Noting that the 9/11 terrorists "were hiding in plain sight," a report released this week by the Washington, DC-based Cato Institute concludes that the practice of data-mining will not help investigators discover terrorists, and severely infringes on civil liberties.
The report was written by Jeff Jonas, a distinguished engineer and chief scientist with IBM’s Entity Analytic Solutions Group, and Jim Harper of the Cato Institute. The blog Defense Tech notes that Jonas has considerable expertise in data-mining activities, as "Casino chiefs and government spooks alike have used his CIA-funded "Non-Obvious Relationship Awareness" software to scour databases for hidden connections."
The Cato report used a Congressional Research Service definition of data-mining, which says it involves "the use of sophisticated data analysis tools to discover previously unknown, valid patterns and relationships in large data sets."
Applying this concept to terrorism is faulty, the report warns. Jonas and Harper find that "Unlike consumers’ shopping habits and financial fraud, terrorism does not occur with enough frequency to enable the creation of valid predictive models....The one thing predictable about predictive data mining for terrorism is that it would be consistently wrong."
Frighteningly, the report cites other studies that show that "Assuming a 99 percent accuracy rate, searching our population of nearly 300,000,000, some 3,000,000 people would be identified as potential terrorists."
To become more effective "data-mining efforts would rely on even more collections of transactional and behavioral information, and on centralization of that data, all to examine Americans for criminality or disloyalty to the United States or Western society. That level of surveillance, aimed at the entire citizenry, would be inconsistent with American values."
The full Cato report can be downloaded at their website.
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