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into this novel setting. It also points to the important intricacies every path entails
and the difficulties of applying the notion of search to this novel setting.
Addressing this interesting comparison need not be a mere theoretical exercise.
The theoretical concepts drawn out here will prove important in the future.
Regulators will surely strive to move from theory to practice, approach data
mining initiatives and establish which practices are to be allowed, and which
must be prohibited. Therefore, this chapter would be of interest not only to
readers interested in legal theory. It might also prove helpful to regulators and
ractitioners seeking ways to ground the novel data mining practices in existing
legal concepts.
Before proceeding, several analytical foundations must be set in place.
Therefore, in section 18.2, the chapter briefly demonstrates and explains the
meaning of data mining initiatives and what they might entail. This is a crucial
step, as the term “data mining” has almost taken on a life of its own, and is applied
in several - at times contradictory - ways. Data mining also presents specific
unique traits, and sets distinct roles for humans and machines. Section 18.3 sets
forth the central thesis of this chapter. It first explains why the chapter chose to
import theoretical insights from “search” related interests in privacy law. It also
explains why specific theories of search were selected for this discussion. It
thereafter moves on to map out three ways in which the somewhat abstract notion
of “search” could be conceptualized, and applies these notions to the data mining
context. In doing so, the analysis addresses specific points where applying the
relevant theory to the data mining context might face theoretical and practical
obstacles, and discusses ways to overcome them. The chapter concludes in section
18.4, where it briefly explains the policy implications of applying every theory,
both in terms of direct and ancillary policy measures which might be called for to
minimize privacy related concerns. In these last two sections, the chapter
demonstrates the importance of the theoretical analysis presented; indeed, the
manner in which data mining practices are conceptualized directly effects the
possible solutions which might be set in place to limit related concerns.
The chapter specifically focuses on the data mining practices of government,
while purposefully neglecting similar initiatives carried out by commercial
entities. This is not to say that the latter practices do not raise privacy concerns in
general, and those related to the concepts of unacceptable searches in particular.
Indeed, marketers, advertisers and insurers are all crunching away on the vast
datasets of personal information at their disposal. In doing so, they open the door
to a flurry of policy and legal problems regarding the permitted scope of using
personal data and (among others) the form of consent data subjects must provide
prior to such uses. This chapter, however, sets these issues aside for now. While
the commercial-related issues are severe, governmental data mining leads to
concerns of a far greater magnitude. The government has great datasets of
personal information at its disposal and almost endless resources and opportunities
to obtain many more. It can collect such information without the data subjects'
consent (and in many cases without their knowledge). Perhaps most crucially, it
can potentially use such information to impact the property, liberty and even life
of the data subjects, given the government's almost limitless powers. For these
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