# Your AI Chats Aren't Private (And How "Unlinkable Inference" Can Help)

Page: https://stenobird.com/podcast/the-brave-technologist-2135907/your-ai-chats-aren-t-private-and-how-unlinkable-inference-can-help
Text version: https://stenobird.com/podcast/the-brave-technologist-2135907/your-ai-chats-aren-t-private-and-how-unlinkable-inference-can-help.md
Podcast: [The Brave Technologist](https://stenobird.com/podcast/the-brave-technologist-2135907)
Published: 2026-05-13T12:00:00+00:00
Episode link: https://thebravemarketer.libsyn.com/your-ai-chats-arent-private-and-how-unlinkable-inference-can-help
Audio file: https://traffic.libsyn.com/secure/thebravemarketer/erik_and_ken_v1.mp3?dest-id=2705240
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JSON: https://stenobird.com/v1/public/podcasts/the-brave-technologist-2135907/episodes/your-ai-chats-aren-t-private-and-how-unlinkable-inference-can-help
Duration seconds: 1807

## Resource

Ken Liu (Computer Science PhD at the Stanford AI Lab) and Erik Chi (CS PhD at UMich) are the Creators of the Open Anonymity Project, which lets people prove things about themselves online without revealing their identity. In this episode we explore what it means for AI systems to "know" you; why today's so-called privacy modes fall short; and how the next generation of AI systems could be built with privacy as a default, rather than an afterthought. Key Takeaways: What "unlinkable inference" means and why it changes the privacy model of AI chat tools What actually happens to your data the moment you hit "send" in a typical AI system Why incognito mode in AI tools is largely a UI illusion, rather than a real privacy protection The role of metadata in identifying and profiling users, and how "secretary models" could enable personalization without sacrificing privacy How anti-censorship and privacy intersect in a future dominated by agentic AI systems Why now is the time to rethink assumptions about privacy in AI tools Guest Bio: Ken Liu is a Computer Science PhD student at the Stanford AI Lab, advised by Percy Liang and Sanmi Koyejo. His research focuses on foundation models and data/user privacy, and the intersection between the two. His recent work studies the privacy properties of AI (such as membership, memorization, and unlearning), and various AI privacy tools (such as anonymization, differential privacy, and federated learning). His papers have earned spotlights at top venues, and his findings have been deployed at scale on Android. Ken also led a team to a 1st-place win at the US-UK PETs Prize sponsored by the White House OSTP and the UK Government. Previously, Ken spent time at Google DeepMind, Carnegie Mellon University, Meta, Apple, and Amazon. Erik Chi is a C…

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