Sovereign: A Socio-Technical Framework for Human Capability and Responsibility Coverage in AI-Assisted Work

Abstract

Generative artificial intelligence (AI) can improve immediate task performance without establishing whether human capability develops, remains stable, or deteriorates. Sovereign proposes responsibility–capability alignment as a theoretical account of whether available capability covers consequential responsibilities under AI disruption. A provisional, time-indexed measurement and allocation bridge connects capability trajectories with coverage of specified responsibilities at their required dates; component profiles take priority over optional averages. Responsibility–capability alignment specifies proposed pathways through which vulnerability can increase despite stable individual competence, and substantial assistance benefits can coexist with adequate collective control. A purposive conceptual synthesis integrates research on technology-supported cognition, expertise, human–automation interaction, productivity, and AI governance. Four complementary modules support that central mechanism: the Capability Dynamics Model represents conditional capability trajectories; the Assisted Performance Gap Index describes assisted–unaided performance differences; the Cognitive Sovereignty Model represents exercised control capacity; and the Dependency Exposure Model organizes capability shortfalls and structural exposures against explicit workflow requirements. AI-supported learning and capability erosion are competing possibilities; a performance gap alone establishes neither deterioration nor unsafe dependency. Bounded equations, comparisons with established theories, contrasting analytical cases, and falsifiable propositions specify the contribution. No original empirical testing, parameter estimation, or validation of the proposed models is reported. Subsequent studies are intended to assess measurement quality, compare alternative specifications, estimate relationships, and determine predictive value against independently observed outcomes. The contribution is an analytically explicit foundation for research on capability governance, rather than an established risk assessment instrument.