Navigating Responsible AI: A Systematic Review of Governance Mechanisms and Future Co-Governance Scenarios
Bibliographic record
Abstract
This systematic review analyzes AI governance literature from 2010 to 2024, introducing the ”Governance Galaxy” framework that envisions AI as a potential co-governing partner with humanity by 2035. Our analysis of 75 peer-reviewed studies reveals four key themes: ethics, regulation, technology, and global coordination. We identify significant gaps between theoretical principles and practical implementation, particularly for underrepresented stakeholders. The paper makes three main contributions: First, we amplify marginalized voices (indigenous communities, SMEs, and non-Western perspectives) that are crucial for equitable governance. Second, we project three potential governance scenarios (Utopian, Dystopian and Fragmented) supported by case studies from Denmark, Canada, Saudi Arabia, and the EU-Asia dialogue. Third, we propose practical tools including regulatory sandboxes and decentralized governance structures. Our findings highlight the need for dynamic, inclusive governance approaches that balance innovation with human oversight.
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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 | 0.019 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".