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Data mining and automated discrimination: a mixed legal/technical perspective

Data mining and automated discrimination: a mixed legal/technical perspective
Data mining and automated discrimination: a mixed legal/technical perspective
Socially sensitive decisions about critical issues such as employment, credit scoring, or insurance premiums are increasingly automated based on big data mining. Although algorithms do not have personal preferences, they are not neutral, and the data itself can reflect various undesirable biases. The authors discuss how discrimination-aware data mining constitutes a crucial step to counter automated discrimination. They then explain why the complexity of legal and social norms requires a balanced interdisciplinary methodology and toolset comprising requirements relating to data accuracy, protection, and provenance, and legitimacy of targeted objectives.
1541-1672
51-55
Carmichael, Laura
3f71fb73-581b-43c3-a261-a6627994c96e
Stalla-Bourdillon, Sophie
c189651b-9ed3-49f6-bf37-25a47c487164
Staab, Steffen
bf48d51b-bd11-4d58-8e1c-4e6e03b30c49
Carmichael, Laura
3f71fb73-581b-43c3-a261-a6627994c96e
Stalla-Bourdillon, Sophie
c189651b-9ed3-49f6-bf37-25a47c487164
Staab, Steffen
bf48d51b-bd11-4d58-8e1c-4e6e03b30c49

Carmichael, Laura, Stalla-Bourdillon, Sophie and Staab, Steffen (2016) Data mining and automated discrimination: a mixed legal/technical perspective. IEEE Intelligent Systems, 31 (6), 51-55.

Record type: Article

Abstract

Socially sensitive decisions about critical issues such as employment, credit scoring, or insurance premiums are increasingly automated based on big data mining. Although algorithms do not have personal preferences, they are not neutral, and the data itself can reflect various undesirable biases. The authors discuss how discrimination-aware data mining constitutes a crucial step to counter automated discrimination. They then explain why the complexity of legal and social norms requires a balanced interdisciplinary methodology and toolset comprising requirements relating to data accuracy, protection, and provenance, and legitimacy of targeted objectives.

Full text not available from this repository.

More information

Accepted/In Press date: 30 September 2016
Published date: 11 November 2016
Organisations: Web & Internet Science, Southampton Law School

Identifiers

Local EPrints ID: 403402
URI: https://eprints.soton.ac.uk/id/eprint/403402
ISSN: 1541-1672
PURE UUID: 034af849-ca0b-40b3-a6ad-343f5fe5c808
ORCID for Steffen Staab: ORCID iD orcid.org/0000-0002-0780-4154

Catalogue record

Date deposited: 30 Nov 2016 15:13
Last modified: 06 Jun 2018 12:21

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