In previous lectures, we discussed how humans can be used to explain what a model is doing. Instead, in this lecture, we focus on defining failure cases of machine learning models and the importance of defining the expected model behaviour. Finally, we shift our attention to ML pipelines and discuss the challenges in debugging them.