CITP Lunch Seminar: Allison Chaney & Brandon Stewart – How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility

Recommendation systems are ubiquitous and impact many domains; they have the potential to influence product consumption, individual's perceptions of the world, and life-altering decisions. These systems are often evaluated or trained with data from users already exposed to algorithmic recommendations; this creates a pernicious feedback loop. We demonstrate how using data confounded in this way homogenizes user behavior without increasing utility.