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‘All data is credit data’: personalised consumer credit scores and anti-discrimination law

‘All data is credit data’: personalised consumer credit scores and anti-discrimination law
‘All data is credit data’: personalised consumer credit scores and anti-discrimination law
Credit-score models provide one of the many contexts through which the big data micro-segmentation or ‘personalisation’ phenomenon can be analysed and critiqued. This chapter approaches the issue through the lens of anti-discrimination law, and in particular the concept of indirect discrimination. The argument presented is that, despite its initial promise based on its focus on impact, ‘indirect discrimination’ is after all unlikely to deliver a mechanism to intervene and curb the excesses of the personalised service model. The reason for its failure does not lie in its inherent weaknesses but rather in the 'shortcomings' (entrenched biases) of empirical reality itself which any 'accurate' (or useful) statistical analysis cannot but reflect. Still, the anti-discrimination context offers insights that are valuable beyond its own disciplinary boundaries. For example, the opportunities for oversight and review based on correlations within outputs rather than analysis of inputs is fundamentally at odds with the current trend that demands greater transparency of AI but may after all be more practical and realistic considering the ‘natural’ opacity of learning algorithms and businesses’ ‘natural’ secrecy. The credit risk score context also provides a low-key yet powerful illustration of the oppressive potential of a world in which individual behaviour from ANY sphere or domain may be used for ANY purpose; where a bank, insurance company, employer, health care provider, or indeed any government authority can tap into our social DNA to pre-judge us, should it be considered appropriate and necessary for their manifold objectives.
124-141
Collado-Rodriguez, Noelia
8ab0d77f-14c7-4556-b53d-b3cc785190c8
Kohl, Uta
813ff335-441f-4027-801b-4e6fc48409c3
Kohl, Uta
Eisler, Jacob
Collado-Rodriguez, Noelia
8ab0d77f-14c7-4556-b53d-b3cc785190c8
Kohl, Uta
813ff335-441f-4027-801b-4e6fc48409c3
Kohl, Uta
Eisler, Jacob

Collado-Rodriguez, Noelia and Kohl, Uta (2021) ‘All data is credit data’: personalised consumer credit scores and anti-discrimination law. In, Kohl, Uta and Eisler, Jacob (eds.) Data-Driven Personalisation in Markets, Politics and Law. pp. 124-141. (doi:10.1017/9781108891325.010).

Record type: Book Section

Abstract

Credit-score models provide one of the many contexts through which the big data micro-segmentation or ‘personalisation’ phenomenon can be analysed and critiqued. This chapter approaches the issue through the lens of anti-discrimination law, and in particular the concept of indirect discrimination. The argument presented is that, despite its initial promise based on its focus on impact, ‘indirect discrimination’ is after all unlikely to deliver a mechanism to intervene and curb the excesses of the personalised service model. The reason for its failure does not lie in its inherent weaknesses but rather in the 'shortcomings' (entrenched biases) of empirical reality itself which any 'accurate' (or useful) statistical analysis cannot but reflect. Still, the anti-discrimination context offers insights that are valuable beyond its own disciplinary boundaries. For example, the opportunities for oversight and review based on correlations within outputs rather than analysis of inputs is fundamentally at odds with the current trend that demands greater transparency of AI but may after all be more practical and realistic considering the ‘natural’ opacity of learning algorithms and businesses’ ‘natural’ secrecy. The credit risk score context also provides a low-key yet powerful illustration of the oppressive potential of a world in which individual behaviour from ANY sphere or domain may be used for ANY purpose; where a bank, insurance company, employer, health care provider, or indeed any government authority can tap into our social DNA to pre-judge us, should it be considered appropriate and necessary for their manifold objectives.

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More information

Accepted/In Press date: 2020
Published date: 1 July 2021

Identifiers

Local EPrints ID: 443583
URI: http://eprints.soton.ac.uk/id/eprint/443583
PURE UUID: 14509839-b276-4170-917b-af5ee94d6525
ORCID for Uta Kohl: ORCID iD orcid.org/0000-0002-8616-9469

Catalogue record

Date deposited: 03 Sep 2020 01:47
Last modified: 13 Oct 2022 01:56

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Contributors

Author: Noelia Collado-Rodriguez
Author: Uta Kohl ORCID iD
Editor: Uta Kohl
Editor: Jacob Eisler

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