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In-Depth Information
Empirical results on indirect discrimination prevention methods can be found in
Hajian et al. (2011b).
13.8 Conclusions and Future Work
In sociology, discrimination is the prejudicial treatment of an individual based on
their membership in a certain group or category. It involves denying to members
of one group opportunities that are available to other groups. Like privacy, dis-
crimination could have negative social impact on acceptance and dissemination of
data mining technology. Discrimination prevention in data mining is a new body
of research focusing on this issue. One of the research questions here is whether
we can adapt and use the pre-processing approaches of data transformation and
hierarchy-based generalization from the privacy preservation literature for dis-
crimination prevention. In response to this question, we try to inspire on the data
transformation methods for knowledge (rule) hiding in privacy preserving data
mining (more discussed in Chapter 11) and we devise new data transformation
methods ( i.e. direct and indirect rule protection, rule generalization) for converting
direct and/or indirect discriminatory decision rules to legitimate (non-
discriminatory) classification rules; our current results are convincing in terms of
discrimination removal and information loss. However, there are many other chal-
lenges regarding discrimination prevention that could be considered in the rest of
this research. For example, the perception of discrimination, just like the percep-
tion of privacy, strongly depends on the legal and cultural conventions of a socie-
ty. Although we argued that discrimination measures based on elift and elb are
reasonable, if substantially different discrimination definitions and/or measures
were to be found, new data transformation methods would need to be designed.
Another challenge is the relationship between discrimination prevention and
privacy preservation in data mining. It would be extremely interesting to find syn-
ergies between rule hiding for privacy-preserving data mining and rule hiding for
discrimination removal. Just as we were able to show that indirect discrimination
removal can help direct discrimination removal, it remains to see whether privacy
protection can help anti-discrimination or vice versa .
Disclaimer and Acknowledgments
The authors are with the UNESCO Chair in Data Privacy, but the views expressed in this
article do not necessarily reflect the position of UNESCO nor commit that organization.
This work was partly supported by the Spanish Government through projects TSI2007-
65406-C03-01 “E-AEGIS”, TIN2011-27076-C03-01 “CO-PRIVACY” and CONSOLIDER
INGENIO 2010 CSD2007-00004 “ARES”, by the Government of Catalonia under
grant 2009 SGR 1135 and by the European Commission under FP7 project “DwB”. The
second author is partly supported as an ICREA Acadèmia Researcher by the Government of
Catalonia.
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