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Title: Fairness and Transparency in Recommendation: The Users’ Perspective
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.  more » « less
Award ID(s):
1911025
NSF-PAR ID:
10253135
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
UMAP '21: Proceedings Of The 29th ACM Conference On User Modeling, Adaptation And Personalization
Page Range / eLocation ID:
274 to 279
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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