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Interaction-Induced Knowledge Narrowing Risk in LLM Systems
Published review in Discover Artificial Intelligence
Author
Dale Rutherford and Ningning WuPublished
September 17, 2026Source
Discover Artificial Intelligence · Springer Nature
Abstract / overview
This conceptual review examines how finite context, ranked synthesis, and repeated interaction can limit the alternatives surfaced by an LLM while its answers remain fluent and accurate. It frames that exclusion as a governance concern and proposes scope declarations, breadth-before-depth interaction, and coverage checkpoints. The work identifies empirical validation of prevalence and control effectiveness as future research.
Keywords
Knowledge narrowing · Human–AI interaction · Epistemic scope · AI governance
Research focus
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Risk framing — Examines omission and narrowing alongside conventional model errors.
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Controls — Proposes scope, breadth, and coverage checks for accountable interaction.
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Research status — Conceptual and governance-oriented; further empirical validation is needed.
Full text access
Public document
CC BY-NC-ND 4.0. Consult the publisher’s article license and terms.
Suggested citation
Rutherford, D., & Wu, N. (2026). Interaction-induced knowledge narrowing risk in LLM systems. Discover Artificial Intelligence, 6, Article 1153. https://doi.org/10.1007/s44163-026-02101-6
Publication details
Document type
Journal article · ReviewAuthor / source
Dale Rutherford and Ningning WuPublication date
September 17, 2026Source / venue
Discover Artificial Intelligence · Springer NatureStatus
Published journal review · Volume 6, article 1153Format & length
PDF · 23 pagesIdentifier
10.1007/s44163-026-02101-6Access
Public PDF; other formats and editable originals by requestLicense / terms
CC BY-NC-ND 4.0. Consult the publisher’s article license and terms.
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