How algorithmic confounding in recommendation systems increases homogeneity and decreases utility

Allison J. B. Chaney, Brandon M. Stewart, Barbara E. Engelhardt

Abstract: Recommendation systems are ubiquitous and impact many domains; they have the potential to influence product consumption, individuals' 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. Using simulations, we demonstrate how using data confounded in this way homogenizes user behavior without increasing utility.