Continuous Trust Delegation (CTD) describes a structural pattern in AI-mediated decision environments in which authority, cognitive influence, and consequence-bearing responsibility become progressively misaligned through repeated interaction. Unlike discrete system failures, CTD emerges without a singular triggering event. It develops incrementally as human decision-makers increasingly rely on AI-mediated outputs in forming judgments, while formal accountability structures remain unchanged. CTD is defined as a recursive interactional condition in which reliance stabilises relative to baseline verification behaviour under repeated interaction. The framework distinguishes CTD from adjacent concepts such as automation bias and deskilling by identifying a cross-temporal, structural misalignment rather than a momentary cognitive effect or capability degradation. This is a non-prescriptive, descriptive classification. It does not define measurement, governance mechanisms, or interventions. It identifies a structural condition under which institutional assumptions regarding accountability, auditability, and human oversight may remain formally intact while underlying decision influence shifts. This work is intended to be read alongside Decision-Pathway Admissibility, which defines the boundary condition for legitimate consequence-bearing decision pathways. Together, the two frameworks distinguish between structural admissibility at the point of decision and the temporal evolution of influence within human–AI interaction.
Victoria Gavaza (Mon,) studied this question.