seeking companies with deception detection products. Two companies were selected: Presage Technologies, which claims its standard cameras can measure heart rate and breathing rate, and Altec Research, a medical sensor company expanding into non-contact sensing. A video of Altec’s prototype released by the DIU demonstrates tracking of head movement, facial skin temperature, and pore activity. Neither company responded to requests for comment, and the DIU declined to comment.
Modern lie detection technology has remained largely unchanged since the polygraph was invented. Examiners assess blood pressure, pulse, respiration, and perspiration to evaluate truthfulness, comparing physiological responses between baseline questions («Is the sky blue?») and target questions («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 persistent challenges, and results are seldom admissible in court. In 1983, Congress’s Office of Technology Assessment concluded there was minimal evidence supporting polygraph use for employee screening. Two decades later, in 2003, the U.S. National Research Council (NRC) characterized evidence for its effectiveness as «weak at best.»
Research indicates humans can detect lies only slightly better than chance without technical assistance. While the American Polygraph Association claims 80% to 94% accuracy, the 2003 NRC report noted that even a screening test with such accuracy would produce numerous errors. With the Department of Defense employing 2.8 million people, an imperfect system at that scale could falsely accuse tens of thousands.
Additional concerns exist. Polygraph interpretations are frequently subjective, and individuals from minority groups face higher likelihood of being judged deceptive. Furthermore, with training, interviewees can learn countermeasures to defeat the test—for instance, artificially increasing physiological responses to baseline questions 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,» she observes.
Various alternative lie detection strategies have been attempted over the years, employing technologies from thermal cameras to pupil trackers to brain scans. None have produced reliable results outside laboratory settings. The fundamental problem: no single telltale sign of lying applies universally to everyone at all times. «There is still no Pinocchio’s nose,» van der Zee says.
AI could theoretically improve detection by identifying patterns in data that human examiners miss. AI algorithms are also better suited for «multi-modal» deception detection, which combines multiple measurements into an overall deception score that’s harder to manipulate. According to van der Zee, three underlying processes occur during lying that detection attempts to identify: physiological stress, cognitive load, and conscious efforts to conceal deception. Current 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 says. This isn’t novel—in the 2000s, researchers at Manchester Metropolitan University in the UK developed a system called [name] that generated deception scores from video footage, later incorporated into iBorderCtrl, an EU-funded pilot program. In the U.S., a project called AVATAR combined eye tracking, voice analysis, and body movement detection for border crossing applications. All these initiatives quietly disappeared.
Kotsoglou argues that merging AI with polygraph technology represents «the worst of both worlds» because it compounds uncertainty atop invalidity. Even if AI or machine learning detects previously unseen patterns in physiological data, it cannot reliably connect them to lying because no real 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 work with Kotsoglou on polygraph use in justice systems. She worries that new lie detection forms will, like the polygraph, serve more as psychological props 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.