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But laws are not the only, and often not the most significant constraint, to
regulate something. Sometimes, things may be legal, but nevertheless considered
unethical or impolite. Lessig mentions the example of smoking, something that is
not illegal in many places, but may be considered impolite, at least without asking
permission of others present in the same room. Examples of ethical issues that are
strictly speaking not illegal that we will come across in this topic are
stigmatization of people, polarization of groups in society and de-
individualization. Such norms have a certain constraint on behavior.
Apart from laws and norms, a third force is the market. Price and quality of
products are important factors here. When the market supplies a wide variety of
data mining and profiling tools (some of these tools may be less discriminating or
more privacy friendly than others), there is more to choose from, reducing
constraints. However, when there are only one or two options available, the
market constrains the options. High prices (for instance, for data mining tools that
do not discriminate or are privacy friendly) that may limit what you can buy.
The fourth and last constraint is created by technology. How a technology is
built (its architecture) determines how it can be used. Walls may constrain where
you are can go. A knife can be used for good purposes, like cutting bread, or for
bad purposes, like hurting a person. Sometimes these constraints are not intended,
but sometimes they are explicitly included in the design of a particular technology.
Examples are copy machines that refuse to copy banknotes and cars that refuse to
start without keys and, in some cases, without alcohol tests. In our case of data
mining and profiling technologies, there are many constraints that can be built into
the technologies. That is the reason why we separated these 'solutions in code'
(Part IV of this topic) from the other solutions (Part V of this topic). Although this
topic has a strong focus on technological solutions, this does not mean, however,
that this is the only (type of) solution. In some cases, what is needed are different
attitudes, and in some cases new or stricter laws and regulations.
1.4 Structure of This Topic
1.4.1 Part I: Opportunities of Data Mining and Profiling
Part I of this topic explains the basics of data mining and profiling and discusses
why these tools are extremely useful in the information society.
In Chapter 2, Calders and Custers explain what data mining is and how it
works. The field op data mining is explored and compared with related research
areas, such as statistics, machine learning, data warehousing and online analytical
processing. Common terminology regarding data mining that will be used
throughout this topic is discussed. Calders and Custers explain the most common
data mining techniques, i.e., classification, clustering and pattern mining, as well
as some supporting techniques, such as pre-processing techniques and database
coupling.
In Chapter 3, Calders and Žliobaitė explain why and how the use of data
mining tools can lead to discriminative decision procedures, even if all
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