Generative models like VAEs, GANs, and Transformers have achieved impressive results in producing images, text, and other data. However, their internal workings are often complex and opaque. Explainability and interpretability are about opening up these "black boxes" so that humans can understand why and how a generative model produces its outputs. In this chapter, we explore why explainability is important and survey techniques to interpret generative models. We will balance theory with practical examples, including visualizations of latent spaces, attention heatmaps, disentangled representations, and even some R code demonstrating interpretability tools. The goal is to make these concepts accessible to an undergraduate reader while providing enough depth for professionals.