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Reviving Purpose Limitation and Data Minimisation in Data-Driven Systems

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arxiv 2101.06203 v3 pith:MISO5ROD submitted 2021-01-15 cs.CY cs.IRcs.LG

classification cs.CYcs.IRcs.LG
keywords dataprotectionsystemsanalysisdata-drivenlimitationminimisationprinciples
verification ladder T0 review T1 audit T2 compute T3 formal
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This paper determines whether the two core data protection principles of data minimisation and purpose limitation can be meaningfully implemented in data-driven systems. While contemporary data processing practices appear to stand at odds with these principles, we demonstrate that systems could technically use much less data than they currently do. This observation is a starting point for our detailed techno-legal analysis uncovering obstacles that stand in the way of meaningful implementation and compliance as well as exemplifying unexpected trade-offs which emerge where data protection law is applied in practice. Our analysis seeks to inform debates about the impact of data protection on the development of artificial intelligence in the European Union, offering practical action points for data controllers, regulators, and researchers.

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  1. Position: EU AI Act's Research Exemptions Can Break the Publication Norms of Major AI Conferences

    cs.CY 2025-06 conditional novelty 6.0 of 10

    Publishing AI research artifacts, such as models and code on GitHub or Hugging Face, may fall outside the EU AI Act's research exceptions and trigger provider obligations.

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