Fairness and Transparency in Recommendation: The Users’ Perspective
Fairness and Transparency in Recommendation: The Users’ Perspective
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DOI：
10.1145/3450613.3456835
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发表时间：
2021-03
期刊：
影响因子：
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通讯作者：
Nasim Sonboli;Jessie J. Smith;Florencia Cabral Berenfus;R. Burke;Casey Fiesler
中科院分区：
文献类型：
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作者：
Nasim Sonboli;Jessie J. Smith;Florencia Cabral Berenfus;R. Burke;Casey Fiesler
尽管推荐系统是由个性化来定义的，但近期的研究已经表明了其他超越准确性的目标（比如公平性）的重要性。因为用户通常期望他们所得到的推荐是完全个性化的，所以在具有公平意识的推荐系统中，这些新的算法目标必须以透明的方式传达。虽然在推荐系统研究中解释有着悠久的历史，但很少有研究试图对使用公平性目标的系统进行解释。尽管人工智能其他分支的先前研究已经探索了将解释作为一种提高公平性的工具，但这些研究并没有聚焦于推荐领域。在此，我们考虑具有公平意识的推荐系统的用户视角以及提高其透明度的技术。我们描述了一项探索性访谈研究的结果，该研究调查了用户对公平性、推荐系统以及具有公平意识的目标的看法。我们根据参与者的需求提出了三个特征，这些特征可以提高用户对具有公平意识的推荐系统的理解和信任。
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.