Deception Detection

by Marek N. Posard, Christian Johnson, Julia L. Melin, Emily Ellinger, Hilary Reininger

A group of RAND Corporation researchers found that machine-learning (ML) models can identify signs of deception during national security background check interviews. The most accurate approach for detecting deception is an ML model that counts the number of times that interviewees use common words.

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How the Experiment Worked

The 103 participants read a story about Edward Snowden leaking classified information from the National Security Agency in 2013.

Participants were randomly assigned to read the same story, but the story was presented as either a news report or a memo with markings indicating it contained sensitive information.

Participants were assigned to one of two groups for interviews: One group was told to lie about what they had read, and the other was told to tell the truth.

Former law enforcement officers interviewed participants via video teleconference (VTC) and text-based chat in randomized order.

RAND researchers used the interview and chat transcripts to train several ML models to see whether the models could distinguish the liars from the truth-tellers.

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