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Fair enough? on (avoiding) bias in data, algorithms and decisions

IFIP Advances in Information and Communication Technology (2020) 576 LNCS 17-26
DOI: 10.1007/978-3-030-42504-3_2
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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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Dechesne, F. (2020). Fair enough? on (avoiding) bias in data, algorithms and decisions. In IFIP Advances in Information and Communication Technology (Vol. 576 LNCS, pp. 17–26). Springer. https://doi.org/10.1007/978-3-030-42504-3_2

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