What is fair? Exploring the artists' perspective on the fairness of music streaming platforms
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Abstract (arXiv)
Music streaming platforms are currently among the main sources of music consumption, and the embedded recommender systems significantly influence what the users consume. There is an increasing interest to ensure that those platforms and systems are fair. Yet, we first need to understand what fairness means in such a context. Although artists are the main content providers for music platforms, there is a research gap concerning the artists' perspective. To fill this gap, we conducted interviews with music artists to understand how they are affected by current platforms and what improvements they deem necessary. Using a Qualitative Content Analysis, we identify the aspects that the artists consider relevant for fair platforms. In this paper, we discuss the following aspects derived from the interviews: fragmented presentation, reaching an audience, transparency, influencing users' listening behavior, popularity bias, artists' repertoire size, quotas for local music, gender balance, and new music. For some topics, our findings do not indicate a clear direction about the best way how music platforms should act and function; for other topics, though, there is a clear consensus among our interviewees: for these, the artists have a clear idea of the actions that should be taken so that music platforms will be fair also for the artists.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Jun 2021.
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Citing papers per year: 2023: 1; 2024: 3; 2025: 1.
5 papers.
- 2024Algorithmic Collective Action in Recommender Systems: Promoting Songs by Reordering Playlistscited by 7
- 2024Let's Get It Started: Fostering the Discoverability of New Releases on Deezercited by 5
- 2023The Role of Relevance in Fair Rankingcited by 3
- 2024MOReGIn: Multi-Objective Recommendation at the Global and Individual Levelscited by 1
- 2025FairExpand: Individual Fairness on Graphs with Partial Similarity Informationcited by 0
- 2025FairExpand: Individual Fairness on Graphs with Partial Similarity Information20 Dec 2025
- 2024Algorithmic Collective Action in Recommender Systems: Promoting Songs by Reordering Playlists19 Mar 2024
- 2024MOReGIn: Multi-Objective Recommendation at the Global and Individual Levels23 Jan 2024
- 2024Let's Get It Started: Fostering the Discoverability of New Releases on Deezer5 Jan 2024
- 2023The Role of Relevance in Fair Ranking9 May 2023