Proving algorithmic discrimination in government decision-making

Joe Tomlinson; Jack Maxwell

Proving algorithmic discrimination in government decision-making

Law

Research output: Contribution to journal › Article › peer-review

Abstract

Public bodies in the United Kingdom are increasingly using algorithms and big data to make decisions. While there is growing awareness of the risks of algorithmic discrimination, it can be very difficult to establish that a specific algorithm is in fact discriminatory. This raises the question of how courts should allocate the burden of testing and proving whether a government algorithm is discriminatory, as between the government and the person affected. In R (Bridges) v South Wales Police [2020] EWCA Civ 1058, the England and Wales Court of Appeal found that public bodies must take positive steps to identify and address risks of algorithmic discrimination. This note explores the decision in Bridges and its implications for algorithmic decision-making in government. It suggests that Bridges, alongside recent decisions in Canada and the Netherlands, forms part of a broader trend: the courts are placing the burden of testing and reviewing potentially discriminatory algorithms on government, rather than the general public.

Original language	English
Pages (from-to)	352-360
Number of pages	8
Journal	OXFORD UNIVERSITY COMMONWEALTH LAW JOURNAL
Volume	20
Issue number	2
Early online date	21 Oct 2020
Publication status	E-pub ahead of print - 21 Oct 2020

Bibliographical note

© 2020 Faculty of Law, Oxford University. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details.

Access to Document

200929 Maxwell Tomlinson - final - for publication (reviewed) - JM
© 2020 Faculty of Law, Oxford University. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details.
Accepted author manuscript, 49.8 KBLicence: Unspecified

Cite this

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