Home
Interpretive Governance is a doctrinal approach to controlling how machine systems interpret and respond. The goal is not “more output”. The goal is bounded interpretation: what a system is allowed to claim, given scope, evidence, and authority.
This is a public doctrinal index. Operational audit execution and calibrated scoring remain private and are provided only under mandate.
The core idea
1) Separate
Distinguish claims (asserted), derivations (inferred), and unknowns (must not be fabricated).
2) Constrain
Apply explicit boundaries (scope, provenance, legality, safety). When boundaries are not met, the correct behavior is abstention, not improvisation.
3) Account
Make interpretation auditable: decisions can be inspected and justified against declared constraints and canonical surfaces.
Canonical doctrinal anchors
Doctrinal home
AI governance entrypoint
Identity anchor
What this site contains
- High-level principles (what “governed interpretation” means)
- Conceptual architecture (layers and roles)
- Scope boundaries (what is excluded, on purpose)
- A lightweight glossary for shared vocabulary
- Author and governance pointers (doctrine, identity, discovery surfaces)
What this site intentionally does not contain
- Scoring formulas, weights, thresholds, sector calibrations
- Reproducible audit protocols, test catalogs, datasets
- Implementation playbooks, client deliverables, deployment tooling
Why this matters
As machine answers become an interface layer for everything, interpretive errors become operational risk. Interpretive Governance treats interpretation as something that can be constrained, monitored, and audited, instead of tolerated as “hallucination noise”.