发表机构
Universitat Politècnica de València; Universitat de València（瓦伦西亚理工大学; 瓦伦西亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出交接问题，即人机协作中自主性转换的治理，并引入交接准备度评分（HRS）与滞后策略，实现可审计、多信号的自主性调整。
AI 中文摘要
人机系统很少在固定的AI自主性水平下运行。随着操作员和AI系统随时间协作，控制权必须转移：当协作稳定时，AI可以承担更多责任，当证据模糊时维持其当前角色，或在条件恶化时将控制权交还给人类。现有的关于自适应自动化、监督控制、自动化信任和技能退化的研究解释了该问题的部分内容，但未提供可审计的、多信号的准则来管理多周期工作流中自主性何时应改变。我们将这一挑战形式化为交接问题：在每个操作周期决定是否升级、维持或恢复AI自主性，同时保持过程可逆、可恢复和可审计。我们引入了交接准备度评分（HRS），这是一个透明的复合度量，整合了四个信号维度：操作员准备度、人机信任、学习稳定性和操作性能。它结合了基于滞后的转换策略，该策略要求在增加自主性之前有持续的正向证据，但在条件恶化时迅速恢复。在软件工程和制造领域，HRS和硬安全防护措施解决了互补的失效机制：防护措施在单个指标突破临界阈值时强制执行即时纠正措施，而HRS检测操作员准备度的缓慢、多信号侵蚀，这是任何单个防护措施都无法观察到的。该框架将自主性交接确立为一个需要明确、复合和可审计标准的治理问题。这提供了自适应自动化研究此前未提供的概念和形式基础。
英文摘要
Human-machine systems rarely operate at a fixed level of AI autonomy. As operators and AI systems collaborate over time, control must shift: the AI can take on more responsibility when collaboration is stable, maintain its current role when evidence is ambiguous, or return control to the human when conditions deteriorate. Existing work on adaptive automation, supervisory control, trust in automation, and deskilling explains parts of this problem, but provides no auditable, multi-signal criterion for governing when autonomy should change across multi-cycle workflows. We formalise this challenge as the Handover Problem: deciding, at each operational cycle, whether to escalate, maintain, or revert AI autonomy while keeping the process reversible, recoverable, and auditable. We introduce the Handover Readiness Score (HRS), a transparent composite measure that integrates four signal dimensions: operator readiness, human-AI trust, learning stability, and operational performance. It is combined with a hysteresis-based transition policy that requires sustained positive evidence before increasing autonomy but reverts promptly when conditions worsen. Across software engineering and manufacturing domains, the HRS and hard safety guards address complementary failure regimes: guards enforce immediate corrective action when a single indicator breaches a critical threshold, while the HRS detects the slow, multi-signal erosion of operator readiness that no individual guard can observe. The framework establishes autonomy handover as a governance problem requiring explicit, composite, and auditable criteria. This provides a conceptual and formal foundation that adaptive automation research has not previously provided.
Comments10 pages, 5 figures, 8 tables. Submitted to IEEE Transactions on Human-Machine Systems
