👉 Sensitivity engineering is a design approach used in machine learning and control systems to enhance the robustness and performance of models by intentionally modifying their sensitivity to various inputs or parameters. This process involves adjusting the model's architecture, training procedures, or hyperparameters to ensure that it remains stable and effective under different conditions, such as noise, outliers, or changes in data distribution. By doing so, sensitivity engineering aims to improve the model's generalization capabilities and reduce its vulnerability to adversarial attacks or unexpected variations, ultimately leading to more reliable and efficient systems.
