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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