Database Reference
In-Depth Information
introduced guided by ample references. Furthermore, literature on economic
models of labor discrimination, approaches for collecting and analyzing data,
discrimination in profiling and scoring and recent work on discrimination
discovery and prevention is discussed. This inventory is intended to provide a
common basis to anyone working in this field.
In Chapter 7, Schermer maps out risks related to profiling and data mining that
go beyond discrimination issues. Risks such as de-individualization and
stereotyping are described. To mitigate these and other risks, traditionally the right
to (informational) privacy is invoked. However, due to the rapid technological
developments, privacy and data protection law have several limitations and
drawbacks. Schermer discusses why it is questionable whether privacy and data
protection legislation provide adequate levels of protection and whether these
legal instruments are effective in balancing different interests when it comes to
profiling and data mining.
1.4.3 Part III: Practical Applications
Part III of this topic sets forth several examples of practical applications of data
mining and profiling. These chapters intend to illustrate the added value of
applying data mining and profiling tools. They also show several practical issues
that practitioners may be confronted with.
In Chapter 8, Kamiran and Žliobaitė illustrate how self-fulfilling prophecies in
data mining and profiling may occur. Using several examples they show how
models learnt over discriminatory data may result in discriminatory decisions.
They explain how discrimination can be measured and show how redlining may
occur. Redlining originally is the practice of denying products and services in
particular neighborhoods, marked with a red line on a map to delineate where not
to provide credit. This resulted in discrimination against black inner city
neighborhoods. In databases this effect may also occur, not necessarily by
geographical profiling, but also by profiling other characteristics. Kamiran and
Žliobaitė present several techniques to preprocess the data in order to remove
discrimination, not by removing all discriminatory data or all differences between
sensitive groups, but by addressing differences unacceptable for decision-making.
With experiments they demonstrate the effectiveness of these techniques.
In Chapter 9, Schakel, Rienks and Ruissen focus on knowledge discovery and
profiling in the specific context of policing. They observe that the positivist
epistemology underlying the doctrine of information-led policing is incongruent
with the interpretive-constructivist basis of everyday policing, and conclude that
this is the cause of its failure to deliver value at the edge of action. After shifting
focus from positivist information-led policing to interpretive-constructivist
knowledge-based policing, they illustrate how profiling technologies can be used
to design augmented realities to intercept criminals red-handedly. Subsequently,
Schakel, Rienks and Ruissen discuss how the processing of data streams (rather
than databases) can meet legal requirements regarding subsidiarity,
proportionality, discrimination and privacy.
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