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.
@article{arxiv.1710.11214,
title = {How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility},
author = {Allison J. B. Chaney and Brandon M. Stewart and Barbara E. Engelhardt},
journal= {arXiv preprint arXiv:1710.11214},
year = {2018}
}