Highlights from FPF’s July 2025 Technologist Roundtable: AI Unlearning and Technical Guardrails

On July 17, 2025, the Future of Privacy Forum (FPF) hosted the second in a series of Technologist Roundtables with the goal of convening an open dialogue on complex technical questions that impact law and policy, and assisting global data protection and privacy policymakers in understanding the relevant technical basics of large language models (LLMs). In this event, we invited a range of academic technical experts and data protection regulators from around the world to explore machine unlearning and technical guardrails. 

We were joined by the following experts:

• A. Feder Cooper, Incoming Assistant Professor, Department of Computer Science, Yale University; Postdoctoral Researcher, Microsoft Research; Postdoctoral Affiliate, Stanford University

• Ken Ziyu Liu, Ph.D. Candidate, Department of Computer Science, Stanford University; Researcher, Stanford Artificial Intelligence Laboratory (SAIL)

• Weijia Shi, Ph.D. Candidate, Department of Computer Science, University of Washington; Visiting Researcher, Allen Institute for Artificial Intelligence

• Pratyush Maini, Ph.D. Candidate, Machine Learning Department, Carnegie Mellon University; Founding member of DatologyAI

In emerging literature, the topic of “machine unlearning” and its related technical guardrails concerns the extent to which information can be “removed” or “forgotten” from an LLM or similar generative AI model or from an overall generative AI system. The topic is relevant to a range of policy goals, including complying with individual data subject deletion requests, respecting copyrighted information, building safety and related content protections, and overall performance. Depending on the goal at hand, different technical guardrails and means of operationalizing “unlearning” have different levels of effectiveness.

In this post-event summary, we highlight the key takeaways from three parts of the Roundtable on July 17: 

1. Machine Unlearning: Overview and Policy Considerations 
2. Core “Unlearning” Methods: Exact vs. Approximate
3. Technical Guardrails and Risk Mitigation

If you have any questions, comments, or wish to discuss any of the topics related to the Roundtable and Post-Event Summary, please do not hesitate to reach out to FPF’s Center for AI at [email protected].

Take a look at last year’s Technologist Roundtable: Key Issues in AI and Data Protection Post-Event Summary and Takeaways.