The Data Mining Balancing Act

Governments are facing new and serious risks when striving to assure the security and safety of their citizens. Perhaps the greatest concern is the fear of terrorist attacks. Various technological tools are being used or considered as means to meet such challenges and curb these risks. Of the tools discussed in the political and legal sphere, data mining applications for the analysis of personal information have probably generated the greatest interest. The discovery of distinct behavior patterns linking several of the 9/11 terrorists to each other and other known operatives has led many to ask: What if data mining had been applied in advance? Could the attacks and their devastating outcomes have been avoided? Data mining has captured the imagination as a tool which can potentially close the intelligence gap constantly deepening between governments and their new targets—individuals posing a risk to security and the public’s well-being (Jonas and Harper 2006; Schneier 2006).1 Data mining is also generating interest in other governmental contexts, such as law enforcement and policing. In recent years, law enforcement has shifted to “Intelligence Led Policing (“ILP”; Cate 2008). Rather than merely reacting to events and investigating them, law enforcement is trying to preempt crime. It does so by gathering intelligence, which includes personal information, closely analyzing it, and allocating police resources accordingly—all tasks which could be enhanced by data mining technology (IBM 2010).2 The growing appeal of data mining in all these contexts results from similar reasons—the development of cutting edge technologies, advances in mathematics,