Inform and explain to AI providers that predictive performance or output quality may change markedly after use starts or fall short of expected accuracy, and the risks that may follow; specifically, make known the handling of bias that each technical element of the model may contain (training data, training process, prompts expected from business users or non-business users, information referred to at inference, linked external services).
The graph holds this control, the 0 it maps to, and the evidence behind each claim, over MCP and REST.