Abstract:Generative AI research increasingly confronts a shared problem: systems must sustain yet govern their own generative activity when uncertainty is high, evidence is missing, or context is insufficient. This position paper argues that metacognition should become the scientific framework for bounded and effective self governance in generative AI, where output generation is properly evaluated together with the capacities through which generative systems navigate and regulate their own activity. We advance this position by showing that bounded and effective AI self-governance requires metacognitive alignment across computational, algorithmic, and ecological levels. At the computational level, metacognition specifies the meta-level functions a system is meant to serve, such as monitoring, evaluation, control, and adaptation. At the algorithmic level, these functions are realized through procedures such as elicitation, iteration, and modularization. At the ecological level, metacognitive signals become meaningful, actionable, and accountable within the interface, workflow, and accountability arrangements. Metacognition thus makes it possible to conceive generative AI as both capable and well-governed, rather than treating capability and governance as competing aims.
| Comments: | 16 pages, 1 figure, 1 table |
| Subjects: | Neurons and Cognition (q-bio.NC); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC); Systems and Control (eess.SY) |
| Cite as: | arXiv:2605.23981 [q-bio.NC] |
| (or arXiv:2605.23981v1 [q-bio.NC] for this version) | |
| https://doi.org/10.48550/arXiv.2605.23981 arXiv-issued DOI via DataCite |
From: Eugene Yu Ji [view email]
[v1]
Wed, 13 May 2026 23:40:56 UTC (1,285 KB)
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