🤖 AI Summary
This study addresses the lack of a unified and stable definition of Artificial General Intelligence (AGI), which has led to significant disagreement in its assessment. Employing a design science research approach, the paper proposes the DAF-AGI framework, which innovatively prioritizes “definition alignment” over “capability alignment” and positions “definitional sovereignty” as a critical dimension of algorithmic sovereignty. The framework evaluates AGI definitions through five sequential criteria for adjudicative fitness and incorporates a governance audit mechanism to examine the authorship, vested interests, and certification structures underlying each definition. Validation across six prominent AGI stances—including one eliminativist perspective—reveals that only performance-oriented definitions classify current generative systems as AGI, while all others either reject this classification or remain indeterminate, thereby underscoring both the ambiguity of existing definitions and the urgent need for governance.
📝 Abstract
Claims that artificial general intelligence has already arrived and claims that it remains decades away are often defended from overlapping evidence. "AGI" lacks a single shared and stable referent and competing operationalizations can return different verdicts on the same system. This article treats that under-specification as a design and governance problem. Following Design Science Research Methodology, it develops DAF-AGI, a second-order conceptual artifact with two coupled components: five ordinal criteria for assessing the adjudicative fitness of candidate definitions and a structured governance audit of authorship, interest, certification, external verification and revision authority. The artifact is demonstrated on five prominent measurement families and one deflationary boundary position in a documented corpus and then stress-tested against a stylized strong arrival claim: that current generative systems constitute AGI because they outperform a well-educated adult on many cognitive tasks. On evidence from the cited 2024-2025 sources, the claim was certifiable only under a performance-based operationalization; capability-ontology, psychometric and skill-acquisition approaches did not certify it, the economic family remains indeterminate and the deflationary position refuses binary adjudication. The contribution is a novel integration and operationalization, not an empirical validation: independent application, inter-rater testing and author-external cases remain necessary. The paper further proposes definitional sovereignty as an enabling component of algorithmic sovereignty: the institutional capacity to contest, certify and revise imported technological categories under public accountability.