Cross-Border Data Sharing for Model Development: A Review of Legal Bases and Technical Alternatives

Authors

  • Jethro Kentigern Centre for Technology Law, Deeside Centre for Machine Intelligence, Chester, United Kingdom Author
  • Waltraud Windischbauer Centre for Responsible AI, Bucklige Welt Institute of Applied Artificial Intelligence, Wiener Neustadt, Austria Author
  • Marieke Hermans-Bry Centre for Technology Policy, Meetjesland Institute of Applied Artificial Intelligence, Eeklo, Belgium Author

DOI:

https://doi.org/10.5281/zenodo.22661294

Keywords:

Cross-Border Data Sharing for Model Development; Applied machine learning; Evidence synthesis; Production deployment; MLOps

Abstract

Cross-Border Data Sharing for Model Development is an area of active development in applied machine learning, with evidence
that has accumulated unevenly across its component questions. We review the current evidence base for cross-border data sharing
for model development, spanning methodological, implementation, governance, and cost dimensions, focusing on where practice
has converged and where it continues to vary, and set out the practical considerations relevant to a team considering its adoption.
We conclude that selective, context-specific use is best supported by the evidence currently available, rather than uniform
adoption or avoidance.

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Published

2026-08-25

Issue

Vol. 5 No. 2 (2026): vol 5 issue 2 2026

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