The U.S. Department of Defense is seeking $30.3 million over five years for Polygraph+, a program that would combine machine learning with new sensors in an attempt to improve employee screening and leak investigations. Congress has not yet approved the requested funding.
MIT Technology Review reports that the Defense Counterintelligence and Security Agency would run the project. The budget documents describe scoring algorithms and “standoff sensing,” which means measuring physiological signals without attaching traditional polygraph equipment to a subject.
New sensors do not solve the old validity problem
Earlier Defense Innovation Unit prototypes used ordinary cameras to estimate heart and breathing rates or tracked signals such as facial temperature, pore activity and head movement. Polygraph+ could combine several measurements into one deception score.
The scientific objection is more fundamental than sensor quality. Traditional polygraphs measure stress-related signals, not lies, and researchers do not have dependable ground-truth labels showing whether the people in large screening datasets were actually deceptive. Training a model on examiners' judgments can therefore automate existing assumptions without validating them.
The federal government conducts tens of thousands of polygraph screenings each year, even though major scientific reviews have found weak evidence for their reliability in employee screening. Researchers interviewed by MIT Technology Review warned that an AI layer could make an uncertain process appear more objective while preserving false positives, examiner bias and the possibility of countermeasures.
