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arXiv · 1710.11214

How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility

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

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Allison J. B. Chaney, Brandon M. Stewart, Barbara E. Engelhardt. 2018-11-27. How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility. https://doi.org/10.1145/3240323.3240370

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