Machine Unlearning is a field of AI research gaining attention in the policy world. It's often discussed in the context of privacy, particularly in relation to the European Union's General Data Protection Regulation (GDPR) and the EU AI Act as well as the California Consumer Privacy Act (CCPA). At World Privacy Forum's tutorial at the Winter Conference on Applications of Computer Vision (WACV) in Tucson, Arizona in March 2026, we aimed to bridge gaps in understanding between the technical academic community, including machine unlearning researchers, and the AI governance, policy, and legal communities. In this episode you'll hear a chat with Dr. Yezhou "YZ" Yang, a tenured associate professor in computer science and engineering in the School of Computing and Augmented Intelligence at Arizona State University; Yang spoke at WPF's tutorial about machine unlearning for concept erasure.
Topics discussed:
- Why machine unlearning for concept erasure isn't just for privacy
- Benchmarks for testing the removal of data - or even the presence of a visual artist's style - from AI models
- Why Yang wants to see more inclusion of technical AI practitioners in the policy conversation
Research discussed:
- EraseFlow: Learning Concept Erasure Policies via GFlowNet-Driven Alignment
- R.A.C.E. : Robust Adversarial Concept Erasure for Secure Text-to-Image Diffusion Model
- WOUAF: Weight Modulation for User Attribution and Fingerprinting in Text-to-Image Diffusion Models
Featured in this episode:
- Dr. Yezhou "YZ" Yang, tenured Associate Professor in Computer Science and Engineering in the School of Computing and Augmented Intelligence at Arizona State University
- Kate Kaye, Deputy Director of World Privacy Forum
The Privacy on the Ground intro theme features music by Pangal. Episode music is by Maciej Sadowski.
