  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.
