Description: Perturbation Analysis is a technique in explainable artificial intelligence, used to evaluate the stability and robustness of machine learning models. It involves introducing small variations in the input data to observe how these alterations affect the model’s predictions. This methodology allows researchers to identify the model’s sensitivity to changes in data, which is crucial for understanding its behavior and reliability. By performing controlled perturbations, researchers can determine whether the model can generalize adequately or if it is susceptible to noise in the data. Furthermore, Perturbation Analysis helps to unravel the model’s decisions, providing greater transparency in its operation. This technique is especially relevant in critical applications, where the interpretation of results is essential for informed decision-making. In summary, Perturbation Analysis contributes to the robustness of models and promotes trust in artificial intelligence solutions by making them more understandable and accessible to end users.
