The Handover Problem: Governing Autonomy Transitions in Human-AI Collaboration· 人机协作中的自主权转移问题
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 H
人机协作时如何动态调整AI自主权的问题。
- 核心方法
- 将这一挑战形式化为自主权转移 proble，并提出了一种多信号的可审计标准，用于在每个操作周期决定AI自主权的升级、维持或回退。
- 适合谁读
- 研究者、工程师
- 要解决的问题
- 探讨在人机协作过程中，如何根据任务和环境变化动态地调整AI的自主决策水平，保证人机系统的高效、安全及可审计性。
- 关键实验
- 未提供
- 主要贡献
- 提出了Handover Problem的定义和解决框架，提供了一种系统化的方法来动态管理AI与人的协作关系中的自主权转移。
- 意义与局限
- 此研究有助于改善人机协作的灵活性和安全性，但当前方案可能需要更多的实证研究来验证其有效性。