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Diplomatic relationship-building in the age of generative AI: the European Union and China

Abstract:
This paper examines the impact of generative AI on international diplomacy through the lens of EU–China diplomatic relationship-building. The first section introduces the broader context of AI’s geopolitical impact by distinguishing two different models—the European and Chinese models of regulating and implementing generative AI. The second discuss or explain how the two AI models contrast one another. The third section of the paper focuses on discussing Generative AI and its possible implications for EU–China relations, the extent to which their efforts are likely to strain or facilitate diplomatic relationship-building. The fourth section takes the analysis forward by examining how generative AI could be an unexpected enabling force for EU–China relationship-building. This paper purports that the distinctive EU and Chinese models of generative AI too often belie the opportunities that could potentially enable the EU and China to build their relationship, since generative AI raises shared concerns for the EU and China, and utilising generative AI could make their communications become more efficient, the EU and China may come to reach some kind of shared framework for generative AI development and governance; this could lead to productive talks in other domains such as the trade deficit issue plaguing the EU–China relations or more sensitive cross-strait issues; moreover, in the realm of public diplomacy generative AI could facilitate the EU and China’s public diplomatic efforts towards each other.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1057/s41254-023-00321-6

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Institution:
University of Oxford
Division:
SSD
Department:
Politics & Int Relations
Role:
Author
ORCID:
0000-0002-2428-1455


Publisher:
Palgrave Macmillan
Journal:
Place Branding and Public Diplomacy More from this journal
Volume:
21
Issue:
3
Pages:
334-338
Publication date:
2024-02-05
Acceptance date:
2023-12-17
DOI:
EISSN:
1751-8059
ISSN:
1751-8040


Language:
English
Keywords:
Pubs id:
1676877
Local pid:
pubs:1676877
Source identifiers:
3290939
Deposit date:
2025-09-17
ARK identifier:
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