The integration of artificial intelligence into human decision-making environments has introduced a previously undertheorized cost: the gradual surrender of human autonomy in exchange for access to information and computational assistance. Building on the Human Identity and Autonomy Gap (HIAG) framework, this paper advances a theoretical model of autonomy surrender as a measurable, cumulative process driven by cognitive bandwidth depletion. The model proposes three interacting mechanisms: the silent cost of AI assistance, in which autonomy is transferred incrementally and without awareness; the surrender threshold, beyond which reclaiming autonomous function becomes cognitively and psychologically difficult; and the recovery mechanism, which establishes the design obligation and the ethical responsibility accompanying deliberate human re-assumption of control. The paper argues that human re-entry into the decision loop is not a passive option but an active cognitive event requiring intentional bandwidth restoration. The design of AI systems must incorporate structured re-entry pathways, here termed recovery mechanisms, that preserve human agency while appropriately distributing responsibility. The model further predicts a terminal state, here termed preference inversion, in which functional dependence on AI assistance is experienced not as a deficit but as a preference, transforming the restoration of autonomy from a design problem into a cultural and political one. Implications are drawn for AI system design, governance frameworks, and human factors research.

翻译：人工智能融入人类决策环境引入了一种此前未被充分理论化的成本：人类为获取信息和计算辅助而逐步让渡自主性。基于人类身份与自主性差距（HIAG）框架，本文提出了一种将自主性让渡视为由认知带宽消耗驱动的可量化累积过程的理论模型。该模型提出三种相互作用机制：人工智能辅助的隐性成本——自主性在无意识状态下被渐进转移；让渡阈值——超过该临界点后恢复自主功能将面临认知与心理障碍；以及恢复机制——确立了人类主动重新掌控时所需的设计义务与伦理责任。本文论证指出，人类重新进入决策回路并非被动选项，而是需要有意恢复认知带宽的主动认知事件。人工智能系统的设计必须包含结构化的重入路径（本文称之为恢复机制），在适当分配责任的同时维护人类主体性。该模型进一步预测了一种终端状态（本文称为偏好反转），在此状态下，对人工智能辅助的功能依赖不再被体验为缺陷，反而转化为偏好，使得自主性的恢复从设计问题转变为文化与政治议题。本文为人工智能系统设计、治理框架与人因研究提出了启示。