Inference Authority: Controlled Governance of Authoritative Output
A governance vocabulary for safety-critical AI: designate which model or compute pathway may produce authoritative output, keep candidate models non-authoritative during shadow-mode validation, evaluate divergence validation against explicit alignment conditions, and transfer authority only within a controlled transition window governed by failover governance.
Core Technical Terminology
The following terms are used throughout this framework to describe architectural roles and governance mechanisms in safety-critical AI systems.
Scope
This Conceptual Framework defines terminology for controlled governance of inference authority in regulated and safety-critical environments. It is intended to support architecture planning, policy discussions, and auditability requirements. Implementation details (latency budgets, actuation constraints, thresholds, and safety policies) are environment-dependent.
Executive Summary
In safety-critical AI, the question is not only “is the model accurate?” but also “which pathway is allowed to control downstream behavior right now?” Many systems run multiple models (or multiple compute pathways) in parallel, but treat authority as an implicit engineering detail rather than an explicit, auditable state.
Inference Authority names and structures that missing layer: a system-level designation of which model or pathway may produce authoritative output, enforced by authority gating. Candidate pathways execute concurrently in a non-authoritative state for divergence validation, and authority is transferred only when explicit alignment conditions are satisfied within a controlled transition.
1. Why “Inference Authority” is a Distinct Governance Concept
Traditional control systems assume explicit, deterministic control paths. Safety-critical AI introduces probabilistic outputs, dynamic model updates, and multi-path inference. When outputs can influence physical systems, it becomes necessary to govern which output is allowed to act.
This framework formalizes a governance structure that introduces an explicit authority state, enforces a non-authoritative execution mode for candidates, requires divergence-based eligibility under defined alignment conditions, and constrains handover behavior through a controlled transition window suitable for audit and safety cases.
2. Definition: Inference Authority
Inference Authority is the system designation of which model, compute unit, or inference pathway is permitted to produce authoritative output that may influence operational processes (control, protection, routing, dispatch, navigation, or decision-making).
Authoritative Pathway
The inference source currently permitted to issue operationally binding outputs (the “active” or “in-control” inference path).
Non-Authoritative Pathway
A parallel inference pathway that may execute under equivalent inputs for validation, monitoring, or comparison, but is prevented from issuing authoritative outputs by authority gating.
3. Roles in a Controlled Inference-Authority Governance Framework
A controlled governance architecture typically includes:
- an authoritative inference pathway (active output)
- one or more non-authoritative candidate pathways executing in shadow mode
- a divergence validation mechanism operating over a validation interval
- explicit alignment conditions and one or more gating thresholds/policies
- failover governance (controller + policy) for authority assignment and revocation
- an output-gating capability to suppress or constrain unsafe outputs during anomalies or transitions
4. Shadow-Mode Validation and Divergence-Based Alignment
Shadow-mode validation runs a candidate model in parallel with the authoritative model using functionally equivalent inputs. Candidate outputs are evaluated but do not influence downstream behavior.
Divergence validation compares outputs over a validation interval. Eligibility for authority transfer is determined by explicit alignment conditions (e.g., stability across time, constraint satisfaction, confidence variance limits, and policy checks), rather than informal “looks good” promotion.
5. Controlled Transition Window and Authority Handover
Transferring inference authority is not a binary “switch flip.” In safety-critical environments, it is a controlled process executed within a transition window designed to reduce discontinuity and system instability.
During a transition window, authority gating may sequence handover (assign/revoke), constrain outputs, or maintain suppression via output-gating until policy criteria are satisfied.
6. Auditability and Model Provenance
In regulated environments, authority transitions must be explainable. A governance framework should log not only that inference authority changed, but why—including policy triggers, divergence metrics, alignment outcomes, and authority/transition state.
Recommended practice is a tamper-evident audit log with model provenance metadata (model identifiers, integrity/attestation context, promotion/failover events) suitable for compliance verification.
7. Relevance Across Safety-Critical Domains
The vocabulary applies wherever AI outputs can influence operational processes: utilities and critical infrastructure, industrial automation, robotics and autonomous platforms, transportation, large-scale computing, and government or defense systems.
Conclusion
Inference Authority provides a concise, repeatable vocabulary for controlled governance of authoritative output in safety-critical AI systems. By requiring an explicit authority state, non-authoritative candidate execution, divergence validation against defined alignment conditions, controlled transitions, and governance-grade auditability, it supports both engineering clarity and regulator-ready safety framing.