Definitional alignment before capability alignment: a Design-Science framework for adjudicating claims about AGI
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 fitn
The proliferation of varying AI capabilities and the increasing public discourse around advanced AI necessitate a more robust framework for defining and evaluating AGI, especially as systems become more sophisticated.
Establishing a clearer definition and set of assessment criteria for AGI is crucial for policy, investment, and ethical considerations, preventing premature claims or underestimations of its arrival.
The proposed DAF-AGI framework offers a structured, scientific method to adjudicate claims about AGI, shifting the conversation from speculative debate to empirical evaluation based on ordinal criteria.
- · AI researchers
- · Policymakers
- · Ethicists
- · AI Governance bodies
- · Sensationalist media
- · Companies making unsubstantiated AGI claims
- · Investors relying on hype
The DAF-AGI framework provides a consistent methodology for assessing the capabilities of advanced AI systems.
This improved clarity around AGI's definition could influence regulatory approaches and funding priorities for AI development.
A shared understanding of AGI's arrival might accelerate or decelerate strategic national investments in AI, impacting global technological leadership.
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Read at arXiv cs.AI