![]() Like the difference between a shotgun and a laser-sited 9mm, there is always the possibility of an error, but there is much less collateral damage with the more accurate weapon.Īgain, the real issue in the debate comes back to privacy concerns. On the other hand, basing our activities on real evidence can only increase the likelihood that we will correctly identify the bad guys while helping to protect the innocent by casting a more targeted net. There is no reason to believe that these same checks and balances would not continue to protect the innocent were data mining to be used extensively. We do not need data mining or technology to make errors we have been able to do that without the assistance of technology for many years. This is why there are so many checks and balances in the system-to protect the innocent. Regarding the suggestion that data mining has been associated with false leads and law enforcement mistakes, it is important to note that these errors happen already, without data mining. Data mining and predictive analytics give law enforcement and intelligence professionals the ability to put more evidence-based input into operational decisions and the deployment of scarce resources, thereby limiting the potential waste of resources in a way not available previously. One of the greatest potential strengths of data mining is that it gives public safety organizations the ability to allocate increasingly scarce law enforcement and intelligence resources in a more efficient manner while accommodating a concomitant explosion in the available information-the so-called “volume challenge” that has been cited repeatedly during investigations into law enforcement and intelligence failures associated with 9/11. Blindly deploying resources based on gut feelings, public pressure, historical precedent, or some other vague notion of crime prevention represents a true waste of resources. This underscores a lack of information regarding these analytical tools. On the other hand, some have suggested that incorporation of data mining and predictive analytics might result in a waste of resources. Subsequent review of the program, however, determined that its main shortcoming was related the failure to conduct a privacy impact study in an effort to ensure the maintenance of individual privacy this is something that organizations considering these approaches should include in their deployment strategies and use of data-mining tools. Originally developed by the Defense Advanced Research Projects Agency (DARPA), this program was ultimately dismantled, due at least in part to the public outcry and concern regarding potential abuses of private information. Unfortunately, many of these fears were based on a misunderstanding of the Total Information Awareness system (TIA, later changed to the Terrorism Information Awareness system), which promised to combine and integrate wide-ranging data and information systems from both the public and private sectors in an effort to identify possible terrorists. With data mining, ensuring privacy should be no different than with any other technique or analytical approach. Data mining and predictive analytics merely analyze the data that is made available they may be extremely powerful tools, but they are tools nonetheless. Privacy is maintained through restricting access to data and information. In fact, this concern is misplaced in many ways because data mining in and of itself has a limited ability, if any, to compromise privacy. The concern regarding an individual's right to privacy versus the need to enhance public safety represents a long-standing tension within the law enforcement and intelligence communities that is not unique to data mining. It has been suggested that data mining tools threaten to invade the privacy of unknowing citizens and unfairly target them for invasive investigative procedures that are associated with a high risk of false allegations and unethical labeling of certain groups. One of the harshest criticisms has addressed important privacy issues. ![]() Similarly, these same assets also can be misused or employed for unethical or illegal purposes. Like many of the devices used in public safety, data mining and predictive analytics can confer great benefit and enhanced public safety through their judicious deployment and use. Unfortunately, much of the debate that followed has been based on misinformation and a lack of knowledge regarding these very important tools. The discipline of data mining came under fire in the Data Mining Moratorium Act of 2003. Further confounding the question of whether to acquire data mining technology is the heated debate regarding not only its value in the public safety community but also whether data mining reflects an ethical, or even legal, approach to the analysis of crime and intelligence data.
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