AI Diplomacy: an infrastructural framework
Bibliographic record
Abstract
Artificial intelligence (AI) is rapidly becoming a global infrastructure for cognition, coordination, and coercion. Yet the policy tools we are using to govern its cross-border impacts: \emph{science diplomacy, tech diplomacy, and classical arms control,} were designed for a different technological era. They treat AI either as a sectoral topic, a bundle of discrete risks, or a generic ``emerging technology'' to be slotted into existing regimes. This paper argues that such inherited tools are necessary but insufficient. It develops a framework for \emph{AI diplomacy} that is explicitly tailored to AI's infrastructural character. The core contribution is a four-by-four framework organised around four \emph{layers} of AI governance targets (resources, systems, applications, epistemics) and four \emph{diplomatic functions} (coordination, containment, composition, contestation). The framework surfaces three hard problems that define the frontier of AI diplomacy: (a) epistemic governance and the challenge of building something like an ``IPCC for AI'' in a fast-moving, private-sector-led domain; (b) the status of AI systems as \emph{quasi-actors} and the problem of legitimate delegation of diplomatic and coercive discretion; and (c) justice and composition for the Global South in a world where compute, data, and talent are highly concentrated. These are not simply descriptive gaps; they constitute a research and policy agenda. The final section briefly applies the framework to Canada as an illustrative case. As a middle power with strong AI research hubs, a bilingual and multicultural society, and a tradition of functional multilateralism, Canada is well placed to pursue an AI diplomacy agenda that leverages coordination and composition rather than relying on hard power. The Canadian case illustrates how the framework can guide the strategic positioning of actors that are neither AI superpowers nor rule-takers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.034 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".