How the reinforcement-switch model works This reinforcement-switch model combines reinforcement learning with human-centered control systems to deliver accountable autonomy.
Continuos learning
Reward loop
Reward and penalties
Human action
Human validation
Model initiation and training
Systems
Copilot mode
Yes
Delegated autonomy?
The switch
The reinforcement-switch model The reinforcement-switch model introduces a dynamic, phased approach to human-AI collaboration—designed not as a binary transition to autonomy, but as a negotiated continuum of control. It offers a structured path from human-supervised intelligence to conditional autonomy,
anchored in trust, oversight and resilience. 1. Prediction and Reinforcement phase
AI agents begin by generating predictions or actions. Human operators validate outcomes—reinforcing successful decisions or applying corrective feedback. This feedback loop is essential to shape behavioral baselines and establish trust. 2. Confidence accumulation
As agents deliver consistently accurate outcomes—e.g., 100 validated decisions across varied scenarios—they earn graduated autonomy. Trust is not assumed; it is earned through performance.
7 | Reinforcement before autonomy: Engineering trustworthy autonomy in insurance AI
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