Fairness and Transparency in Recommendation: The Users' Perspective

• URL: http://arxiv.org/abs/2103.08786v1
• Date: Tue, 16 Mar 2021 00:42:09 GMT
• Title: Fairness and Transparency in Recommendation: The Users' Perspective
• Authors: Nasim Sonboli and Jessie J. Smith, Florencia Cabral Berenfus, Robin Burke, Casey Fiesler
• Abstract summary: We discuss user perspectives of fairness-aware recommender systems. We propose three features that could improve user understanding of and trust in fairness-aware recommender systems.
• Score: 14.830700792215849
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Though recommender systems are defined by personalization, recent work has shown the importance of additional, beyond-accuracy objectives, such as fairness. Because users often expect their recommendations to be purely personalized, these new algorithmic objectives must be communicated transparently in a fairness-aware recommender system. While explanation has a long history in recommender systems research, there has been little work that attempts to explain systems that use a fairness objective. Even though the previous work in other branches of AI has explored the use of explanations as a tool to increase fairness, this work has not been focused on recommendation. Here, we consider user perspectives of fairness-aware recommender systems and techniques for enhancing their transparency. We describe the results of an exploratory interview study that investigates user perceptions of fairness, recommender systems, and fairness-aware objectives. We propose three features -- informed by the needs of our participants -- that could improve user understanding of and trust in fairness-aware recommender systems.

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