When machine learning models are deployed, for example in recommender systems, they can affect the distribution on which they operate. Such endogenous distribution shifts arise due to the impact of decisions on individuals, and these effects can cause issues like polarization and bias amplification. In this talk, I will discuss models of impact at a variety of levels: users consuming content, producers creating it, and learning-based services who serve it. I will draw on recent work on preference dynamics in personalized recommendation, producer competition under algorithmic curation, and multi-learner participation dynamics. Time permitting, I will introduce a perspective based on the unifying framework of dynamical systems, and outline open problems.

Bio: Sarah is an Assistant Professor in the Computer Science Department at Cornell. She is interested in the interplay between optimization, machine learning, and dynamics, and her research focuses on understanding the fundamentals of data-driven control and decision-making. This work is grounded in and inspired by applications ranging from robotics to recommendation systems. Sarah has a PhD in EECS from UC Berkeley and did a postdoc at the University of Washington.