Governmental Data Mining and its Alternatives
By Tal Z. Zarsky.
Governments face new and serious risks when striving to protect their citizens. Of the various information technology tools discussed in the political and legal sphere, data mining applications for the analysis of personal information have probably generated the greatest interest. Data mining has captured the imagination as a tool which can potentially close the intelligence gap constantly deepening between governments and their targets. Data mining initiatives are popping up everywhere. The reaction to the data mining of personal information by governmental entities came to life in a flurry of reports, discussions, and academic papers. The general notion in these sources is that of fear and even awe. As this discourse unfolds, something is still missing. An important methodological step must be part of every one of these inquires mentioned abovethe adequate consideration of alternatives. This article is devoted to bringing this step to the attention of academics and policymakers.
The article begins by explaining the term “data mining,” its unique traits, and the roles of humans and machines. It then maps out, with a very broad brush, the various concerns raised by these practices. Thereafter, it introduces four central alternative strategies to achieve the governmental objectives of security and law enforcement without engaging in extensive data mining and an additional strategy which applies some data mining while striving to minimize several concerns. The article sharpens the distinctions between the central alternatives to promote a full understanding of their advantages and shortcomings. Finally, the article briefly demonstrates how an analysis that takes alternative measures into account can be carried out in two contexts. First, it addresses a legal perspective, while considering the detriments of data mining and other alternatives as overreaching “searches.” Second, it tests the political process set in motion when contemplating these measures. This final analysis leads to an interesting conclusiondata mining (as opposed to other options) might indeed be disfavored by the public, but mandates the least scrutiny by courts. In addition, the majority’s aversion from the use of data mining might result from the fact that data mining refrains from shifting risk and costs to weaker groups. This is yet one of the ways the methodology of examining alternatives can illuminate our understanding of data mining and its effects.