The United States government is proposing to allocate $30.3 million over a five-year period for an upgraded lie detection system, according to budget documents. The initiative, named Polygraph+ (also referred to as Polygraph Next), would emphasize artificial intelligence and machine learning scoring algorithms, along with a technique known as «standoff sensing»—the capacity to capture physiological data without physical contact with the individual being tested.
Budget records indicate the project aims to «modernize federal polygraph and credibility assessment technologies» to enhance their precision and dependability. However, this endeavor may simply represent the most recent in a series of unsuccessful efforts to deploy technology for deception detection. «It’s a misguided effort to reduce the complex to something that is tangible,» remarks Kyri Kotsoglou, a legal academic at Northumbria University in the United Kingdom who researches polygraph applications within the justice system.
This development occurs amid significant internal friction at the department. Under Defense Secretary Pete Hegseth, the Pentagon has increasingly employed polygraph examinations in efforts to identify individuals allegedly leaking information to the media. 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 inventories 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 authorized, the new technology would support vetting of job candidates and «insider threat detection.» The specific technologies to be utilized remain unspecified, and the DCSA did not reply to inquiries for further details.
Nevertheless, other Pentagon initiatives may provide indications. In 2023, the department’s Defense Innovation Unit (DIU) conducted an open call for companies offering products suitable for deception detection.
It chose two firms: Presage Technologies, which asserts it can gauge heart rate and respiration rate via conventional cameras, and Altec Research, a medical sensor firm now 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.
Contemporary lie detection methods have seen minimal evolution since the polygraph was developed. Examiners depend on blood pressure, pulse, respiration, and perspiration readings to assess whether an individual is being deceptive. They evaluate the truthfulness of responses based on variations in physiological reactions to control questions such as «Is the sky blue?» versus 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 dependability has faced frequent criticism—and its findings are seldom accepted in court. In 1983, Congress’s Office of Technology Assessment determined there was scant evidence backing the polygraph’s use for employee screening, and in 2003, the US National Research Council (NRC) characterized the evidence for its effectiveness as «weak at best.»
Research indicates that humans can detect deception only slightly more than half the time without any technological aid. The American Polygraph Association maintains the polygraph is 80% to 94% accurate. However, the 2003 NRC report noted that a screening test with such accuracy could still produce numerous errors. The Department of Defense employs 2.8 million individuals; an imperfect system applied at that magnitude could result in tens of thousands of false accusations.
Additional complications exist. Polygraph interpretations are frequently subjective, and individuals from minority backgrounds are more likely to be classified as deceptive. Furthermore, with training, interviewees can acquire various countermeasures to defeat the test; for instance, they might deliberately amplify their physiological reactions to control questions by stepping on a hidden pin in their shoe.
«If you know how it works, you can beat it,» states Sophie van der Zee, an associate professor specializing in deception studies at Erasmus University in Rotterdam. She notes the machine’s primary impact is deterrence—frequently, subjects confess before the test even starts. «But that only works if people think a polygraph works,» she observes.
Numerous novel approaches to lie detection have been explored over time, incorporating technologies from thermal imaging to pupil tracking to brain scanning. None have produced dependable outcomes beyond laboratory settings. The core issue is the absence of a universal indicator of lying that applies to everyone consistently. «There is still no Pinocchio’s nose,» van der Zee says.
Artificial intelligence could theoretically enhance this if it can identify patterns in data that human examiners overlook. AI algorithms are also more apt to be applied for «multi-modal» deception detection, which aims to merge various measurements into a comprehensive deception «score» that is more difficult for individuals to manipulate. Three underlying processes occur that lie detection seeks to identify, according to van der Zee: physiological stress, cognitive load, and the deliberate attempts people make to hide their deception. Present 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,» van der Zee explains. This is not a novel idea—during the 2000s, researchers at Manchester Metropolitan University in the UK created a system called Silent Talker that produced a deception score from video recordings. This was subsequently integrated into iBorderCtrl, a pilot program funded by the EU. In the US, a project named AVATAR combined eye tracking, voice analysis, and body movement detection into a tool intended for border checkpoints. All of these initiatives have quietly disappeared.
Kotsoglou argues that merging AI with the polygraph amounts to «the worst of both worlds» since it introduces uncertainty atop an already invalid foundation. Even if AI or machine learning can detect previously unrecognized patterns in physiological data, it cannot reliably connect them to deception because no true baseline 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 usage in the justice system. She worries that emerging lie detection methods will, similar to the polygraph, serve more as a psychological crutch than a scientific instrument.
«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.