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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.
Bios: Allison Chaney is an assistant professor at Duke University’s Fuqua School of Business. Her research is focused on developing scalable and interpretable machine learning methods and understanding the impacts of these methods on individuals and society when they are deployed in real-world markets. She received her Ph.D. in Computer Science at Princeton University, advised by David Blei, and was also a postdoctoral researcher at Princeton with Barbara Engelhardt and Brandon Stewart.
Brandon Stewart is an assistant professor in the Department of Sociology and is also affiliated with the Department of Politics and the Office of Population Research. He develops new quantitative statistical methods for applications across the social sciences. Methodologically his focus is in tools which facilitate automated text analysis and model complex heterogeneity in regression. Many recent applications of these methods have centered on using large corpora of text to better understand propaganda in contemporary China. His research has been published in journals such as American Journal of Political Science, Political Analysis and the Proceedings of the Association of Computational Linguistics. His work has won the Edward R Chase Dissertation Prize, the Gosnell Prize for Excellence in Political Methodology, and the Political Analysis Editor’s Choice Award.
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