Canonical Governance Terminology for Behavioral AI Systems (HHI Series Overview)

This publication provides an overview and structural guide to the Hollow House Institute (HHI) Canonical Governance Terminology Series, a layered, versioned set of citable definitions for behavioral AI governance. The series establishes stable terminology for execution-time governance, authority assignment, escalation mechanics, and longitudinal failure dynamics in human, organizational, and AI-mediated systems. Each component is published as an independent, immutable DOI to preserve semantic stability, auditability, and citation precision. The canonical series currently consists of: • Canonical Terms for Behavioral AI Governance (v1.0.0) — Behavioral foundations, including drift, reliance formation, and governance as infrastructure. • Canonical Authority and Escalation Terms for Behavioral AI Governance (v1.1.0) — Execution-time authority, decision boundaries, stop authority, and escalation mechanics. • Canonical Longitudinal and Failure Terms for Behavioral AI Governance (v1.2.0) — Longitudinal risk, behavioral accumulation, governance lag, and failure dynamics. This Series Overview does not redefine any terms. It serves as a navigational, interpretive, and citation anchor for the glossary as a whole. Citation guidance: • Cite the specific versioned DOI when referencing individual terms. • Cite this Series Overview when referencing the HHI governance terminology framework in aggregate. The canonical DOIs are the authoritative reference layer. The Hollow House Standards Library serves as a version-controlled downstream implementation surface.