论文核心亮点的
Governance & Audit Implications。

这一节的目标不是“讨论政策”，而是把你的方法论提升为“可执行的治理工具”。

7. Governance and Audit Implications

From Interpretive AI Ethics to Executable Semantic Governance

7.1 Motivation: The Governance Gap

Current AI governance frameworks predominantly focus on:

• model scale and capability thresholds,
• training data provenance,
• risk categorization by application domain,
• post-hoc human oversight.

However, these approaches share a critical limitation:

they lack a formal, operational criterion for determining whether an AI system’s outputs constitute stable semantic reasoning or merely context-sensitive behavioral imitation.

As a result, governance decisions often rely on:

• subjective expert judgment,
• vendor self-reporting,
• or non-reproducible demonstrations.

This creates a structural accountability gap.

7.2 Semantic Emergence as a Governance Object

The methodology proposed in this work reframes governance around a new auditable object:

Semantic structures, rather than models or claims of intelligence.

Because semantic emergence is defined through:

• transferability,
• compressibility,
• cross-model reproducibility,

it becomes:

• model-agnostic,
• vendor-neutral,
• verifiable by third parties.

This enables regulators, auditors, and institutions to evaluate what is being relied upon, rather than who claims capability.

7.3 Auditable Criteria vs. Narrative Assurance

Many contemporary AI deployments justify trust through:

• benchmarks optimized for performance,
• demonstrations curated for stakeholders,
• assurances of internal safety mechanisms.

By contrast, the present framework introduces negative accountability:

• outputs are rejected with explicit reason codes,
• failure modes are logged and classifiable,
• emergence claims are denied unless all criteria are met.

This shifts governance from:

“Why should we trust this system?” to “Under which conditions is trust epistemically warranted?”

7.4 Third-Party Semantic Auditing

Because the evaluation protocol treats models as black boxes, it enables:

• independent audits by regulators,
• cross-vendor comparison,
• longitudinal monitoring of semantic stability over time.

Audits no longer require access to:

• proprietary weights,
• training corpora,
• or alignment pipelines.

Instead, they operate solely on:

• compressed semantic representations,
• observable outputs,
• formally defined equivalence tests.

This lowers barriers to oversight while preserving intellectual property.

7.5 Compliance Without Anthropomorphism

A common failure in AI governance discourse is the implicit attribution of human-like understanding or intent to models.

This framework explicitly avoids such attribution.

It does not certify that a system:

• understands,
• reasons,
• or possesses agency.

It certifies only that:

certain semantic structures meet externally verifiable stability criteria.

This distinction is crucial for legal and regulatory contexts, where ontological claims about machine cognition are neither necessary nor desirable.

7.6 Risk-Based Deployment Controls

The framework enables a new class of semantic gating mechanisms:

• High-risk decisions (e.g., legal, medical, governance-critical contexts) may require: verified semantic emergence across a predefined model set
• Lower-risk contexts may permit: non-emergent but useful outputs

This supports proportional regulation without blanket prohibition or blind permissiveness.

7.7 Interoperability With Existing Frameworks

The proposed methodology is complementary to existing governance efforts, including:

• AI risk classification regimes,
• documentation and transparency requirements,
• post-deployment monitoring obligations.

It provides a missing layer:

a way to formally test whether a relied-upon semantic construct is robust enough to warrant institutional dependence.

7.8 Institutional Implications

By shifting focus from models to semantic structures, institutions can:

• decouple governance from vendor dominance,
• avoid lock-in to proprietary claims,
• standardize semantic audit practices across jurisdictions.

This aligns governance incentives with epistemic robustness rather than technological spectacle.

7.9 Summary Statement

AI governance should not ask whether a model is intelligent, but whether a semantic structure is stable enough to be trusted.