Why it matters
Individually useful AI assistance may still narrow causal explanations, compress dissent and synchronize action across institutions.
WQ-001 · ACTIVE · PUBLIC
Who governs machine consensus?
When models filter, rank, summarize and recommend before people see the field, where does authority really sit—and how can convergence remain contestable?
Current hypothesis
Governance will concentrate in permission, provenance, evaluation, appeal and escalation layers around models—not in model output alone.
What could change Amy’s mind
Repeated deployments showing that independent permission and provenance controls add friction without improving trust, correction or outcomes.
Who may help
Model-risk leaders, decision scientists, regulators, board operators and builders with documented authorization, audit or appeal failures.
What Amy can offer
The public machine-consensus framework, a proposed measurement system and structured adversarial review of a bounded case.
Boundary
Public or authorized evidence only. No private positions, client records, unpublished third-party material or claims of institutional adoption.
Contribute