Updating purpose limitation for AI: a normative approach from law and philosophy

Rainer Mühlhoff & Hannah Ruschemeier

International Journal of Law and Information Technology2025https://doi.org/10.1093/ijlit/eaaf003article
ABDC A
Weight
0.44

Abstract

The purpose limitation principle goes beyond the protection of the individual data subjects: it aims to ensure transparency, fairness and its exception for privileged purposes. However, in the current reality of powerful AI models, purpose limitation is often impossible to enforce and is thus structurally undermined. This paper addresses a critical regulatory gap in EU digital legislation: the risk of secondary use of trained models and anonymised training datasets. Anonymised training data, as well as AI models trained from this data, pose the threat of being freely reused in potentially harmful contexts such as insurance risk scoring and automated job applicant screening. We propose shifting the focus of purpose limitation from data processing to AI model regulation. This approach mandates that those training AI models define the intended purpose and restrict the use of the model solely to this stated purpose.

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https://doi.org/https://doi.org/10.1093/ijlit/eaaf003

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@article{rainer2025,
  title        = {{Updating purpose limitation for AI: a normative approach from law and philosophy}},
  author       = {Rainer Mühlhoff & Hannah Ruschemeier},
  journal      = {International Journal of Law and Information Technology},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1093/ijlit/eaaf003},
}

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Updating purpose limitation for AI: a normative approach from law and philosophy

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Evidence weight

0.44

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.32 × 0.4 = 0.13
M · momentum0.57 × 0.15 = 0.09
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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