Pentagon requests $30.3 million for AI lie detector development
The Pentagon is seeking $30.3 million from Congress to develop an AI-powered lie detector for military use, aiming to improve the accuracy of truth assessments in interrogations and security screeninโฆ
The Pentagon has asked Congress for $30.3โฏmillion to build an artificialโintelligenceโdriven lie detector, a program it says will be rolled out over the next five years and used in military interrogations and security screenings. The request was filed in the Defense Departmentโs annual budget proposal and earmarked for a new research effort under the Defense Advanced Research Projects Agency.
The move comes as the U.S. government seeks faster, more reliable ways to assess truthfulness after years of mixed results with traditional polygraph tests. Recent advances in machine learning have made it possible to analyze facial microโexpressions, voice stress and physiological signals at scale. Officials argue that an AI system could reduce human error and speed up decisionโmaking in highโstakes environments, from combat zones to airport security. Critics, however, warn that the technology is still unproven and could be vulnerable to bias, especially when trained on limited data sets.
DARPAโs โDeception Detectionโ project will lead the effort, partnering with universities and private firms that specialize in computer vision and speech analysis. Early prototypes have shown promise in lab settings, detecting deception with accuracy rates that exceed those of human operators by a modest margin. The program will initially focus on controlled experiments with volunteer participants, before moving to field trials in military bases. Civilโrights groups have already filed comments with the Office of Management and Budget, urging safeguards to protect privacy and prevent misuse.
If Congress approves the funding, the Pentagon plans to begin fullโscale development within the next twelve months. The first phase will involve building a dataโrich repository of truthโverification scenarios and training the AI models. Subsequent phases will test the system in realโworld interrogations, with the goal of integrating it into existing security workflows by 2030. The outcome could reshape how the military and other agencies evaluate credibility, but it also raises questions about accountability and the ethical limits of automated surveillance.
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