What question did this study set out to answer?

The aim is to establish a framework that integrates traceable human judgment into AI-native professional systems to enhance accountability and governance.

June 23, 2026Open Access

Frame Infrastructure for AI, Vol. 3: Expert Oversight Network — Traceable Human Judgment for AI-Native Professional Systems

Key Points

The aim is to establish a framework that integrates traceable human judgment into AI-native professional systems to enhance accountability and governance.
Development of the Expert Oversight Network (EON) as part of the Frame Infrastructure for AI series.
Integration of licensed, auditable, risk-calibrated expert judgment into machine-native memory.
Mechanisms for claim-directed review, risk-calibrated routing, and conflict preservation are discussed.
EON provides a persistent, attributable framework for human oversight, enhancing the traceability of expert judgment.
Audit records from EON allow for detailed incident reconstruction, ensuring accountability in high-risk domains.
The proposed architecture maintains different deployment thresholds across various professional fields, ensuring tailored yet consistent oversight.

Abstract

Frame Infrastructure for AI, Vol. 3: Expert Oversight Network — Traceable Human Judgment for AI-Native Professional Systems Civilization Physics — Series: Frame Infrastructure for AI This article develops the Expert Oversight Network as the professional verification layer of the Frame Infrastructure for AI series. Vol. 1 defined machine-native memory as source-anchored structural persistence. Vol. 2 defined AI safety as traceable responsibility rather than symbolic safety. Vol. 3 adds the missing human layer: licensed, auditable, risk-calibrated expert judgment integrated into machine-native memory, liability allocation, and insurance-ready deployment. The central claim is that “human oversight” only matters when human judgment becomes traceable. In high-risk professional systems, review cannot remain an informal afterthought or a vague assurance label. It must attach to claims, sources, conflicts, updates, review scope, expert credentials, sign-off authority, and audit records. EON turns expert judgment into a persistent, attributable object inside the system. The article develops this argument through several linked mechanisms: Expert oversight must be bound to machine-native memory so that review attaches to claims, sources, conflicts, and state transitions rather than to a free-floating answer. Risk-calibrated routing determines whether outputs are released immediately, sampled, provisionally reviewed, held for expert review, or blocked until explicit sign-off. Claim-directed review allows experts to confirm, correct, reject, or mark specific propositions as outside scope. Disagreement is preserved as a first-class object through conflict records rather than being averaged away or hidden behind false consensus. Audit records make review status, override authority, sign-off history, and failure location available for incident reconstruction, procurement, regulation, and insurance. Professional deployment requires different thresholds across medicine, law, finance, and public administration while preserving the same underlying architecture. This article reframes expert oversight from ceremonial “human in the loop” language into a concrete verification system. Expert judgment strengthens AI governance only when it is bound to provenance, written into memory, exposed through review states, and tied to responsibility architecture. EON therefore supplies the professional layer that makes AI-native systems more deployable, contractable, auditable, and insurable in high-stakes domains. Keywords: Expert Oversight Network, EON, AI governance, human oversight, traceable human judgment, machine-native memory, professional verification, claim-level review, audit logs, liability allocation, AI insurance, provenance, review states, high-risk AI, Frame Infrastructure for AI

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