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.
51-55
Carmichael, Laura
3f71fb73-581b-43c3-a261-a6627994c96e
Stalla-Bourdillon, Sophie
c189651b-9ed3-49f6-bf37-25a47c487164
Staab, Steffen
bf48d51b-bd11-4d58-8e1c-4e6e03b30c49
11 November 2016
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), .
(doi:10.1109/MIS.2016.96).
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.
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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: http://eprints.soton.ac.uk/id/eprint/403402
ISSN: 1541-1672
PURE UUID: 034af849-ca0b-40b3-a6ad-343f5fe5c808
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Date deposited: 30 Nov 2016 15:13
Last modified: 16 Mar 2024 04:05
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Author:
Steffen Staab
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