%0 Conference Proceedings
%T Fair Enough? On (Avoiding) Bias in Data, Algorithms and Decisions
%+ Universiteit Leiden = Leiden University 
%A Dechesne, Francien
%Z Part 1: Invited Papers
%< avec comité de lecture
%( IFIP Advances in Information and Communication Technology
%B 14th IFIP International Summer School on Privacy and Identity Management (Privacy and Identity)
%C Windisch, Switzerland
%Y Michael Friedewald
%Y Melek Önen
%Y Eva Lievens
%Y Stephan Krenn
%Y Samuel Fricker
%I Springer International Publishing
%3 Privacy and Identity Management. Data for Better Living: AI and Privacy
%V AICT-576
%P 17-26
%8 2019-08-19
%D 2019
%R 10.1007/978-3-030-42504-3_2
%K Bias
%K Data analytics
%K Algorithmic decision systems
%K Fairness
%Z Computer Science [cs]Conference papers
%X 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.
%G English
%Z TC 9
%Z TC 11
%Z WG 9.2
%Z WG 9.6
%Z WG 11.7
%Z WG 11.6
%2 https://inria.hal.science/hal-03378959/document
%2 https://inria.hal.science/hal-03378959/file/496005_1_En_2_Chapter.pdf
%L hal-03378959
%U https://inria.hal.science/hal-03378959
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-WG
%~ IFIP-TC9
%~ IFIP-TC11
%~ IFIP-WG9-2
%~ IFIP-WG9-6
%~ IFIP-WG11-7
%~ IFIP-WG11-6
%~ IFIP-PRIMELIFE
%~ IFIP-AICT-576