The Handover Problem: Governing Autonomy Transitions in Human-AI Collaboration
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.
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Key points
- 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.
Sources (1)
- [1]The Handover Problem: Governing Autonomy Transitions in Human-AI CollaborationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 04:27 PM
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.
Human-machine systems rarely operate at a fixed level of AI autonomy.
Extractive summary: sentences quoted from the sources.