Training for users of AI-enabled systems, including those deciding on their employment, builds proficiency together with a grasp of how the system behaves and performs and where it falls short, so that users can calibrate their trust across use cases and conditions; training spans many scenarios, among them edge cases that put the human/machine team under strain and trigger reversionary working. Training Needs Analysis includes these understanding and trust needs. Where collaborative training lets humans and AI learn from each other, the overall behaviour of each team is assured case by case.
The graph holds this control, the 0 it maps to, and the evidence behind each claim, over MCP and REST.