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

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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Conference papers
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Submitted on : Thursday, October 14, 2021 - 5:48:17 PM
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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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