Data Dilemmas in the Information Society: Introduction and Overview - Discrimination and Privacy in the Information Society - page 18

discrimination sensitive data in the databases is removed or suppressed before the
data mining is commenced. It is shown how data mining may exhibit
discriminatory behavior towards particular groups based, for instance, upon
gender or ethnicity. It is often suggested that removing all discrimination sensitive
attributes such as gender and ethnicity from databases may prevent the discovery
of such discriminatory relationships. 35 Without sensitive data it is impossible to
find sensitive patterns or relations, it is argued. Calders and Žliobaitė show that
this is not necessarily true. They carefully outline three realistic scenarios to
illustrate this and explain the reasons for this phenomenon.
1.4.2 Part II: Possible Discrimination and Privacy Issues
Part II of this topic explains the basics of discrimination and privacy and discusses
how data mining and profiling may cause discrimination and privacy issues.
In Chapter 4, Gellert, De Vries, De Hert and Gutwirth compare and distinguish
between European anti-discrimination law and data protection law. They show
that both rights have the same structure and increasingly turn to the same mode of
operation in the information society, even though their content is far from
identical. Gellert, De Vries, De Hert and Gutwirth show that this is because both
rights are grounded in the notion of negative freedom as evidenced by I. Berlin 36 ,
and thus aim at safeguarding the autonomy of the citizen in the information
society. Finally, they analyze two cases where both rights apply, and draw
conclusions on how to best articulate the two tools.
In Chapter 5, Pedreschi, Ruggieri and Turini address the problem of
discovering discrimination in large databases. Proving discrimination may be
difficult. For instance, was a job applicant turned down because she was pregnant
or because she was not suited for the job? In a single case, this may be difficult to
prove, but it may be easier if there are many cases. For instance, if a company
with over one thousand employees has no employees from ethnic minorities, this
may be due to discrimination. Similarly, when all top management boards in a
country consist of 90% of males, this may indicate possible discrimination. In
Chapter 5, the focus is on finding discriminatory situations and practices hidden in
large amounts of historical decision records. Such patterns and relations may be
useful for anti-discrimination authorities. Pedreschi, Ruggieri and Turini discuss
the challenges in discovering discrimination and present an approach for finding
discrimination on the basis of legally-grounded interesting measures.
In Chapter 6, Romei and Ruggieri present an annotated bibliography on
discrimination analysis. Literature on discrimination discovery and prevention is
mapped in the areas of law, sociology, economics and computer sciences.
Relevant legal and sociological concepts such as prejudices, racism, affirmative
action (positive discrimination) and direct versus indirect discrimination are
35 For instance, article 8 of the European Data Protection Directive (95/46/EC) explicitly
limits the processing of special categories of data that is considered especially sensitive
to data subjects, such as personal data revealing racial or ethnic origin, political opinions,
religious or philosophical beliefs, trade-union membership, health and sex life.
36 Berlin, I. (1969).