Researchers Propose Framework for Defining Artificial General Intelligence to Resolve Competing Claims
Computer scientists have developed DAF-AGI, a governance framework designed to adjudicate competing definitions of artificial general intelligence (AGI) and evaluate claims about whether it has been achieved. The framework applies five ordinal criteria and a structured audit process to assess the fitness of different AGI definitions, addressing the problem that "AGI" currently lacks a single shared meaning. This matters because conflicting definitions allow researchers to reach opposite conclusions about the same systems, complicating policy and governance decisions.
Researchers at arXiv have introduced DAF-AGI, a conceptual framework following Design Science Research Methodology to resolve the fundamental problem that "AGI" lacks a stable, shared definition. The framework consists of two components: five ordinal criteria for assessing how well candidate definitions work, and a governance audit examining authorship, interests, certification, external verification, and revision authority. When tested against prominent measurement approaches and a claim that current generative AI systems constitute AGI, the framework found the claim certifiable only under performance-based definitions; capability-ontology, psychometric, and skill-acquisition approaches did not support it, while economic measures remained indeterminate. The authors propose "definitional sovereignty"—the institutional capacity to contest and revise technological categories under public accountability—as essential to algorithmic governance. The work is presented as a novel conceptual contribution requiring independent testing and external validation rather than as empirical proof.
What's missing
The paper does not provide empirical validation of the framework itself; the authors explicitly note that independent application, inter-rater testing, and author-external cases remain necessary. The practical feasibility and adoption prospects of the governance audit process across different institutional contexts are not addressed.
What different sources said
- arXiv cs.AICenter
Definitional alignment before capability alignment: a Design-Science framework for adjudicating claims about AGI
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