Navigating Responsible AI: A Systematic Review of Governance Mechanisms and Future Co-Governance Scenarios
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it