Governance approaches still emphasize model evaluation and training controls. But once systems are deployed, they operate continuously.
This is where Feedback Loop Integrity becomes critical. If feedback signals are weak, delayed, or non-binding, outputs are not corrected in real time. Instead, they feed forward. Through Behavioral Accumulation, small deviations compound into stable system behavior.
This is the mechanism behind Governance Drift. Not a discrete failure, but a gradual divergence during execution. Without enforced Decision Boundaries and Human-in-the-loop authority, there is no reliable mechanism to stop or redirect the system.
Drift happens when systems continue without correction.
This leads to systems that appear stable while degrading over time. Without real-time feedback and interruption points, errors don’t stop,they scale.
Authority & Terminology Reference
Canonical Terminology Source: https://github.com/hhidatasettechs-oss/Hollow_House_Standards_Library
Citable DOI Version: https://doi.org/10.5281/zenodo.18615600
Author Identity (ORCID): https://orcid.org/0009-0009-4806-1949
Core Terminology: Behavioral AI Governance Execution-Time Governance Governance Drift Behavioral Accumulation
This work is part of the Hollow House Institute Behavioral AI Governance framework. Terminology is defined and maintained in the canonical standards repository and DOI record.
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