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Activity Number:
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384
- Artificial Intelligence Meets Behavioral Science: Innovations in Discovering and Leveraging Nudges
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Type:
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Invited
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Date/Time:
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Tuesday, July 30, 2019 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistics in Marketing
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Abstract #300299
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Presentation 1
Presentation 2
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Title:
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How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility
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Author(s):
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Allison Chaney* and Brandon Stewart and Barbara Engelhardt
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Companies:
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Duke University and Princeton University and Princeton University
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Keywords:
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recommendation systems;
algorithmic confounding
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Abstract:
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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. We demonstrate how using data confounded in this way homogenizes user behavior without increasing utility.
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Authors who are presenting talks have a * after their name.
