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Rule-based versus AI-driven benefits allocation: GDPR and AIA legal implications and challenges for automation in public social security administration
Umeå University, Faculty of Social Sciences, Department of Law.ORCID iD: 0000-0002-2768-9149
2024 (English)In: Information & communications technology law, ISSN 1360-0834, E-ISSN 1469-8404, Vol. 33, no 2, p. 222-246Article in journal (Refereed) Published
Abstract [en]

This article focuses on the legal implications of the growing reliance on automated systems in public administrations, using the example of social security benefits administration. It specifically addresses the deployment of automated systems for decisions on benefits eligibility within the frameworks of the General Data Protection Regulation (GDPR) and the Artificial Intelligence Act (AIA). It compares how these two legal frameworks, each targeting different regulatory objects (personal data versus AI systems) and employing different protective measures, apply for two common system types: rule-based systems utilised for making fully automated decisions on eligibility, and machine learning AI systems utilised for assisting case administrators in their decision-making. It concludes on the combined impact that the GDPR and the AIA will have on each of these types of systems, as well as on differences in how these instruments determines the basic legality of utilising such systems within social security administration.

Place, publisher, year, edition, pages
Routledge, 2024. Vol. 33, no 2, p. 222-246
Keywords [en]
Automated decision-making, GDPR, Artificial Intelligence Act, social security administration, public administration
National Category
Law (excluding Law and Society) Other Legal Research Criminology
Research subject
Law
Identifiers
URN: urn:nbn:se:umu:diva-224152DOI: 10.1080/13600834.2024.2349835ISI: 001216905500001Scopus ID: 2-s2.0-85192517164OAI: oai:DiVA.org:umu-224152DiVA, id: diva2:1857015
Funder
Swedish Research Council, 2020-02278Available from: 2024-05-09 Created: 2024-05-09 Last updated: 2025-02-20Bibliographically approved

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Enqvist, Lena

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