Explainable artificial intelligence (XAI) aims to overcome the opacity of black box systems, i.e., to make them understandable to suitable stakeholders. In this chapter, I investigate how understanding depends on how much is at stake in a context. I support the intuition that understanding is sensitive to the stakes with a pair of cases. I further use this pair of cases to spell out how exactly the stakes affect understanding, particularly, outright understanding why. To do so, I connect discussions of the concept of understanding with debates on pragmatic encroachment and on inductive risk. I zoom in on one necessary condition on understanding specifically, viz. the epistemic justification condition, according to which the beliefs involved in understanding have to be supported by sufficient evidence and have to cohere sufficiently well. I argue that, where the stakes are high, there are more stringent standards for sufficient evidence and coherence, and that this explains the stakes-sensitivity of understanding.