Legal contestation of artificial intelligence-related decision-making in the United Kingdom : reflections for policy
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This paper considers legal contestation in the UK as a source of useful reflections for AI policy. The government has published a 'National AI Strategy', but it is unclear how effective this will be given doubts about levels of public trust. One key concern is the UK's apparent ?side-lining? of the law. A series of events were convened to investigate critical legal perspectives on the issues, culminating in an expert workshop addressing five sectors. Participants discussed AI in the context of wider trends towards automated decision-making (ADM). A recent proliferation in legal actions is expected to continue. The discussions illuminated the various ways in which individual examples connect systematically to developments in governance and broader 'AI-related decision-making', particularly due to chronic problems with transparency and awareness. This provides a fresh and current insight into the perspectives of key groups advancing criticisms relevant to policy in this area. Policymakers? neglect of the law and legal processes is contributing to quality issues with recent practical ADM implementation in the UK. Strong signals are now required to switch back from the vicious cycle of increasing mistrust to an approach capable of generating public trust. Suggestions are summarised for consideration by policymakers.
Drake , A , Keller , P , Pietropaoli , I , Puri , A , Maniatis , S , Tomlinson , J , Maxwell , J , Fussey , P , Pagliari , C , Smethurst , H , Edwards , L & Blair , S W 2021 , ' Legal contestation of artificial intelligence-related decision-making in the United Kingdom : reflections for policy ' , International Review of Law, Computers & Technology , vol. Latest Articles . https://doi.org/10.1080/13600869.2021.1999075
International Review of Law, Computers & Technology
Copyright © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/ licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
DescriptionKing’s College London work for this paper was supported by the EPSRC under the Trust in HumanMachine Partnership (THuMP) project (EP/R033722/1). University of Essex work for this paper was supported by the Economic and Social Research Council under the Human Rights and Information Technology in the Era of Big Data (HRBDT) project (ES/M010236/1).
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