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The Right to Be Forgotten Meets Machine Learning: Evaluating the Legal Feasibility of Unlearning Methods
Department of Law, Stockholm University, Stockholm, Sweden.
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Information Science and Engineering.ORCID iD: 0000-0002-0036-9049
2025 (English)In: Artificial Intelligence and the Rule of Law: The Age of Legal Tech and Digital Governance in A Fractured Digital World / [ed] Armando Aliu, Springer Nature , 2025, p. 131-170Chapter in book (Other academic)
Abstract [en]

The rapid advancement of machine learning (ML) technology is exposing a critical mismatch between the pace of technological advancement and the capacity of existing legal frameworks to effectively regulate it. As ML technologies advance, driving major changes across all sectors of society, traditional regulatory mechanisms—such as notice-and-comment rulemaking, legislation, and judicial review—are increasingly inadequate to address its unique and complex challenges. These established legal tools were designed for a slower, more predictable regulatory landscape, and they now struggle to respond to the ethical, privacy, and accountability questions that ML raises.

Place, publisher, year, edition, pages
Springer Nature , 2025. p. 131-170
National Category
Law
Identifiers
URN: urn:nbn:se:kth:diva-375204DOI: 10.1007/978-3-031-97389-5_6Scopus ID: 2-s2.0-105025628028OAI: oai:DiVA.org:kth-375204DiVA, id: diva2:2026577
Note

Part of ISBN 9783031973888, 9783031973895

QC 20260109

Available from: 2026-01-09 Created: 2026-01-09 Last updated: 2026-01-09Bibliographically approved

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Oechtering, Tobias J.

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