Perturbation techniques make small changes to inputs to a model or algorithm and the. track how they change the output. They are black-box methods in that they do not need to know anything about the internal workings of the model or algorithm. Perturbatuon techniques can be used to measure the {[sensitivity}} of a model to changes and hence the uncertainty of the model. They can also be really useful for explainable AI, particularly when applied to provide a ({local explanation}} for a specific decision.
Used on pages 495, 502, 513
Also known as perturbation