Defining AI Models and AI Systems: A Framework to Resolve the Boundary Problem
This work addresses a foundational problem for regulators and AI practitioners by providing definitions to resolve ambiguities in assigning obligations under laws like the EU AI Act, though it is incremental as it builds on existing frameworks.
The paper tackles the lack of clear definitions for 'AI model' and 'AI system' in emerging regulations by analyzing 896 academic papers and 80+ documents, finding that existing definitions are ambiguous and derived from evolving OECD frameworks. It proposes conceptual and operational definitions where models consist of trained parameters and architecture, while systems include the model plus additional components like interfaces, and discusses implications for regulatory implementation and responsibility allocation.
Emerging AI regulations assign distinct obligations to different actors along the AI value chain (e.g., the EU AI Act distinguishes providers and deployers for both AI models and AI systems), yet the foundational terms "AI model" and "AI system" lack clear, consistent definitions. Through a systematic review of 896 academic papers and a manual review of over 80 regulatory, standards, and technical or policy documents, we analyze existing definitions from multiple conceptual perspectives. We then trace definitional lineages and paradigm shifts over time, finding that most standards and regulatory definitions derive from the OECD's frameworks, which evolved in ways that compounded rather than resolved conceptual ambiguities. The ambiguity of the boundary between an AI model and an AI system creates practical difficulties in determining obligations for different actors, and raises questions on whether certain modifications performed are specific to the model as opposed to the non-model system components. We propose conceptual definitions grounded in the nature of models and systems and the relationship between them, then develop operational definitions for contemporary neural network-based machine-learning AI: models consist of trained parameters and architecture, while systems consist of the model plus additional components including an interface for processing inputs and outputs. Finally, we discuss implications for regulatory implementation and examine how our definitions contribute to resolving ambiguities in allocating responsibilities across the AI value chain, in both theoretical scenarios and case studies involving real-world incidents.