Metacognition Should Be the Scientific Framework for Bounded and Effective Self-Governance in Generative AI
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866910249851551744 |
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| author | Ji, Eugene Yu Grossmann, Igor Karimi, Amir-Hossein |
| author_facet | Ji, Eugene Yu Grossmann, Igor Karimi, Amir-Hossein |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_23981 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Metacognition Should Be the Scientific Framework for Bounded and Effective Self-Governance in Generative AI Ji, Eugene Yu Grossmann, Igor Karimi, Amir-Hossein Neurons and Cognition Artificial Intelligence Computers and Society Human-Computer Interaction Systems and Control 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. |
| title | Metacognition Should Be the Scientific Framework for Bounded and Effective Self-Governance in Generative AI |
| topic | Neurons and Cognition Artificial Intelligence Computers and Society Human-Computer Interaction Systems and Control |
| url | https://arxiv.org/abs/2605.23981 |