Reviews examining how AI systems redistribute authority, create institutional duties and expose gaps between governance principles and enforceable controls.
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Separating autonomous execution from independent enforcement makes organizational authority more testable, but verifiable Law, approvals, and effects do not themselves establish the legitimacy of the sovereign organization or provide external redress.
Digital constitutionalism changes who can govern platform conduct, but the legitimacy of expanded protective powers depends on enforceable limits, reviewable moderation decisions, and remedies for both unlawful content and wrongful suppression.
Local compliance is not a compositional property: when institutional outcomes emerge from coupled agents, governance must attach observability, revocable authority, containment, and accountability to the population and workflow, not only to each component.
Keeping a human judge formally in charge does not by itself preserve judicial legitimacy; a governable judicial-AI regime must also make algorithmic influence, institutional operators, override incentives, and avenues for contestation independently auditable.
Independent enforcement can constrain an agent's actions, but accountable delegation also requires evidence that the enforced scope was legitimately granted and a remedy for people harmed by its exercise.
Authority checks must govern the next action before an agent crosses a boundary, while leaving the competence and legitimacy of the authorizing institution open to challenge.
AI governance in journalism fails when ethical principles are published without enforceable decision rights, audit access, institutional capacity, and controls over dependence on proprietary infrastructure.
Agentic-swarm governance should not depend on predicting every collective action; it should bound delegated authority, constrain reachable states, preserve attribution, and contain failures when emergent behavior exceeds prediction.
AI-generated evidence should acquire courtroom authority only through a verifiable evidentiary process that preserves provenance, transformation history, disclosure, independent challenge, and judicial control.
Responsible AI governance in essential services becomes meaningful only when transparency is connected to identifiable institutional responsibility, accessible contestation, and enforceable remedy.
Digital twins turn agentic governance into a control-plane problem: autonomy should track consequence and reversibility, but oversight is meaningful only when humans possess actual authority, timely evidence, and workable mechanisms to intervene.
Verifiable agent infrastructure can make identity, delegation, execution, and evidence machine-checkable without settling who is entitled to govern, whose preferences should count, or how affected parties can contest automated authority.
Computational enforcement redistributes public authority before doctrine catches up, so legitimacy depends on making machine-produced evidence contestable rather than merely keeping a human nominally in the loop.
JAZB makes machine authority governable by turning provenance, delegation, revocation, jurisdiction, actuation, and evidence into explicit control objects, but organizational authority becomes legitimate governance only when the human sovereign root is itself constrained, contestable, and externally accountable.
Governance evidence becomes institutionally meaningful only when it can reconstruct a decision, prove that authorization preceded execution, and preserve the authority and action context that made the decision legitimate.