Fair Enough? On (Avoiding) Bias in Data, Algorithms and Decisions

  • Francien DechesneEmail author
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 576)


This contribution explores bias in automated decision systems from a conceptual, (socio-)technical and normative perspective. In particular, it discusses the role of computational methods and mathematical models when striving for “fairness” of decisions involving such systems.


Bias Data analytics Algorithmic decision systems Fairness 



This work is part of the SCALES project funded by the Dutch Research Council NWO MVI-program under project number 313-99-315.


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Copyright information

© IFIP International Federation for Information Processing 2020

Authors and Affiliations

  1. 1.eLaw Center for Law and Digital TechnologiesLeiden University Law SchoolLeidenThe Netherlands

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