Guiding the Future: Boardroom Governance in the Age of Artificial Intelligence
Bibliographic record
Abstract
As GenAI and other advanced technologies become increasingly embedded in business operations, boards of directors face new demands in strategic oversight, risk, ethics, and organizational change. Despite these challenges, scholarly research on board-level AI governance remains sparse. In parallel, many boards struggle to translate high-level principles and emerging academic recommendations into actionable strategies. This panel brings together scholars and board members from public and private organizations with expertise in information systems (IS) and digital transformation. Panelists represent diverse experiences and viewpoints, creating space to explore tensions and dilemmas in governing AI at the board level. Discussions will highlight real-world governance dilemmas, strategies for addressing them, lessons learned, and unresolved questions emerging from boardroom practice. By fostering critical debate, the panel aims to deepen understanding of the complexities of board-level AI governance and shape a research agenda that supports practical, ethical, and effective oversight in the age of intelligent technologies.
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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 | 0.037 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.024 |
| Scholarly communication | 0.024 | 0.017 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".