Fairness and Transparency in Recommendation: The Users’ Perspective
推荐中的公平性和透明度：用户的视角
尽管推荐系统是由个性化定义的，但最近的工作表明了附加的，超出准确性的目标（如公平性）的重要性。
因为用户通常希望他们的推荐是完全个性化的，所以这些新的算法目标必须在了解公平的推荐系统中透明地传达。
尽管解释在推荐系统研究中具有悠久的历史，但很少有尝试解释使用公平性目标的系统的工作。
即使AI的其他领域先前的工作已经探索了将解释作为增加公平性的工具，但这项工作并未集中在推荐上。
在这里，我们考虑了注重公平性的推荐系统和增强透明度的技术的用户视角。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、推荐系统和注重公平性的目标的看法。我们提出了三个特征 —— 根据参与者的需求 —— 可以提高用户对公平性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.
