Artificial Intelligence Governance For Businesses

TLDR

While artificial intelligence (AI) governance is thoroughly discussed on philosophical, societal, and regulatory levels, few works target companies. The authors derive a conceptual framework for AI governance in businesses from existing literature. The framework decomposes AI governance into data, machine learning models, and AI systems, each examined across who, what, and how dimensions. The decomposition facilitates evolving governance structures and introduces business‑specific aspects such as data value measurement and new AI governance roles.

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.