Using Artificial Intelligence to Help Clear the Investigation Backlog
With all of the process changes regarding background investigations implemented by the Office of the Director of National Intelligence (ODNI), Office of Personnel Management (OPM), and the Defense Security Service (DSS), one would think the backlog numbers would start shrinking. Yet based on the recent report from the National Background Investigation Bureau (NBIB) it still seems to be going the other way. DoD has invested in using a continuous evaluation system lieu of periodic reinvestigations and other measures to reduce the time it takes for initial clearance investigations to get completed.
There is also a push from certain sectors to leverage artificial intelligence (or machine learning) to further assist in reducing the investigation backlog. A recent article in Nextgov provides an interesting perspective on how we could use AI to parse through massive amounts of data and make the security clearance process much more efficient. This direct quote from the story pretty much sums it all up :
“It is important for key decision-makers at the Pentagon to understand that machine learning is a tool that investigators and reviewers should use in making clearance determinations by flagging risk factors to be further evaluated before a final determination is made. Using machine-learning tools does not mean simply trusting AI robots to make critical national security rulings, but instead help more efficiently flag risks for human evaluation.”
In my opinion this “machine learning” tool is a force multiplier and would help clear cases a lot faster. It would not replace investigators and adjudicators, but rather assists them in reviewing no-issue cases and identifying flags that need further scrutiny and follow-up.