Insider action research in an AI startup identifies three patterns of how practitioners view regulatory requirements and proposes internal expert collaboration as a way to turn external governance rules into shared, practical ownership.
et al.: AI governance: a systematic literature review
3 Pith papers cite this work, alongside 143 external citations. Polarity classification is still indexing.
3
Pith papers citing it
143
external citations · OpenAlex
citation-role summary
background 1
citation-polarity summary
years
2026 3roles
background 1polarities
background 1representative citing papers
AI to Learn 2.0 is a deliverable-oriented framework with a seven-dimension maturity rubric and capability-evidence ladder that permits opaque AI for exploration but requires final outputs to be auditable, transferable, and supported by human-attributable evidence.
A systematic review of 21 peer-reviewed papers identifies two classification approaches for agentic AI governance (autonomous goal-pursuit and moral agency), synthesizes privacy concerns, and evaluates Singapore's Model AI Governance Framework as the first dedicated agentic AI governance model.
citing papers explorer
-
Engaged AI Governance: Addressing the Last Mile Challenge Through Internal Expert Collaboration
Insider action research in an AI startup identifies three patterns of how practitioners view regulatory requirements and proposes internal expert collaboration as a way to turn external governance rules into shared, practical ownership.
-
AI to Learn 2.0: A Deliverable-Oriented Governance Framework and Maturity Rubric for Opaque AI in Learning-Intensive Domains
AI to Learn 2.0 is a deliverable-oriented framework with a seven-dimension maturity rubric and capability-evidence ladder that permits opaque AI for exploration but requires final outputs to be auditable, transferable, and supported by human-attributable evidence.
-
Towards Agentic AI Governance: A Preliminary Assessment
A systematic review of 21 peer-reviewed papers identifies two classification approaches for agentic AI governance (autonomous goal-pursuit and moral agency), synthesizes privacy concerns, and evaluates Singapore's Model AI Governance Framework as the first dedicated agentic AI governance model.