Chapter 03 · Data Substrate
Data Substrate as a reviewable surface.
AI foundations depend on data context: provenance, consent, representativeness, quality, missingness, labeling, refresh cycle, access control, and retention.
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Data Substrate chapter.
FoundationsAI foundations depend on data context: provenance, consent, representativeness, quality, missingness, labeling, refresh cycle, access control, and retention.
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What this page maps.
operating contentData meaning
Records what data represents, where it came from, and what it excludes.
Quality limits
Tracks bias, missingness, shift, leakage, and subgroup performance concerns.
Record layer
Connects datasets, model inputs, outputs, review notes, and final decisions.
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Governance questions.
review logicWhat decision or record does this data substrate surface influence, and who owns that decision?
Which evidence is needed before routine use in Foundations, and where is it retained?
What signal triggers review, restriction, escalation, or retirement?
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Evidence-ready minimum record.
iFeed useOwnerNamed operational, clinical, technical, and governance owners.
UseClear intended use, user group, workflow point, and excluded use.
RiskRisk tier, rationale, residual risks, controls, and escalation route.
EvidenceSource claims, validation basis, limitations, approval decision, and review date.
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Source anchors and claim boundary.
official firstThese anchors support the source layer for this page. iFeed interpretation remains separate from source facts and does not replace legal, regulatory, clinical, or product-specific advice.