Saved in:
| Hovedforfatter: | |
|---|---|
| Format: | Recurso digital |
| Sprog: | engelsk |
| Udgivet: |
Zenodo
2026
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| Fag: | |
| Online adgang: | https://doi.org/10.5281/zenodo.19224347 |
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Indholdsfortegnelse:
- <p>Current AI governance discourse increasingly demands “faithful explanations” from large language models (LLMs). This requirement presumes that (1) human explanations are faithful, (2) natural language is a reliable container for cognition, and (3) post-hoc narratives can serve as admissible evidence of decision formation. None of these assumptions hold. Human cognition is reconstructive, not transparent; language is lossy and metaphorical; and LLMs inherit these properties from their training data. This note argues that explanation-centric governance is structurally incapable of producing admissible decision authority. Governance must shift from post-hoc narrative extraction to substrate-level constraints that shape intent formation, privilege activation, and admissibility before a decision exists — making governance constitutive rather than interpretive.</p>