Fairness in machine learning has been studied by many researchers. In particular, fairness in recommender systems has been investigated to ensure the recommendations meet certain criteria with respect to certain sensitive features such as race, gender etc. However, often recommender systems are multi-stakeholder environments in which the fairness towards all stakeholders should be taken care of. It is well-known that the recommendation algorithms suffer from popularity bias; few popular items are over-recommended which leads to the majority of other items not getting proportionate attention. This bias has been investigated from the perspective of the users and how it makes the final recommendations skewed towards popular items in general. In this paper, however, we investigate the impact of popularity bias in recommendation algorithms on the provider of the items (i.e. the entities who are behind the recommended items). Using a music dataset for our experiments, we show that, due to some biases in the algorithms, different groups of artists with varying degrees of popularity are systematically and consistently treated differently than others.

中文翻译：

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音乐推荐中艺人不公平曝光

许多研究人员已经研究了机器学习中的公平性。特别是，已经研究了推荐系统的公平性，以确保推荐满足某些敏感特征（例如种族、性别等）的某些标准。 然而，推荐系统通常是多利益相关者环境，在这种环境中应该对所有利益相关者采取公平性照顾。众所周知，推荐算法存在流行性偏差；很少有人过度推荐热门商品，这导致大多数其他商品没有得到相应的关注。已经从用户的角度研究了这种偏见，以及它如何使最终推荐偏向于普遍的流行项目。然而，在本文中，我们调查了推荐算法中流行偏差对项目提供者（即推荐项目背后的实体）的影响。在我们的实验中使用音乐数据集，我们表明，由于算法中的一些偏见，具有不同受欢迎程度的不同艺术家群体被系统地和一致地对待与其他艺术家不同。