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.
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