Oct 19, 2022
The authors of this report developed and tested the potential for new interviewing methods, including the use of machine learning (ML), to detect speech patterns that reflect attempts at deception or truthfulness during simulated background investigation interviews. They found that ML models are promising tools that have the capacity to augment existing security clearance background investigation processes.
An Exploratory Analysis for Automated Detection of Deception
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Security clearance investigations are onerous for both the applicants and investigators, and such investigations are expensive for the U.S. government. In this report, the authors present results from an exploratory analysis that tests automated tools for detecting when some of these applicants attempt to deceive the government during the interview portion of this process. How interviewees answer interview questions could be a useful signal to detect when they are trying to be deceptive.
Relevant Background Literature
Description of Data
Results from Analysis of Interview Data
Potential Sources of Bias
Limitations, Conclusions, and Recommendations
Modified Cognitive Interviewing
Example Output from Amazon Web Services Transcribe
Proof of Concept: Deep Learning Contradiction Model
This research was sponsored by Performance Accountability Council's Program Management Office and conducted within the Forces and Resources Policy Center of the RAND National Security Research Division (NSRD).
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