Google Research· Research

A differentially private framework for gaining insights into AI chatbot use

Alexander Knop, Research Scientist, and Daogao Liu, Post Doc Researcher, Google Research Introducing a novel framework that generates high-level insights into AI chatbot usage through a pipeline of DP clustering, DP keyword extraction, and LLM summarization. This approach provides rigorous, end-to-end DP guarantees, ensuring user conversation privacy while offering utility for platform improvement. Quick links Paper Share Copy link × Large language model (LLM) chatbots are used by hundreds of millions of people daily for tasks ranging from drafting emails and writing code to planning vacations and creating menus for cafes. Understanding these high-level use cases is incredibly valuable for platform providers looking to improve services or enforce safety policies. It also offers the public insights into how AI is shaping our world. But this raises a critical question: How can we gain valuable insights when the conversations themselves might contain private or sensitive information? Existing approaches, like the CLIO …

Announced

10 Dec 2025 21:59 UTC

Detected

03 Aug 2026 21:12 UTC (+235d after)