  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.
