The Way Forward

  • Bart Custers
  • Toon Calders
  • Tal Zarsky
  • Bart Schermer
Part of the Studies in Applied Philosophy, Epistemology and Rational Ethics book series (SAPERE, volume 3)


The growing use of data mining practices by both government and commercial entities leads to both great promises and challenges. They hold the promise of facilitating an information environment which is fair, accurate and efficient. At the same time, they might lead to practices which are both invasive and discriminatory, yet in ways the law has yet to grasp. This point is demonstrated by showing how the common measures for mitigating privacy concerns, such as a priori limiting measures (particularly access controls, anonymity and purpose specification) are mechanisms that are increasingly failing solutions against privacy and discrimination issues in this novel context.

Instead, a focus on (a posteriori) accountability and transparency may be more useful. This requires improved detection of discrimination and privacy violations as well as designing and implementing techniques that are discrimination-free and privacy-preserving. This requires further (technological) research.

But even with further technological research, there may be new situations and new mechanisms through which privacy violations or discrimination may take place. Novel predictive models can prove to be no more than sophisticated tools to mask the “classic” forms of discrimination, by hiding discrimination behind new proxies. Also, discrimination might be transferred to new forms of population segments, dispersed throughout society and only connected by some attributes they have in common. Such groups will lack political force to defend their interests. They might not even know what is happening.

With regard to privacy, the adequacy of the envisaged European legal framework is discussed in the light of data mining and profiling. The European Union is currently revising the data protection legislation. The question whether these new proposals will adequately address the issues raised in this book is dealt with.


Data Mining Access Control Personal Data Data Protection Data Subject 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Bart Custers
    • 1
  • Toon Calders
    • 2
  • Tal Zarsky
    • 3
  • Bart Schermer
    • 1
  1. 1.eLaw, Institute for Law in the Information SocietyLeiden UniversityLeidenThe Netherlands
  2. 2.Eindhoven University of TechnologyEindhovenvThe Netherlands
  3. 3.Faculty of LawUniversity of HaifaHaifaIsrael

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