Abstract
To achieve truly autonomous AI agents that can handle complex tasks on our behalf, we must first trust them with our sensitive data while ensuring they can learn from and use this information responsibly. Yet current language models frequently expose private information and reproduce copyrighted content in unexpected ways, highlighting why we need to move beyond simplistic “good” or “bad” blanket rules toward context-aware systems. In this talk, we will examine data exposure through membership inference attacks, develop controlled generation methods to protect information, and design privacy frameworks grounded in contextual integrity theory. Looking ahead, we’ll explore emerging directions in formalizing semantic privacy, developing dynamic data controls, and creating evaluation frameworks that bridge technical capabilities with human needs.
Speaker Biography
Niloofar Mireshghallah is a postdoctoral scholar at the Paul G. Allen Center for Computer Science & Engineering at the University of Washington. She received her PhD in 2023 from the Department of Computer Science and Engineering at the University of California, San Diego. Mireshghallah’s research interests include privacy in machine learning, natural language processing, and generative AI and law. She is a recipient of the 2020 National Center for Women & IT Aspirations in Computing Collegiate Award, a finalist for the 2021 Qualcomm Innovation Fellowship, and a 2022 Rising Star in both Adversarial Machine Learning and Electrical Engineering and Computer Science.