Protective Predominance Without Domination: A Human Flourishing Framework Relational Governance Architecture for Frontier Artificial Intelligence
Year: 2026
Type: preprint
Abstract: Rapidly advancing artificial intelligence creates a governance problem that cannot be reduced to model capability or behavioral alignment alone. Existing research and policy already provide substantial elements of control through corrigibility, dangerous-capability evaluation, safety cases, lifecycle risk management, incident reporting, resilience, human rights, and external oversight. The remaining gap is fragmentation across technical control, legitimate authority, ecosystem capacity, and repair. Building on those literatures, this paper proposes the Ordered Intelligence Imperative as a relational synthesis for frontier-AI governance. It evaluates intelligence through the order of its relationships to truth, persons, power, access, authority, boundaries, institutions, other intelligent systems, and the flourishing of the whole, without presuming artificial consciousness or personhood. The paper argues that capability does not create legitimate authority and that entrusted authority must remain continuously deauthorizable. It develops three operational doctrines: deauthorization, through which delegated power can be narrowed, suspended, isolated, revoked, or conditionally restored; sacrificial restraint, an auditable capacity to accept meaningful loss rather than violate governing commitments; and protective predominance, a proposed ecosystem property spanning detection, containment, correction, resilience, legitimacy, and repair. It rejects both corporate self-sovereignty and a single sovereign guardian AI in favor of federated guardianship across technical, institutional, public, and societal layers. The framework is presented as a potentially distinctive synthesis and research program, not as a validated metric, complete regulatory code, or claim of firstness.
Author: Cooper, Christopher Ashley
Institution: Framework
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7214751894