Definitional alignment before capability alignment: a Design-Science framework for adjudicating claims about AGI
J. E. Aguilera Briones
Why It Matters
What makes this one worth your time
This work is relevant for researchers and engineers as it provides a structured approach to navigate the complex landscape of AGI definitions, which is crucial for effective communication and governance in AI development.
A framework to clarify AGI definitions and governance amidst conflicting claims.
Summary
The paper proposes a Design-Science framework called DAF-AGI to address the definitional ambiguity surrounding artificial general intelligence (AGI) by developing criteria for assessing definitions and a governance audit process.
Key contributions
- Development of a second-order conceptual artifact (DAF-AGI) for adjudicating AGI definitions.
- Establishment of five ordinal criteria for assessing definitions of AGI.
- Creation of a structured governance audit addressing authorship, interest, and certification.
Notable insights
- The framework emphasizes the importance of definitional sovereignty in the context of algorithmic governance.
- It highlights the inadequacy of performance-based operationalizations in certifying AGI claims.
Possible limitations
- Not stated in the abstract.
Abstract
arXiv:2606.12713v1 Announce Type: new 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.