Cross-Border Data Transfers and AI Model Training: Adequacy, Consent, and Standard Clauses
DOI:
https://doi.org/10.54536/ajdsai.v2i1.7372Keywords:
Adequacy Decisions, Artificial Intelligence (AI), Consent, Cross Border Data Transfers, Data Protection, GDPR, Model Training Standard Contractual Clauses (SCCs)Abstract
Cross border transfers of personal and non personal data underpin modern digital economies and the development of artificial intelligence (AI) systems. AI model training often requires large and diverse datasets, which leads to frequent transfers of data across jurisdictions. The European Union (EU) General Data Protection Regulation (GDPR) provides several legal bases for such transfers, including adequacy decisions, standard contractual clauses and derogations based on consent. This paper examines how these mechanisms interact with AI model training and analyses their effectiveness in safeguarding data protection while enabling innovation. The study uses doctrinal analysis of EU legal texts, case law and policy documents up to 2018, supplemented by economic literature on data flows, to evaluate the adequacy, consent and standard clause regimes in the context of AI. The results highlight tensions between data protection and the data hungry nature of AI, the challenges of obtaining meaningful consent, and the limitations of contractual safeguards. The paper concludes with recommendations for policymakers and AI developers to enhance cross border data governance and ensure responsible AI model training.
References
Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., & Mané, D. (2016). Concrete problems in AI safety. arXiv. https://doi.org/10.48550/arXiv.1606.06565
Angwin, J., Larson, J., Mattu, S., & Kirchner, L. (2016, May 23). Machine bias. ProPublica. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
Barocas, S., & Selbst, A. D. (2016). Big data’s disparate impact. California Law Review, 104, 671–732. https://doi.org/10.2139/ssrn.2477899
Borenstein, J., Grodzinsky, F. S., Howard, A., Miller, K. W., & Wolf, M. J. (2021). AI ethics: A long history and a recent burst of attention. Computer, 54(1), 96–102. https://doi.org/10.1109/MC.2020.3034950
Bostrom, N., & Yudkowsky, E. (2014). The ethics of artificial intelligence. In K. Frankish & W. M. Ramsey (Eds.), The Cambridge handbook of artificial intelligence (pp. 316–334). Cambridge University Press. https://doi.org/10.1017/CBO9781139046855.020
Brundage, M., Avin, S., Clark, J., Toner, H., Eckersley, P., Garfinkel, B., Dafoe, A., Scharre, P., Zeitzoff, T., Filar, B., Anderson, H., Roff, H., Allen, G. C., Steinhardt, J., Flynn, C., Ó hÉigeartaigh, S., Beard, S. J., Belfield, H., Farquhar, S., . . . Amodei, D. (2018). The malicious use of artificial intelligence: Forecasting, prevention, and mitigation. arXiv. https://doi.org/10.48550/arXiv.1802.07228
Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. Proceedings of Machine Learning Research, 81, 77–91. https://proceedings.mlr.press/v81/buolamwini18a.html
Cath, C., Wachter, S., Mittelstadt, B. D., Taddeo, M., & Floridi, L. (2018). Artificial intelligence and the ‘good society’: The US, EU, and UK approach. Science and Engineering Ethics, 24(2), 505–528. https://doi.org/10.1007/s11948-017-9901-7
Dignum, V. (2018). Ethics in artificial intelligence: Introduction to the special issue. Ethics and Information Technology, 20(1), 1–3. https://doi.org/10.1007/s10676-018-9450-z
Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv. https://doi.org/10.48550/arXiv.1702.08608
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., & Vayena, E. (2018). AI4People: An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5
Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254–280. https://doi.org/10.1016/j.techfore.2016.08.019
Russell, S., Dewey, D., & Tegmark, M. (2015). Research priorities for robust and beneficial artificial intelligence. AI Magazine, 36(4), 105–114. https://doi.org/10.1609/aimag.v36i4.2577
St. Martin, G. (2018, February 1). Northeastern, Gallup release findings from national AI survey. Northeastern Global News. https://news.northeastern.edu/2018/02/01/northeastern-gallup-release-findings-from-national-ai-survey/
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Idara Sebastian Bassey

This work is licensed under a Creative Commons Attribution 4.0 International License.