Interaction-induced knowledge narrowing risk in LLM systems

Abstract

AI governance frameworks address well-documented risks through controls on data, models, and post-deployment performance. This paper identifies a distinct, under-governed lifecycle risk arising not from model error or degradation, but from interaction itself. Termed Interaction-Induced Knowledge Narrowing (IIKN), this risk occurs when bounded context windows, ranked probabilistic synthesis, and iterative interaction dynamics systematically constrain the epistemic scope surfaced during AI system use. IIKN manifests as unobserved exclusion rather than incorrectness: AI systems may produce fluent, accurate, and aligned outputs while silently omitting plausible, defensible, and consequential alternatives. Because IIKN leaves no error signal, it routinely passes undetected by conventional assurance mechanisms. This paper provides a formal definition, differentiates IIKN from adjacent risk categories, and explains its emergence through inference-time selection and interaction path dependence. Mapping IIKN across the AI lifecycle demonstrates why late-stage review is insufficient and early controls are necessary. The paper proposes proportionate governance controls, including epistemic scope declaration, breadth-before-depth interaction patterns, coverage confirmation checkpoints, and lifecycle-aligned trigger gates, that render exclusion explicit and accountable without requiring exhaustive enumeration. Potential alignment with ISO/IEC 42,001 and the NIST AI Risk Management Framework is demonstrated, with the argument that governing what AI systems omit is as critical as governing what they produce, particularly as organizations adopt increasingly agentic systems.