Human AI Handovers: A Dynamic Authority Reversal Framework for Trust Calibration and Transitional Accountability

Introduction: Human AI collaboration is reconceptualized as a temporal authority problem: leadership shifts within a single decision episode across four states -Leadership, and Mutual Override triggered by data superiority, contextual judgment needs, risk thresholds, and ethics overrides. Aim: To formalize the Dynamic Authority Reversal (DAR) framework, calibrating trust and aligning accountability with the active leadership state, and to translate it into testable propositions and an operational playbook. Method: Conceptual synthesis of leader follower, distributed leadership, and HITL literatures; state-and-transition architecture; derivation of ten propositions with measurement constructs -sector implementation roadmap. Findings: DAR surfaces and addresses five debates decision ownership during AI-led phases, autonomy acceptance trade-offs (mitigated by safe-exit guarantees), optimal reversal tempo, state-contingent explanations, and transitional accountability via role-state logging. It specifies triggers, guardrails, telemetry, and KPIs to make handovers auditable and performant. Conclusion: By keeping humans ultimately responsible while enabling reversible AI leadership with measurable handovers, DAR improves effectiveness, legitimacy, and compliance in high-stakes workflows across finance, healthcare, public administration, and HR Originality and value: DAR moves beyond static augmentation/autonomy/HITL categories by modeling intra-episode handovers and coupling theory to buildable instruments Authority-State Playbooks, safe-exit timers, state-contingent XAI, and a Reversal Register plus falsifiable propositions for cumulative research.