I gave a guest talk at the Institute for Natural Language Processing at Stuttgart University covering some of our work on interpretability for authorial style. The talk covered two directions. First, how we interpret the latent space of pre-trained language models to surface which stylistic features drive attribution decisions. Second, proposing two architectures that build interpretable models by design: One grounding embeddings at the word sense level, the other at the transformer layer level, making model decisions transparent by construction. Together, these approaches offer complementary paths toward trustworthy, human-readable authorship attribution.