Fair Enough? On (Avoiding) Bias in Data, Algorithms and Decisions - Privacy and Identity Management. Data for Better Living: AI and Privacy
Conference Papers Year : 2020

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

Francien Dechesne
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Universiteit Leiden = Leiden University

Abstract

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.

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Computer Science [cs]
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Submitted on : Thursday, October 14, 2021-5:48:17 PM

Last modification on : Monday, September 4, 2023-2:04:05 PM

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hal-03378959 , version 1 (14-10-2021)

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Francien Dechesne. Fair Enough? On (Avoiding) Bias in Data, Algorithms and Decisions. 14th IFIP International Summer School on Privacy and Identity Management (Privacy and Identity), Aug 2019, Windisch, Switzerland. pp.17-26, ⟨10.1007/978-3-030-42504-3_2⟩. ⟨hal-03378959⟩

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