Constructing an Ethical Normative System for International Trade towards Trustworthy Artificial Intelligence
Bibliographic record
Abstract
This study proposes a comprehensive ethical framework to govern the development and deployment of artificial intelligence (AI) in international trade. The framework integrates insights from multi-stakeholder engagement, risk assessments, and existing ethical guidelines. It establishes five core ethical principles: fairness, privacy, transparency, accountability, and human values promotion. Operational guidelines and processes are provided to operationalize these principles across the AI system lifecycle, including risk mitigation protocols, impact assessments, stakeholder governance mechanisms, and capacity building provisions. The framework aims to foster responsible and trustworthy AI applications in international trade while addressing potential challenges such as balancing interests, cultural variations, resource constraints, and technological evolution. By proactively addressing ethical considerations, the framework represents a crucial step towards unlocking AI's transformative potential in international trade while safeguarding fundamental ethical tenets and societal interests.
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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 | 0.069 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.009 | 0.044 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".