Public Trust in Global <scp>AI</scp> Governance Across Geopolitical Rivals
Bibliographic record
Abstract
ABSTRACT The global governance of artificial intelligence (AI) depends on coordination among national governments, international organizations, and non‐state actors. While existing research has mapped the institutional complexity of the emerging AI regime, public trust in the stakeholders involved remains underexplored. This study addresses this gap using parallel surveys in the United States and China, two leading AI powers locked in strategic rivalry. Results show that respondents in both countries express the highest levels of trust in their own government and the lowest in their geopolitical rival, with other actors such as the European Union, tech firms, and research institutes falling in between. These patterns reflect how geopolitical competition and intergroup dynamics shape public trust, posing challenges for inclusive and cooperative governance in contested global domains such as AI. At the same time, individuals who view AI as socially beneficial and who support international cooperation report higher trust across a broad set of actors, including rivals. These findings illuminate systematic patterns in public opinions that condition the political viability of global AI governance and suggest that narratives emphasizing shared benefits and collaboration may help bridge trust gaps.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".