The Explanation Fallacy: Why "Faithful Explanations" Cannot Serve as a Governance Primitive for AI Systems
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| Format: | Recurso digital |
| Sprache: | Englisch |
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2026
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| _version_ | 1866901088392708096 |
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| author | Truong, Narnaiezzsshaa |
| author_facet | Truong, Narnaiezzsshaa |
| contents | <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> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19224347 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | The Explanation Fallacy: Why "Faithful Explanations" Cannot Serve as a Governance Primitive for AI Systems Truong, Narnaiezzsshaa faithful explanations, AI accountability, substrate governance, post-hoc rationalization, admissibility governance primitives, confabulation, LLM explainability, constitutive governanc <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> |
| title | The Explanation Fallacy: Why "Faithful Explanations" Cannot Serve as a Governance Primitive for AI Systems |
| topic | faithful explanations, AI accountability, substrate governance, post-hoc rationalization, admissibility governance primitives, confabulation, LLM explainability, constitutive governanc |
| url | https://doi.org/10.5281/zenodo.19224347 |