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Computer Science > Computers and Society

arXiv:1710.11214 (cs)
[Submitted on 30 Oct 2017 (v1), last revised 27 Nov 2018 (this version, v2)]

Title:How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility

Authors: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.
Subjects: Computers and Society (cs.CY); Machine Learning (cs.LG); Machine Learning (stat.ML)
Cite as: arXiv:1710.11214 [cs.CY]
  (or arXiv:1710.11214v2 [cs.CY] for this version)
  https://doi.org/10.48550/arXiv.1710.11214
Related DOI: https://doi.org/10.1145/3240323.3240370

Submission history

From: Allison Chaney [view email]
[v1] Mon, 30 Oct 2017 19:42:02 UTC (3,025 KB)
[v2] Tue, 27 Nov 2018 01:58:30 UTC (783 KB)
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