Model assumptions are checked through rigorous testing with clear rules and methods. Objectives are risk-sensitive so that targets balance risks and benefits and are not gamed. Input variable selection complies with policies on the data used and its impact and avoids target variable leakage, drawing on organisational knowledge and causal relationships. Overfitting is controlled through cross-validation and regularisation. Where training is outsourced to a public cloud or built on third-party pre-trained models, trusted sources and platforms are used. Against poisoning and evasion attacks the team performs adversarial training with as many adversarial examples as possible, conducts independent security assessment of the application, and applies dimensionality reduction to training data.
The graph holds this control, the 0 it maps to, and the evidence behind each claim, over MCP and REST.