Sector players should run their AI systems through the governance process the framework sets out: establish a data governance system (representative and unbiased data from varied sources, accurate annotation and labelling, security measures and privacy compliance); choose a model suited to the problem and data, train it on the collected data, validate it on a separate dataset to check generalisation and limit overfitting, then deploy it; make sure users understand the system's capabilities and limits and can give feedback and report problems, adhering to the seven principles and the applicable laws of the domain and region; watch performance and behaviour as it happens, catch and correct drift in the data and bias in predictions, and update models through a controlled, tested process; design an emergency shut-off that disables the system on unexpected behaviour or ethical concerns; develop a performance measurement index with ethics metrics (fairness, bias, explainability, accountability) and assess the system against the principles and its impact on stakeholders; analyse and mitigate risks; run a feedback loop that feeds user feedback, monitoring and risk findings back into governance; keep full documentation of where the data came from, how the model is built, the ethical questions weighed and the actions taken; and report on behaviour, performance and adherence to users, regulators and the public, adapting the process as technology and regulation change.
The graph holds this control, the 0 it maps to, and the evidence behind each claim, over MCP and REST.