Operationalising the Definition of General Purpose AI Systems: Assessing Four Approaches
This work addresses the regulatory problem of distinguishing between AI system types for EU policymakers, representing an incremental step in clarifying definitions for legal frameworks.
The paper tackles the challenge of defining General Purpose AI Systems (GPAIS) under the EU AI Act by operationalizing differences through 'distinct tasks' and evaluating four approaches (quantity, performance, adaptability, emergence) to classify systems as fixed-purpose or GPAIS, suggesting these as a starting point for stakeholders.
The European Union's Artificial Intelligence (AI) Act is set to be a landmark legal instrument for regulating AI technology. While stakeholders have primarily focused on the governance of fixed purpose AI applications (also known as narrow AI), more attention is required to understand the nature of highly and broadly capable systems. As of the beginning of 2023, several definitions for General Purpose AI Systems (GPAIS) exist in relation to the AI Act, attempting to distinguish between systems with and without a fixed purpose. In this article, we operationalise these differences through the concept of "distinct tasks" and examine four approaches (quantity, performance, adaptability, and emergence) to determine whether an AI system should be classified as a GPAIS. We suggest that EU stakeholders use the four approaches as a starting point to discriminate between fixed-purpose and GPAIS.