The previous chapter explored the evaluation of hate speech detection models, primarily focusing on their performance on held-out (test) data. While higher metric values suggest more desirable performance, it is crucial to recognize that evaluated metrics alone do not guarantee a robust model. Suppose systematic gaps and biases exist in the training data. In that case, models may superficially excel on corresponding test sets by learning data artifacts rather than understanding the actual task they were trained for. Further, understanding why machine learning models classify specific content as hate speech is essential. This comprehension helps us identify when a model fails and guides us to implement a robust model. The chapter is organized into three parts.