KD-014 · Governance stack

Source Authority Model

Show how conflicts between human, system, and AI-derived information are resolved before a recommendation is trusted.

Evidence level: Verified
Executive audience: Data leader, product leader, operations leader
Publication status: Published after human review

Inspect the operating model

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Text alternative and Mermaid source

Source Authority Model. Show how conflicts between human, system, and AI-derived information are resolved before a recommendation is trusted. Business problem: Conflicting facts become operational risk when no authority model explains which source may resolve a conflict. Claim boundary: Code-backed RachelOS implementation pattern. It does not establish revenue, adoption, conversion, healthcare deployment, or a result outside the demonstrated implementation.

flowchart TB
  H[Human-confirmed fact] --> F[Resolved fact]
  S[Governed system fact] --> F
  AI[AI-derived suggestion] --> R[Review required]
  R --> F
  F --> N[State and recommendation]

Why this matters

Conflicting facts become operational risk when no authority model explains which source may resolve a conflict.

Executive decision

Assign source precedence, preserve provenance, and distinguish fact from interpretation before the system recommends action.

Claim boundary

Code-backed RachelOS implementation pattern. It does not establish revenue, adoption, conversion, healthcare deployment, or a result outside the demonstrated implementation.

Evidence summary

rachelos:ev-rachelos-relationship-memory

Knowledge that lived in one person's head became a persistent, timeline-based per-relationship snapshot that survives outside any individual.

Boundary: Code-backed memory layer. No outcome metric implied.

rachelos:ev-rachelos-human-approved-ai

AI drafts and recommendations wait in a dedicated operator queue for human review before anything is sent.

Boundary: Enforced in code, not promised. No send-rate metric.