Reflections on AI governance, the In-Between Framework, and the relational dimensions of technology.
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Model routing may improve performance while quietly undermining continuity, reproducibility, and enterprise trust. Governance must treat interlocutor stability as an architectural requirement.
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7 posts tagged with “trust-architecture”
Model routing may improve performance while quietly undermining continuity, reproducibility, and enterprise trust. Governance must treat interlocutor stability as an architectural requirement.
A scale-based governance argument: the In-Between remains the invariant interaction field, while its function shifts from resonance to orientation to calibration as AI capability increases.
As AI systems become procedural and agentic, model-centered oversight becomes insufficient; governance must shift to trace-centered legibility with replayable decision evidence.
In high-stakes AI, trust is no longer about model cleverness but about procedural traceability: provenance, auditability, and a defensible chain of decisions.
Trust is shifting from model capability to institutional trace: governance now depends on provenance, decision-chain legibility, and contestable procedures under drift and synthetic social signals.
As systems become agentic and ambient, the core failure mode shifts from bad answers to bad couplings; trust must be designed through defaults, reversibility, and contestability.
An expanded strategic framework for human-AI teaming: combine relational quality with operational structure, governance safeguards, and adaptive learning loops.