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With digital music consumption being at an all-time high, online music encyclopedia like MusicBrainz and music intelligence platforms like The Echo Nest are becoming increasingly important in identifying, organizing, and recommending music for listeners around the globe. As a byproduct, such sites collect comprehensive information about a vast amount of artists, their recorded songs, institutional support, and the collaborations between them. Using a unique mash-up of crowdsourced, curated, and algorithmically augmented data, this paper unpacks an unsolved problem that is key to promoting artistic innovation, i.e., how gender penetrates into artistic context leading to the globally perceived gender gap in the music industry. Specifically, we investigate gender-related differences in the sonic features of artists’ work, artists’ tagging by listeners, their record label affiliations, and collaboration networks. We find statistically significant disparities along all these dimensions. Moreover, the differences allow models to reliably identify the gender of songs’ creators and help elucidate the role of cultural and structural factors in sustaining inequality. Our findings contribute to a better understanding of gender differences in music production and inspire strategies that could improve the recognition of female artists and advance gender equity in artistic leadership and innovation.
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This content will become publicly available on September 14, 2026
Musicians’ ethical concerns about AI: an interview study
Generative AI in music (GAIM) technologies are rapidly transforming music production, yet little is known about how working musicians perceive and respond to these changes. This study presents findings from in-depth interviews with 43 musicians, spanning diverse genres, professional roles and experience with music technology. Our analysis, informed by a reflexive thematic analysis approach, suggests complex tensions between perceived benefits and risks of GAIM adoption. Key themes were generated around tensions between (i) fear of reduced job opportunities for professional musicians and appreciation of the potential of AI to make individual musicians more independent and productive; (ii) fear about the exploitation of artists’ work and benefits of open music exchanges; (iii) fear that AI will exacerbate inequities and recognition of AI’s potential to increase access to music production. Our findings highlight the need for careful consideration of justice and fairness in GAIM development and deployment, suggesting that different types of GAIM use (from assistant to replacement) carry distinct ethical implications. This work provides a foundation for understanding how GAIMs can be integrated into music production while respecting artists’ rights and creative agency.
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- Award ID(s):
- 2222129
- PAR ID:
- 10637113
- Publisher / Repository:
- Springer
- Date Published:
- Journal Name:
- AI & SOCIETY
- ISSN:
- 0951-5666
- Subject(s) / Keyword(s):
- Generative AI Music Ethics Copyright Creativity
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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