Effective Counterterrorism and the Limited Role of Predictive Data Mining

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The terrorist attacks on September 11, 2001,spurred extraordinary efforts intended to protectAmerica from the newly highlighted scourge ofinternational terrorism. Among the efforts was theconsideration and possible use of "data mining" asa way to discover planning and preparation for terrorism.Data mining is the process of searchingdata for previously unknown patterns and usingthose patterns to predict future outcomes.

Information about key members of the 9/11plot was available to the U.S. government priorto the attacks, and the 9/11 terrorists were closelyconnected to one another in a multitude ofways. The National Commission on TerroristAttacks upon the United States concluded that,by pursuing the leads available to it at the time,the government might have derailed the plan.

Though data mining has many valuable uses,it is not well suited to the terrorist discoveryproblem. It would be unfortunate if data miningfor terrorism discovery had currency withinnational security, law enforcement, and technologycircles because pursuing this use of datamining would waste taxpayer dollars, needlesslyinfringe on privacy and civil liberties, and misdirectthe valuable time and energy of the men andwomen in the national security community.

What the 9/11 story most clearly calls for is asharper focus on the part of our national securityagencies—their focus had undoubtedly sharpenedby the end of the day on September 11,2001—along with the ability to efficiently locate,access, and aggregate information about specificsuspects.

Jeff Jonas and Jim Harper

Jeff Jonas is distinguished engineer and chief scientist with IBM's Entity Analytic Solutions Group. Jim Harper is director of information policy studies at the Cato Institute and author of Identity Crisis: How Identification Is Overused and Misunderstood.