Reshaping Privacy: Ai's New Demands On Data Librarianship

AI‑inflected systems are reshaping what privacy means for library users and workers. Large language models (LLMs) rely on vast amounts of data, and their data collection is ongoing and often opaque: User input and interaction logs can be used not only to refine these systems but also to deepen commercial profiling of individuals. This presentation argues that these shifts introduce a new dimension of data literacy and librarianship: as the nature of data collection and privacy evolves, data librarians and scientists must adapt their teaching and practices to equip users for a new privacy landscape.