Approaches to Responsible Governance of GenAI in Organizations : Peer-Reviewed and accepted in IEEE-ISTAS 2025
Bibliographic record
Abstract
The rapid evolution and integration of Generative AI (GenAI) across industries have introduced unprecedented opportunities for innovation while also presenting complex challenges around ethics, accountability, and societal impact. This white paper draws on a combination of literature review, established governance frameworks [1] - [10], and insights from industry roundtable discussions with industry experts varying in professional backgrounds and organizations. Weekly discussions with these experts have contributed valuable practical insights that have enriched the paper, ensuring that its recommendations are grounded in real-world experiences and challenges. Through an analysis of existing governance models, real-world use cases, and expert perspectives, this paper identifies core principles for integrating responsible GenAI governance into diverse organizational structures. The primary objective is to provide actionable recommendations for organizations to adopt a balanced, risk-based governance approach that allows for both innovation and oversight. Through an analysis of existing governance models, expert roundtable discussions, and real-world use cases, this paper identifies core principles for integrating responsible GenAI governance into diverse organizational structures. Findings emphasize the need for adaptable risk assessment tools, continuous monitoring practices, and cross-sector collaboration to establish trustworthy and responsible AI. These insights provide a structured foundation for organizations to align their AI initiatives with ethical, legal, and operational best practices.
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.
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 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".