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Engineering a Safer Recommender System
Liu Leqi · Sarah Dean

While recommender systems suffuse our daily life, influencing information we receive, products we purchase, and beliefs we form, few works have systematically examined the safety of these systems. This can be partly attributed to the complex feedback loops. In this work, we take a systems safety perspective and focus on a particular feedback loop in recommender systems where users react to recommendations they receive. We characterize the difficulties of designing a safe recommender within this feedback loop. Further, we connect the causes of widely covered recommender system failures to flaws of the system in treating the feedback loop. Our analysis suggests lines of future work on designing safer recommender systems and more broadly systems that interact with people psychologically.

Author Information

Liu Leqi (Carnegie Mellon University)
Sarah Dean (Cornell University)

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