Assessing neural network robustness: ISO/IEC 24029-1:2021

AI systems are increasingly used in contexts where reliability, safety, fairness, privacy and trust matter. In this course, you’ll learn how to assess the robustness of neural networks using the structured approach set out in ISO/IEC TR 24029-1:2021. You’ll explore why non-robust systems can behave unexpectedly, create unfair outcomes or become vulnerable to adversarial attacks, and you’ll learn how to identify and evaluate these risks before deployment. Through clear explanations, worked examples and practical assessment scenarios, you’ll examine statistical, formal and empirical methods for testing robustness. You’ll learn how to set robustness goals, choose suitable datasets and metrics, define thresholds, interpret results and document evidence-based decisions. The course also introduces key techniques such as data perturbation and abstract interpretation, helping you understand how neural networks respond to changed, distorted or deliberately manipulated inputs.