What Is Fair? Exploring the Artists' Perspective on the Fairness of Music Streaming Platforms

被引:24
作者
Ferraro, Andres [1 ]
Serra, Xavier [1 ]
Bauer, Christine [2 ]
机构
[1] Univ Pompeu Fabra, Barcelona, Spain
[2] Univ Utrecht, Utrecht, Netherlands
来源
HUMAN-COMPUTER INTERACTION, INTERACT 2021, PT II | 2021年 / 12933卷
关键词
Music artists; Fairness; Music streaming platform; Transparency; Quotas; Lack of control; Gender balance; Music context; Influencing taste; RECOMMENDER SYSTEMS;
D O I
10.1007/978-3-030-85616-8_33
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:562 / 584
页数:23
相关论文
共 57 条
[1]   Let the music play? Free streaming and its effects on digital music consumption [J].
Aguiar, Luis .
INFORMATION ECONOMICS AND POLICY, 2017, 41 :1-14
[2]  
Aguiar Luis, 2018, Working Paper 24713
[3]  
Akimchuk D., 2019, EVALUATING RECOMMEND
[4]  
Andersen K., 2016, 17 INT SOC MUSIC INF, P122
[5]   Algorithmic Effects on the Diversity of Consumption on Spotify [J].
Anderson, Ashton ;
Maystre, Lucas ;
Mehrotra, Rishabh ;
Anderson, Ian ;
Lalmas, Mounia .
WEB CONFERENCE 2020: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2020), 2020, :2155-2165
[6]  
Anderson C., 2004, WIRED
[7]  
Anderson C., 2006, LONG TAIL WHY FUTURE
[8]  
[Anonymous], 2010, Cultures and organizations: Software of the mind: Intercultural cooperation and its importance for survival
[9]   Data and Algorithmic Bias in the Web [J].
Baeza-Yates, Ricardo .
PROCEEDINGS OF THE 2016 ACM WEB SCIENCE CONFERENCE (WEBSCI'16), 2016, :1-1
[10]  
Bauer C., 2019, PROC 1 WORKSHOP DESI, P16