Paul SpiesbergerPrelude to a fairer music industry: https://fairmuse.eu/
FAIRMUSE aims to make the music sector in Europe more efficient, more competitive, and more sustainable, donating your playlist:
Konstantin Kernfragen von #FairMusE:
– Wer bekommt welche Empfehlungen?
– Wie beeinflussen Algorithmen Einkommen & Sichtbarkeit?
– Werden bestimmte Genres, Regionen oder Indies systematisch benachteiligt? 3/
Die EU‑Studie #FairMusE hat ihre Ergebnisse veröffentlicht: Sie untersucht, wie Streaming‑Algorithmen Musik empfehlen – wer was zu hören bekommt und ob alle Artists & Labels eine faire Chance haben. 1/
RE: https://bsky.app/profile/did:plc:zca4otc32qp3r3i4vbueogw7/post/3mf5q4mmfds2m
RE: https://bsky.app/profile/did:plc:zca4otc32qp3r3i4vbueogw7/post/3mf5q4mmfds2m
ListenBrainz#FairMusE, an EU-financed research project into fairness in streaming, has released its findings.
With the goal of answering: What music is rec’d by the big algorithms: Who is rec’d what, and why? Do all artists and labels get a fair chance?
Slightly hampered by services not releasing data on artist remuneration... but nonetheless some very interesting outcomes, in graphic form: https://dashboard.fairmuse.eu/
ferrie=differentieel🔻in 
Share your streaming data for research on #fairmuse
You can help us by asking your streaming service (Spotify, Apple Music or YouTube) for your music streaming data. This will contribute to our research on streaming services and how algorithms work.
look at portal.fairmuse.eu https://portal.fairmuse.eu/
MetaBrainz#ListenBrainz users: #FairMusE needs your help.
What music is rec’d by the big algorithms: Who is rec’d what, and why? Do all artists and labels get a fair chance?
This EU-financed research project aims to find out. Opt-in your LB listening data to help:
https://blog.metabrainz.org/2024/09/10/towards-fair-streaming-introducing-the-fairmuse-project/
