Skip to main content

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

  • Conference paper
  • First Online:
Human-Computer Interaction – INTERACT 2021 (INTERACT 2021)

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Interaction is limited to providing recordings and some meta-data (e.g., title, tags).

  2. 2.

    https://doi.org/10.5281/zenodo.4793395.

References

  1. Aguiar, L.: Let the music play? Free streaming and its effects on digital music consumption. Inf. Econ. Policy 41, 1–14 (2017). https://doi.org/10.1016/j.infoecopol.2017.06.002

    Article  Google Scholar 

  2. Aguiar, L., Waldfogel, J.: Platforms, promotion, and product discovery: evidence from spotify playlists. Tech. rep, National Bureau of Economic Research (2018)

    Google Scholar 

  3. Akimchuk, D., Clerico, T., Turnbull, D.: Evaluating recommender system algorithms for generating local music playlists (2019). https://arxiv.org/abs/1907.08687

  4. Andersen, K., Knees, P.: Conversations with expert users in music retrieval and research challenges for creative MIR. In: Proceedings of the 17th International Society for Music Information Retrieval Conference, ISMIR 2016, pp. 122–128 (2016)

    Google Scholar 

  5. Anderson, A., Maystre, L., Anderson, I., Mehrotra, R., Lalmas, M.: Algorithmic effects on the diversity of consumption on spotify. In: Proceedings of The Web Conference 2020, WWW 2020, pp. 2155–2165 (2020)

    Google Scholar 

  6. Anderson, C.: The long tail. Wired, January 2004. https://www.wired.com/2004/10/tail/

  7. Anderson, C.: The Long Tail: Why the Future of Business is Selling Less of More. Hyperion, New York (2006)

    Google Scholar 

  8. Baeza-Yates, R.: Data and algorithmic bias in the web, New York, NY, USA (2016). https://doi.org/10.1145/2908131.2908135

  9. Bauer, C.: Allowing for equal opportunities for artists in music recommendation: a position paper. In: Proceedings of the 1st Workshop on Designing Human-Centric Music Information Research Systems, wsHCMIR 2019, Delft, The Netherlands, pp. 16–18 (2019)

    Google Scholar 

  10. Bauer, C.: Report on the ISMIR 2020 special session: how do we help artists? ACM SIGIR Forum 54(2), 1–7 (2020). http://sigir.org/wp-content/uploads/2020/12/p15.pdf

  11. Bauer, C., Kholodylo, M., Strauss, C.: Music recommender systems challenges and opportunities for non-superstar artists. In: Proceedings of the 30th Bled eConference, Bled, Slovenia, pp. 21–32 (2017)

    Google Scholar 

  12. Bauer, C., Schedl, M.: Global and country-specific mainstreaminess measures: definitions, analysis, and usage for improving personalized music recommendation systems. PLOS ONE 14(6), 1–36 (2019). https://doi.org/10.1371/journal.pone.0217389

  13. Baym, N.K.: Playing to the Crowd: Musicians, Audiences, and the Intimate Work of Connection, vol. 14. NYU Press, New York (2018)

    Google Scholar 

  14. Biega, A.J., Gummadi, K.P., Weikum, G.: Equity of attention: amortizing individual fairness in rankings. In: The 41st International ACM Conference on Research & Development in Information Retrieval, SIGIR 2018, pp. 405–414 (2018)

    Google Scholar 

  15. Binns, R.: On the apparent conflict between individual and group fairness. In: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, FAT* 2020, pp. 514–524 (2020). https://doi.org/10.1145/3351095.3372864

  16. Celma, O.: Music recommendation and discovery: The Long Tail, Long Fail, and Long Play in the Digital Music Space. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13287-2

  17. Coelho, M.P., Mendes, J.Z.: Digital music and the “death of the long tail’’. J. Bus. Res. 101, 454–460 (2019)

    Article  Google Scholar 

  18. Cramer, H., Garcia-Gathright, J., Reddy, S., Springer, A., Takeo Bouyer, R.: Translation, tracks & data: an algorithmic bias effort in practice. In: Extended Abstracts of the 2019 Conference on Human Factors in Computing Systems, CHI EA 2019, pp. 1–8 (2019). https://doi.org/10.1145/3290607.3299057

  19. Cramer, H., Garcia-Gathright, J., Springer, A., Reddy, S.: Assessing and addressing algorithmic bias in practice. Interactions 25(6), 58–63 (2018)

    Google Scholar 

  20. Creswell, J.W., Poth, C.N.: Qualitative Inquiry and Research Design: Choosing Among Five Approaches. Sage Publications, Thousand Oaks (2016)

    Google Scholar 

  21. Dwork, C., Hardt, M., Pitassi, T., Reingold, O., Zemel, R.: Fairness through awareness. In: Proceedings of the 3rd Innovations in Theoretical Computer Science Conference, ITCS 2012, pp. 214–226 (2012). https://doi.org/10.1145/2090236.2090255

  22. Farnadi, G., Kouki, P., Thompson, S.K., Srinivasan, S., Getoor, L.: A fairness-aware hybrid recommender system. arXiv preprint arXiv:1809.09030 (2018), https://arxiv.org/abs/1809.09030

  23. Ferraro, A., Bogdanov, D., Serra, X., Yoon, J.: Artist and style exposure bias in collaborative filtering based music recommendations. In: Proceedings of the 1st Workshop on Designing Human-Centric Music Information Research Systems, wsHCMIR 2019, Delft, The Netherlands, pp. 8–10 (2019)

    Google Scholar 

  24. Ferraro, A., Jannach, D., Serra, X.: Exploring longitudinal effects of session-based recommendations. In: 14th ACM Conference on Recommender Systems, RecSys 2020, pp. 474–479 (2020). https://doi.org/10.1145/3383313.3412213

  25. Ferraro, A., Jeon, J.H., Kim, B., Serra, X., Bogdanov, D.: Artist biases in collaborative filtering for music recommendation. In: Machine Learning for Media Discovery Workshop at International Conference on Machine Learning (ICML) (2020)

    Google Scholar 

  26. Ferraro, A., Serra, X., Bauer, C.: Break the loop: Gender imbalance in music recommenders. In: 6th ACM SIGIR Conference on Human Information Interaction and Retrieval, CHIIR 2021, New York, NY, USA, pp. 249–254. ACM (2021). https://doi.org/10.1145/3406522.3446033

  27. Feuerriegel, S., Dolata, M., Schwabe, G.: Fair AI. Bus. Inf. Syst. Eng. 62(4), 379–384 (2020). https://doi.org/10.1007/s12599-020-00650-3

  28. Fleder, D., Hosanagar, K.: Blockbuster culture’s next rise or fall: the impact of recommender systems on sales diversity. Manag. Sci. 55(5), 697–712 (2009)

    Article  Google Scholar 

  29. Guest, G., Bunce, A., Johnson, L.: How many interviews are enough?: an experiment with data saturation and variability. Field Methods 18(1), 59–82 (2006). https://doi.org/10.1177/1525822X05279903

  30. Harrison, G., Hanson, J., Jacinto, C., Ramirez, J., Ur, B.: An empirical study on the perceived fairness of realistic, imperfect machine learning models. In: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, FAT* 2020, New York, NY, USA, pp. 392–402. ACM (2020). https://doi.org/10.1145/3351095.3372831

  31. Helberger, N., Araujo, T., de Vreese, C.H.: Who is the fairest of them all? public attitudes and expectations regarding automated decision-making. Comput. Law Secur. Rev. 39, 105456 (2020). https://doi.org/10.1016/j.clsr.2020.105456

    Article  Google Scholar 

  32. Hofstede, G., Hofstede, G.J., Minkov, M.: Cultures and Organizations: Software of the Mind, vol, 3rd, revised. edn. McGraw-Hill, New York (2010)

    Google Scholar 

  33. Holstein, K., Wortman Vaughan, J., Daumé, H., Dudik, M., Wallach, H.: In: Improving fairness in machine learning systems: What do industry practitioners need? , New York, NY, USA (2019). https://doi.org/10.1145/3290605.3300830

  34. Holzapfel, A., Sturm, B., Coeckelbergh, M.: Ethical dimensions of music information retrieval technology. Trans. Int. Soc. Music Inf. Retr. 1(1), 44–55 (2018)

    Google Scholar 

  35. Hutchinson, B., Mitchell, M.: 50 years of test (un)fairness: lessons for machine learning. In: Proceedings of the Conference on Fairness, Accountability, and Transparency, FAT* 2019, pp. 49–58 (2019). https://doi.org/10.1145/3287560.3287600

  36. Jannach, D., Bauer, C.: Escaping the McNamara fallacy: toward more impactful recommender systems research. AI Mag. 41(4), 79–95 (2020). https://doi.org/10.1609/aimag.v41i4.5312

  37. Kowald, D., Müllner, P., Zangerle, E., Bauer, C., Schedl, M., Lex, E.: Support the underground: characteristics of beyond-mainstream music listeners. EPJ Data. Science 10(1) (2021). https://doi.org/10.1140/epjds/s13688-021-00268-9

  38. Kunaver, M., Požrl, T.: Diversity in recommender systems - a survey. Knowl. Based Syst. 123, 154–162 (2017). https://doi.org/10.1016/j.knosys.2017.02.009

    Article  Google Scholar 

  39. Madaio, M.A., Stark, L., Wortman Vaughan, J., Wallach, H.: In: Co-designing checklists to understand organizational challenges and opportunities around fairness in AI. New York, NY, USA (2020). https://doi.org/10.1145/3313831.3376445

  40. Marlin, B., Zemel, R.S., Roweis, S., Slaney, M.: Collaborative filtering and the missing at random assumption. arXiv preprint arXiv:1206.5267 (2012), https://arxiv.org/abs/1206.5267

  41. Mayring, P.: Qualitative Content Analysis. In: A Companion to Qualitative Research, chap. 5.12, pp. 159–176. SAGE, London (2004)

    Google Scholar 

  42. Mehrotra, R., McInerney, J., Bouchard, H., Lalmas, M., Diaz, F.: Towards a fair marketplace: counterfactual evaluation of the trade-off between relevance, fairness & satisfaction in recommendation systems. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, CIKM 2018, pp. 2243–2251 (2018). https://doi.org/10.1145/3269206.3272027

  43. Milano, S., Taddeo, M., Floridi, L.: Recommender systems and their ethical challenges. AI Soc. (2020). https://doi.org/10.1007/s00146-020-00950-y

  44. Morse, J.M.: Designing funded qualitative research. In: Handbook of Qualitative Research, pp. 220–235. Sage Publications, Thousand Oaks (1994)

    Google Scholar 

  45. Murthy, Y.V.S., Koolagudi, S.G.: Content-based music information retrieval (cb-mir) and its applications toward the music industry: A review. ACM Computing Survey 51(3) (2018). https://doi.org/10.1145/3177849

  46. Rosen, S.: The economics of superstars. Am. Econ. Rev. 71(5), 845–858 (1981). http://www.jstor.org/stable/1803469

  47. Sapiezynski, P., Zeng, W., E Robertson, R., Mislove, A., Wilson, C.: Quantifying the impact of user attention on fair group representation in ranked lists. In: Proceedings of The 2019 World Wide Web Conference, WWW 2019, pp. 553–562 (2019). https://doi.org/10.1145/3308560.3317595

  48. Selbst, A.D., Boyd, D., Friedler, S.A., Venkatasubramanian, S., Vertesi, J.: Fairness and abstraction in sociotechnical systems. In: Proc. of the Conference on Fairness, Accountability, and Transparency, FAT* 2019, pp. 59–68 (2019). https://doi.org/10.1145/3287560.3287598

  49. Singh, A., Joachims, T.: Fairness of exposure in rankings. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, SIGKDD 2018, pp. 2219–2228 (2018). https://doi.org/10.1145/3219819.3220088

  50. Sonboli, N., Smith, J.J., Berenfus, F.C., Burke, R., Fiesler, C.: Fairness and transparency in recommendation: the users’ perspective. arXiv preprint arXiv:2103.08786 (2021). https://arxiv.org/abs/2103.08786

  51. Srivastava, M., Heidari, H., Krause, A.: Mathematical notions vs. human perception of fairness: a descriptive approach to fairness for machine learning, KDD 2019, New York, NY, USA, pp. 2459–2468. ACM (2019). https://doi.org/10.1145/3292500.3330664

  52. Turnbull, D., Waldner, L.: Local music event recommendation with long tail artists (2018). https://arxiv.org/abs/1809.02277

  53. Vall, A., Quadrana, M., Schedl, M., Widmer, G.: Order, context and popularity bias in next-song recommendations. Int. J. Multimed. Inf. Retr. 8(2), 101–113 (2019). https://doi.org/10.1007/s13735-019-00169-8

    Article  Google Scholar 

  54. Wang, R., Harper, F.M., Zhu, H.: Factors influencing perceived fairness in algorithmic decision-making: Algorithm outcomes, development procedures, and individual differences. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, CHI 2020, New York, NY, USA, pp. 1–14. ACM (2020). https://doi.org/10.1145/3313831.3376813

  55. Way, S.F., Garcia-Gathright, J., Cramer, H.: Local trends in global music streaming. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 705–714 (2020)

    Google Scholar 

  56. Woodruff, A., Fox, S.E., Rousso-Schindler, S., Warshaw, J.: A qualitative exploration of perceptions of algorithmic fairness. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, CHI 2018, New York, NY, USA, pp. 1–14. ACM (2018). https://doi.org/10.1145/3173574.3174230

  57. Yao, S., Huang, B.: Beyond parity: Fairness objectives for collaborative filtering. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS 2017, pp. 2925–2934 (2017). https://doi.org/10.5555/3294996.3295052

Download references

Acknowledgments

This research was partially supported by Kakao Corp.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Christine Bauer .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 IFIP International Federation for Information Processing

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ferraro, A., Serra, X., Bauer, C. (2021). What Is Fair? Exploring the Artists’ Perspective on the Fairness of Music Streaming Platforms. In: Ardito, C., et al. Human-Computer Interaction – INTERACT 2021. INTERACT 2021. Lecture Notes in Computer Science(), vol 12933. Springer, Cham. https://doi.org/10.1007/978-3-030-85616-8_33

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-85616-8_33

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-85615-1

  • Online ISBN: 978-3-030-85616-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics