Database Reference
In-Depth Information
mining and profiling tools.
5
The aim of this project was to investigate how and to
what extent legal and ethical rules can be integrated into data mining algorithms to
prevent discrimination. For the practical testing of theories this project developed,
data sets in the domain of public security made available by police and justice
departments, were used for testing. The project's focus was on preventing an
outcome according to which selection rules turn out to discriminate particular groups
of people in unethical or illegal ways. Key questions were how existing legal and
ethical rules and principles can be translated into formats understandable to
computers and in which way these rules can be used to guide the data mining process.
Furthermore, the technological possibilities were used as feedback to formulate
concrete guidelines and recommendations for formalizing legislation. These concrete
tasks also related to broader and abstract themes, such as clarifying how existing
ethical and legal principles are to be applied to new technologies and what the limits
of privacy are. Contrary to previous scholarly attempts to examine privacy in data
mining, this project did not focus on (a priori) access limiting measures regarding
input data. The project's focus rather was on (a posteriori) responsibility and
transparency. Instead of limiting the access to data, which is increasingly hard to
enforce, questions as to how data can and may be used were stressed.
The research project was scheduled to run from October 2009 to October 2010
and conclude at that time. In reality, it never did. The research results encouraged
us to engage in further research, particularly when we discovered that simply
deleting discrimination sensitive characteristics (such as gender, ethnic
background, nationality) from databases still resulted in (possibly) discriminating
patterns. In other words, things were far more complicated than everyone initially
thought. New algorithms were developed to prevent discrimination and violations
of privacy. Thus far, the research results were presented in several internationally
acclaimed scientific journals, at international conferences in seven countries and
in technical reports, topic chapters and popular journals. A complete overview of
the research results can be found at the wiki of the project.
6
During one of the meetings with the valorization panel, the panel members
suggested that the research results, particularly the more technical results, are very
interesting for people with a non-technical background. Thus, the valorization
panel asked us whether it would be possible to combine the research results in a
topic that explains the latest technological developments with regard to data
mining and profiling in a manner which is comprehensible to a crowd which lacks
a technological background. This topic tries to achieve this. This topic presents the
research results of our project together with contributions of leading authors in this
field, all written in a non-technical language. Complicated equations were avoided
as much as possible or moved to the footnotes. Technological terminology is
avoided in some places and carefully explained in other places. Similarly, the
jargon of the legal and other non-technical chapters is avoided or carefully
explained. All this should help non-technical readers to understand what is
technologically already possible (or impossible) and how exactly it works. At the
same time it should help technical readers to understand how end users really
view, use and judge these technological tools and why they are sometimes
5
http://www.nwo.nl/nwohome.nsf/pages/NWOP_8K6G4N_Eng
6
http://wwwis.win.tue.nl/~tcalders/dadm/doku.php
Search WWH ::
Custom Search