Weiyan Shi

Research Abstract:

AI research has so far focused on modeling common human skills, such as building systems to see, read, or talk. As these systems gradually achieve a human level in standard benchmarks, it is increasingly important to develop next-generation interactive AI systems with more advanced human skills, to function in realistic and critical applications such as providing personalized emotional support. In this talk, I will cover (1) how to build such expert-like AI systems specialized in social influence that can persuade, negotiate, and cooperate with other humans during conversations. (2) I will also discuss how humans perceive such specialized AI systems. This study validates the necessity of Autobot Law and proposes guidance to regulate such systems. (3) As these systems become more powerful, they are also more prone to leak users' private information. So I will describe our proposed new privacy notion, Selective Differential Privacy, and an algorithm to train privacy-preserving models with high utilities. Finally, I will conclude with my long-term vision to build a natural interface between human intelligence and machine intelligence via dialogues, from a multi-angel approach that combines Artificial Intelligence, Human-Computer Interaction, and social sciences, to develop expert AI systems for everyone.

Bio:

Weiyan Shi is a postdoc at Stanford NLP and an incoming assistant professor at Northeastern University starting in Fall 2024. Her research interests are in Natural Language Processing (NLP), especially in social influence dialogue systems such as persuasion, negotiation, and recommendation. She has also worked on privacy-preserving NLP applications. She is recognized as a Rising Star in Machine Learning by the University of Maryland. Her work on personalized persuasive dialogue systems was nominated for ACL 2019 best paper. She was also a core team member behind a Science publication on the first negotiation AI agent, Cicero, that achieves a human level in the game of Diplomacy. This work has been featured in The New York Times, The Washington Post, MIT Technology Review, Forbes, and other major media outlets.