{"slug": "definitional-alignment-before-capability-alignment-a-design-science-framework", "title": "Definitional alignment before capability alignment: a Design-Science framework for adjudicating claims about AGI", "summary": "A new Design-Science framework called DAF-AGI provides five ordinal criteria and a structured governance audit to adjudicate competing claims about artificial general intelligence. Applied to the claim that current generative systems constitute AGI, the framework found the assertion certifiable only under a performance-based definition, while capability-ontology, psychometric, and skill-acquisition approaches did not certify it. The framework positions definitional sovereignty as a component of algorithmic sovereignty, enabling institutions to contest and revise imported technological categories under public accountability.", "body_md": "arXiv:2606.12713v1 Announce Type: new\nAbstract: 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.", "url": "https://wpnews.pro/news/definitional-alignment-before-capability-alignment-a-design-science-framework", "canonical_source": "https://arxiv.org/abs/2606.12713", "published_at": "2026-06-12 04:00:00+00:00", "updated_at": "2026-06-12 04:52:21.134156+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-safety", "ai-policy", "ai-research", "ai-ethics"], "entities": ["DAF-AGI"], "alternates": {"html": "https://wpnews.pro/news/definitional-alignment-before-capability-alignment-a-design-science-framework", "markdown": "https://wpnews.pro/news/definitional-alignment-before-capability-alignment-a-design-science-framework.md", "text": "https://wpnews.pro/news/definitional-alignment-before-capability-alignment-a-design-science-framework.txt", "jsonld": "https://wpnews.pro/news/definitional-alignment-before-capability-alignment-a-design-science-framework.jsonld"}}