CS Colloquium: Yangsibo Huang - Auditing Policy Compliance in Machine Learning Systems

Speaker: Yangsibo Huang, Princeton University

Talk Title: Auditing Policy Compliance in Machine Learning Systems

Abstract: As the capabilities of large-scale machine learning models expand, so too do their associated risks. There is an increasing demand for policies that mandate these models to be safe, privacy-preserving, and transparent regarding data usage. However, there are significant challenges with developing enforceable policies and translating the qualitative mandates into quantitative, auditable, and actionable criteria. In this talk, I will present my work on addressing the challenges.  I will first share my exploration of privacy leakage and mitigation strategies in distributed training. Then, I will explore strategies for auditing compliance with data transparency regulations. I will also examine methods to quantify and assess the fragility of safety alignments in Large Language Models. Finally, I will discuss my plans for future research directions, including collaboration with policy researchers and policymakers.   This lecture satisfies requirements for CSCI 591: Research Colloquium

Biography: Yangsibo Huang is a Ph.D. candidate and Wallace Memorial Fellow at Princeton University.  She has been doing research at the intersection of machine learning, systems, and policy, with a focus on auditing and improving machine learning systems’ compliance with policies, from the perspectives of privacy, safety, and data usage. She interned at Google AI, Meta AI, and Harvard Medical School and was named an EECS rising star in 2023.   

Host: Yue Zhao

Location: Olin Hall of Engineering (OHE) - 136

Audiences: Everyone Is Invited

Contact: CS Faculty Affairs