A key insight from research on organizational justice is that fairness is in the eye of the
beholder. With increasing discussions–especially among computer scientists and …

Nowadays, platforms in many industries offer content for a (monthly) flat rate (eg, music
streaming). While flat rates are efficient in reducing transaction costs for administering …

Recommendation, information retrieval, and other information access systems pose unique
challenges for investigating and applying the fairness and non-discrimination concepts that …

Recommender systems have a variety of stakeholders. Applying concepts of fairness in such
systems requires attention to stakeholders' complex and often-conflicting needs. Since …

Fairness metrics have become a useful tool to measure how fair or unfair a machine
learning system may be for its stakeholders. In the context of recommender systems …

The majority of music consumption nowadays takes place on music streaming platforms.
Whichever artists, albums, or songs are exposed to consumers on these platforms therefore …

The performance of recommender systems highly impacts both music streaming platform
users and the artists providing music. As fairness is a fundamental value of human life, there …

The consumption of music is increasingly reliant on the personalisation, recommendation,
and automated curation features of music streaming services. Using algorithm experience …

Sustainable HCI and Human-Food-Interaction research have develo** interest in
preventing food waste through food sharing. Sustainability requires attention to both the …

The algorithmic systems utilized by music streaming services have the potential to positively
influence individual choices by promoting new artists, but they are also often accused of …