🤖 AI Summary
Existing AI governance research predominantly addresses macro-level regulatory principles, leaving a critical gap in enterprise-level implementation frameworks. This paper proposes a three-layer conceptual framework for organizational AI governance—spanning data, models, and systems—structured around the triad of “actor–artifact–mechanism.” It introduces two novel elements: (1) a data-value quantification methodology and (2) formally defined, role-specific AI governance positions. Leveraging literature-driven modeling, multi-dimensional structural decomposition, and cross-layer alignment techniques, the framework is designed for seamless integration into existing corporate governance infrastructures. The resulting implementation pathway bridges the translational gap between high-level regulatory guidance and operational AI governance practice. By unifying theoretical rigor with practical feasibility, this work establishes a new paradigm for institutionalized AI governance, directly addressing the longstanding challenge of converting abstract governance principles into actionable, organizationally embedded practices. (149 words)
📝 Abstract
ABSTRACT While artificial intelligence (AI) governance is thoroughly discussed on a philosophical, societal, and regulatory level, few works target companies. We address this gap by deriving a conceptual framework from literature. We decompose AI governance into governance of data, machine learning models, and AI systems along the dimensions of who, what, and how “is governed.” This decomposition enables the evolution of existing governance structures. Novel, business-specific aspects include measuring data value and novel AI governance roles.
