1.
McGuire, Jack; Cremer, David De; de Cruys, Tim Van
Establishing the importance of co-creation and self-efficacy in creative collaboration with artificial intelligence Journal Article
In: Scientific Reports, vol. 14, no. 1, pp. 18525, 2024, ISSN: 2045-2322, (Publisher: Nature Publishing Group).
@article{mcguire_establishing_2024,
title = {Establishing the importance of co-creation and self-efficacy in creative collaboration with artificial intelligence},
author = {Jack McGuire and David De Cremer and Tim Van de Cruys},
url = {https://www.nature.com/articles/s41598-024-69423-2},
doi = {10.1038/s41598-024-69423-2},
issn = {2045-2322},
year = {2024},
date = {2024-08-01},
urldate = {2024-11-22},
journal = {Scientific Reports},
volume = {14},
number = {1},
pages = {18525},
abstract = {The emergence of generative AI technologies has led to an increasing number of people collaborating with AI to produce creative works. Across two experimental studies, in which we carefully designed and programmed state-of-the-art human–AI interfaces, we examine how the design of generative AI systems influences human creativity (poetry writing). First, we find that people were most creative when writing a poem on their own, compared to first receiving a poem generated by an AI system and using sophisticated tools to edit it (Study 1). Following this, we demonstrate that this creativity deficit dissipates when people co-create with—not edit—AI and establish creative self-efficacy as an important mechanism in this process (Study 2). Thus, our findings indicate that people must occupy the role of a co-creator, not an editor, to reap the benefits of generative AI in the production of creative works.},
note = {Publisher: Nature Publishing Group},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The emergence of generative AI technologies has led to an increasing number of people collaborating with AI to produce creative works. Across two experimental studies, in which we carefully designed and programmed state-of-the-art human–AI interfaces, we examine how the design of generative AI systems influences human creativity (poetry writing). First, we find that people were most creative when writing a poem on their own, compared to first receiving a poem generated by an AI system and using sophisticated tools to edit it (Study 1). Following this, we demonstrate that this creativity deficit dissipates when people co-create with—not edit—AI and establish creative self-efficacy as an important mechanism in this process (Study 2). Thus, our findings indicate that people must occupy the role of a co-creator, not an editor, to reap the benefits of generative AI in the production of creative works.
2.
Hsu, Greta; Bechky, Beth A.
Exploring the Digital Undertow: How generative AI impacts social categorizations in creative work Journal Article
In: Organization Theory, vol. 5, no. 3, pp. 26317877241275118, 2024, ISSN: 2631-7877, (Publisher: SAGE Publications Ltd).
@article{hsu_exploring_2024,
title = {Exploring the Digital Undertow: How generative AI impacts social categorizations in creative work},
author = {Greta Hsu and Beth A. Bechky},
url = {https://doi.org/10.1177/26317877241275118},
doi = {10.1177/26317877241275118},
issn = {2631-7877},
year = {2024},
date = {2024-07-01},
urldate = {2024-11-22},
journal = {Organization Theory},
volume = {5},
number = {3},
pages = {26317877241275118},
abstract = {This paper examines generative AI’s broader implications for the social construction of categories. Building on the Orlikowski and Scott’s concept of the ‘digital undertow’, we consider how generative AI’s influence will likely extend beyond immediate technological benefits and lead to deeper shifts in the societal structures and occupational identities constituting conventional categories. We extrapolate from emerging findings that suggest that, while generative AI improves efficiency and the average quality of a creative product, it also tends to reduce the advantages of expertise and induce a homogenization of what is creatively produced as outputs. We consider how such dynamics might play out in the specific case of the changing roles of screenwriters and studio executives within the television and film industries. With this focused thought experiment, our overall aim is to draw attention to the broader implications of change brought forth by this technological innovation.},
note = {Publisher: SAGE Publications Ltd},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This paper examines generative AI’s broader implications for the social construction of categories. Building on the Orlikowski and Scott’s concept of the ‘digital undertow’, we consider how generative AI’s influence will likely extend beyond immediate technological benefits and lead to deeper shifts in the societal structures and occupational identities constituting conventional categories. We extrapolate from emerging findings that suggest that, while generative AI improves efficiency and the average quality of a creative product, it also tends to reduce the advantages of expertise and induce a homogenization of what is creatively produced as outputs. We consider how such dynamics might play out in the specific case of the changing roles of screenwriters and studio executives within the television and film industries. With this focused thought experiment, our overall aim is to draw attention to the broader implications of change brought forth by this technological innovation.
3.
Wingström, Roosa; Hautala, Johanna; Lundman, Riina
Redefining Creativity in the Era of AI? Perspectives of Computer Scientists and New Media Artists Journal Article
In: Creativity Research Journal, vol. 36, no. 2, pp. 177–193, 2024, ISSN: 1040-0419, (Publisher: Routledge _eprint: https://doi.org/10.1080/10400419.2022.2107850).
@article{wingstrom_redefining_2024,
title = {Redefining Creativity in the Era of AI? Perspectives of Computer Scientists and New Media Artists},
author = {Roosa Wingström and Johanna Hautala and Riina Lundman},
url = {https://doi.org/10.1080/10400419.2022.2107850},
doi = {10.1080/10400419.2022.2107850},
issn = {1040-0419},
year = {2024},
date = {2024-04-01},
urldate = {2024-11-22},
journal = {Creativity Research Journal},
volume = {36},
number = {2},
pages = {177–193},
abstract = {Artificial intelligence (AI) has breached creativity research. The advancements of creative AI systems dispute the common definitions of creativity that have traditionally focused on five elements: actor, process, outcome, domain, and space. Moreover, creative workers, such as scientists and artists, increasingly use AI in their creative processes, and the concept of co-creativity has emerged to describe blended human–AI creativity. These issues evoke the question of whether creativity requires redefinition in the era of AI. Currently, co-creativity is mostly studied within the framework of computer science in pre-organized laboratory settings. This study contributes from a human scientific perspective with 52 interviews of Finland-based computer scientists and new media artists who use AI in their work. The results suggest scientists and artists use similar elements to define creativity. However, the role of AI differs between the scientific and artistic creative processes. Scientists need AI to produce accurate and trustworthy outcomes, whereas artists use AI to explore and play. Unlike the scientists, some artists also considered their work with AI co-creative. We suggest that co-creativity can explain the contemporary creative processes in the era of AI and should be the focal point of future creativity research.},
note = {Publisher: Routledge
_eprint: https://doi.org/10.1080/10400419.2022.2107850},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Artificial intelligence (AI) has breached creativity research. The advancements of creative AI systems dispute the common definitions of creativity that have traditionally focused on five elements: actor, process, outcome, domain, and space. Moreover, creative workers, such as scientists and artists, increasingly use AI in their creative processes, and the concept of co-creativity has emerged to describe blended human–AI creativity. These issues evoke the question of whether creativity requires redefinition in the era of AI. Currently, co-creativity is mostly studied within the framework of computer science in pre-organized laboratory settings. This study contributes from a human scientific perspective with 52 interviews of Finland-based computer scientists and new media artists who use AI in their work. The results suggest scientists and artists use similar elements to define creativity. However, the role of AI differs between the scientific and artistic creative processes. Scientists need AI to produce accurate and trustworthy outcomes, whereas artists use AI to explore and play. Unlike the scientists, some artists also considered their work with AI co-creative. We suggest that co-creativity can explain the contemporary creative processes in the era of AI and should be the focal point of future creativity research.
4.
Erickson, Kristofer
AI and work in the creative industries: digital continuity or discontinuity? Journal Article
In: Creative Industries Journal, pp. 1–21, 2024, ISSN: 1751-0694, (Publisher: Routledge).
@article{erickson_ai_nodate,
title = {AI and work in the creative industries: digital continuity or discontinuity?},
author = {Kristofer Erickson},
url = {https://www.tandfonline.com/doi/full/10.1080/17510694.2024.2421135},
doi = {10.1080/17510694.2024.2421135},
issn = {1751-0694},
year = {2024},
date = {2024-00-00},
journal = {Creative Industries Journal},
pages = {1–21},
note = {Publisher: Routledge},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
5.
Mahmud, Bahar; Hong, Guan; Fong, Bernard
A Study of Human–AI Symbiosis for Creative Work: Recent Developments and Future Directions in Deep Learning Journal Article
In: ACM Trans. Multimedia Comput. Commun. Appl., vol. 20, no. 2, pp. 47:1–47:21, 2023, ISSN: 1551-6857.
@article{mahmud_study_2023,
title = {A Study of Human–AI Symbiosis for Creative Work: Recent Developments and Future Directions in Deep Learning},
author = {Bahar Mahmud and Guan Hong and Bernard Fong},
url = {https://doi.org/10.1145/3542698},
doi = {10.1145/3542698},
issn = {1551-6857},
year = {2023},
date = {2023-09-01},
urldate = {2024-11-22},
journal = {ACM Trans. Multimedia Comput. Commun. Appl.},
volume = {20},
number = {2},
pages = {47:1–47:21},
abstract = {Recent advances in Artificial Intelligence (AI), particularly deep learning, are having an enormous impact on our society today. Record numbers of jobs previously held by people have been automated, from manufacturing to transportation to customer services. The concerns of AI replacing humans by taking over people's jobs need to be urgently addressed. This article investigates some promising different directions of AI development: Instead of using AI to replace people, we should use AI to team up with people so that both can work better and smarter. Human–AI symbiosis refers to people and AI working together to jointly solve problems and perform specific tasks. The recent developments in deep learning models and frameworks have significantly improved the efficiency and performance of human and AI collaborations. In this article, some research work on human–AI collaborative environments has been extensively studied and analyzed to reveal the progress in this field. Although the teaming of humans and machines includes many complex tasks, the development has been very promising. One of the main goals in this field is to develop additional capabilities in machines capable of being successful teammates with a human partner. The correctness of the outcomes is often determined by the underlying technology and how performance and human satisfaction are measured through the collaborative nature of the system. We conclude that the teaming of humans and AI, particularly deep learning, has the advantage of combining the power of AI with the human domain expertise to improve performance and create value. Human–AI symbiosis could be a promising future direction for AI's continuing integration into the world.},
keywords = {},
pubstate = {published},
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}
Recent advances in Artificial Intelligence (AI), particularly deep learning, are having an enormous impact on our society today. Record numbers of jobs previously held by people have been automated, from manufacturing to transportation to customer services. The concerns of AI replacing humans by taking over people's jobs need to be urgently addressed. This article investigates some promising different directions of AI development: Instead of using AI to replace people, we should use AI to team up with people so that both can work better and smarter. Human–AI symbiosis refers to people and AI working together to jointly solve problems and perform specific tasks. The recent developments in deep learning models and frameworks have significantly improved the efficiency and performance of human and AI collaborations. In this article, some research work on human–AI collaborative environments has been extensively studied and analyzed to reveal the progress in this field. Although the teaming of humans and machines includes many complex tasks, the development has been very promising. One of the main goals in this field is to develop additional capabilities in machines capable of being successful teammates with a human partner. The correctness of the outcomes is often determined by the underlying technology and how performance and human satisfaction are measured through the collaborative nature of the system. We conclude that the teaming of humans and AI, particularly deep learning, has the advantage of combining the power of AI with the human domain expertise to improve performance and create value. Human–AI symbiosis could be a promising future direction for AI's continuing integration into the world.
6.
Hitsuwari, Jimpei; Ueda, Yoshiyuki; Yun, Woojin; Nomura, Michio
Does human–AI collaboration lead to more creative art? Aesthetic evaluation of human-made and AI-generated haiku poetry Journal Article
In: Computers in Human Behavior, vol. 139, pp. 107502, 2023, ISSN: 0747-5632.
@article{hitsuwari_does_2023,
title = {Does human–AI collaboration lead to more creative art? Aesthetic evaluation of human-made and AI-generated haiku poetry},
author = {Jimpei Hitsuwari and Yoshiyuki Ueda and Woojin Yun and Michio Nomura},
url = {https://www.sciencedirect.com/science/article/pii/S0747563222003223},
doi = {10.1016/j.chb.2022.107502},
issn = {0747-5632},
year = {2023},
date = {2023-02-01},
urldate = {2024-11-22},
journal = {Computers in Human Behavior},
volume = {139},
pages = {107502},
abstract = {With the development of technology, the quality of AI-generated text has improved. This is relevant in the AI art field, where AI generates literature or poetry that is appreciated. This study compared human-made and AI-generated haiku poetry, which is composed with 17 syllables and the world's shortest and clearest rules, to examine aesthetic evaluations of AI art and people's beliefs about it. AI-generated haiku were divided into those with and without human intervention. Two tasks were completed by 385 participants. The first involved evaluating human-made and AI-generated haiku on 21 items, such as beauty. The second involved determining whether the haiku were human-made or AI-generated. The results showed that the beauty rating of the AI-generated haiku with the human intervention was the highest, and those of the human-made and AI-generated haiku without human intervention were equal. Furthermore, participants could not distinguish between human-made and AI-generated haiku. These results suggest that human–AI collaboration has better creativity in haiku production. Moreover, a negative correlation was found between discrimination performance and beauty rating in AI-generated haiku, suggesting that high-quality AI-generated work is believed to be human-made. This study indicates the potential of human–AI collaboration in haiku and the underestimation of AI art due to algorithm aversion.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
With the development of technology, the quality of AI-generated text has improved. This is relevant in the AI art field, where AI generates literature or poetry that is appreciated. This study compared human-made and AI-generated haiku poetry, which is composed with 17 syllables and the world's shortest and clearest rules, to examine aesthetic evaluations of AI art and people's beliefs about it. AI-generated haiku were divided into those with and without human intervention. Two tasks were completed by 385 participants. The first involved evaluating human-made and AI-generated haiku on 21 items, such as beauty. The second involved determining whether the haiku were human-made or AI-generated. The results showed that the beauty rating of the AI-generated haiku with the human intervention was the highest, and those of the human-made and AI-generated haiku without human intervention were equal. Furthermore, participants could not distinguish between human-made and AI-generated haiku. These results suggest that human–AI collaboration has better creativity in haiku production. Moreover, a negative correlation was found between discrimination performance and beauty rating in AI-generated haiku, suggesting that high-quality AI-generated work is believed to be human-made. This study indicates the potential of human–AI collaboration in haiku and the underestimation of AI art due to algorithm aversion.
7.
Lundman, Riina; Nordström, Paulina
In: Transactions of the Institute of British Geographers, vol. 48, no. 3, pp. 650–664, 2023, ISSN: 1475-5661, (_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/tran.12608).
@article{lundman_creative_2023,
title = {Creative geographies in the age of AI: Co-creative spatiality and the emerging techno-material relations between artists and artificial intelligence},
author = {Riina Lundman and Paulina Nordström},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/tran.12608},
doi = {10.1111/tran.12608},
issn = {1475-5661},
year = {2023},
date = {2023-01-01},
urldate = {2024-11-22},
journal = {Transactions of the Institute of British Geographers},
volume = {48},
number = {3},
pages = {650–664},
abstract = {Artificial intelligence (AI) expands the more-than-human perspective on creativity and creative geographies, as new techno-material relations and spatialities are formed when humans and AI create together. In this paper, we suggest the concept of ‘co-creative spatiality’ to refer to the special sites, relations and processes of human–AI collaboration in artistic creative practice. Our study builds on interviews with 26 Finland-based artists who have used AI in their artistic work. In our analysis, we focus on the questions of what, how, where, who and with of creativity, through which we study the spatialities of creativity and their links to AI. We discuss, how the emerging techno-materialities of AI affect co-creative spatiality by stretching the boundaries of (human) imagination and sparking creativity across new imaginative terrains. Co-creative spatiality reveals novel and unfamiliar collaborations that constitute our material worlds and, therefore, we believe it invites geographical analysis from all those interested in the transformations of creative and artistic practices in the age of AI.},
note = {_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/tran.12608},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Artificial intelligence (AI) expands the more-than-human perspective on creativity and creative geographies, as new techno-material relations and spatialities are formed when humans and AI create together. In this paper, we suggest the concept of ‘co-creative spatiality’ to refer to the special sites, relations and processes of human–AI collaboration in artistic creative practice. Our study builds on interviews with 26 Finland-based artists who have used AI in their artistic work. In our analysis, we focus on the questions of what, how, where, who and with of creativity, through which we study the spatialities of creativity and their links to AI. We discuss, how the emerging techno-materialities of AI affect co-creative spatiality by stretching the boundaries of (human) imagination and sparking creativity across new imaginative terrains. Co-creative spatiality reveals novel and unfamiliar collaborations that constitute our material worlds and, therefore, we believe it invites geographical analysis from all those interested in the transformations of creative and artistic practices in the age of AI.
8.
Hong, Joo-Wha; Fischer, Katrin; Ha, Yul; Zeng, Yilei
Human, I wrote a song for you: An experiment testing the influence of machines’ attributes on the AI-composed music evaluation Journal Article
In: Computers in Human Behavior, vol. 131, pp. 107239, 2022, ISSN: 0747-5632.
@article{hong_human_2022,
title = {Human, I wrote a song for you: An experiment testing the influence of machines’ attributes on the AI-composed music evaluation},
author = {Joo-Wha Hong and Katrin Fischer and Yul Ha and Yilei Zeng},
url = {https://www.sciencedirect.com/science/article/pii/S0747563222000619},
doi = {10.1016/j.chb.2022.107239},
issn = {0747-5632},
year = {2022},
date = {2022-06-01},
urldate = {2024-11-22},
journal = {Computers in Human Behavior},
volume = {131},
pages = {107239},
abstract = {This study examines the evaluation of musical performances of artificial intelligence (AI) and the acceptance of AI music generators as musicians. Relying on theoretical frameworks of anthropomorphism and creative machine heuristics, a 2 x 2 experiment is designed, where both the perceived anthropomorphism of AI (high vs. low anthropomorphism) and its autonomy of creativity (independent vs. dependent creativity) are controlled. The study found that humanlike traits of an AI music generator led it to be accepted as a musician. However, whether it was autonomous when creating songs did not influence its perception as a genuine musician. Also, the evaluation of its songs was done independently from its attributes. Still, people who perceived the AI music generator as a musician appreciated its songs more than those who did not. The implications of the expected findings are discussed.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This study examines the evaluation of musical performances of artificial intelligence (AI) and the acceptance of AI music generators as musicians. Relying on theoretical frameworks of anthropomorphism and creative machine heuristics, a 2 x 2 experiment is designed, where both the perceived anthropomorphism of AI (high vs. low anthropomorphism) and its autonomy of creativity (independent vs. dependent creativity) are controlled. The study found that humanlike traits of an AI music generator led it to be accepted as a musician. However, whether it was autonomous when creating songs did not influence its perception as a genuine musician. Also, the evaluation of its songs was done independently from its attributes. Still, people who perceived the AI music generator as a musician appreciated its songs more than those who did not. The implications of the expected findings are discussed.
9.
Paesano, Andrea
Artificial intelligence and creative activities inside organizational behavior Journal Article
In: International Journal of Organizational Analysis, vol. 31, no. 5, pp. 1694–1723, 2021, ISSN: 1934-8835, (Publisher: Emerald Publishing Limited).
@article{paesano_artificial_2021,
title = {Artificial intelligence and creative activities inside organizational behavior},
author = {Andrea Paesano},
url = {https://www.emerald.com/insight/content/doi/10.1108/ijoa-09-2020-2421/full/html},
doi = {10.1108/IJOA-09-2020-2421},
issn = {1934-8835},
year = {2021},
date = {2021-11-01},
urldate = {2024-11-22},
journal = {International Journal of Organizational Analysis},
volume = {31},
number = {5},
pages = {1694–1723},
abstract = {This study aims to investigate about the use of artificial intelligence (AI) (man machine relationship) regarding organizational behavior. The aim of this research paper is to analyze whether the current AI is used also to replace man in “creative” activities.,This study is based on a qualitative and explorative approach. It is made a review of the literature with “Scopus” and “Web of Science” databases. The research fields are AI, organizational behavior, man-machine relationship and creativity.,Analyzing whether the intensive use of AI in organizational behavior can replace human work in creative activities.,The connection of AI with creative activities within the organization is only just beginning. For this reason, other sources, like Harvard Business Review, public reports and professional papers found on the internet have been considered. The most important limitation of this paper is that all the results presented here do not concern a single case study.,In this paper, there are some examples that can show the use of AI in creative activities; however, this does not complete the situation facing companies in any sector because the AI technologies used within enterprises are constantly evolving. It is possible to continue to do research in this field.,The paper is meaningful because highlights the development of AI toward creative activities typically of human resources. It is also interesting because it analyzes the exploratory use of AI in increasingly human work, generating positive and negative externalities.},
note = {Publisher: Emerald Publishing Limited},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This study aims to investigate about the use of artificial intelligence (AI) (man machine relationship) regarding organizational behavior. The aim of this research paper is to analyze whether the current AI is used also to replace man in “creative” activities.,This study is based on a qualitative and explorative approach. It is made a review of the literature with “Scopus” and “Web of Science” databases. The research fields are AI, organizational behavior, man-machine relationship and creativity.,Analyzing whether the intensive use of AI in organizational behavior can replace human work in creative activities.,The connection of AI with creative activities within the organization is only just beginning. For this reason, other sources, like Harvard Business Review, public reports and professional papers found on the internet have been considered. The most important limitation of this paper is that all the results presented here do not concern a single case study.,In this paper, there are some examples that can show the use of AI in creative activities; however, this does not complete the situation facing companies in any sector because the AI technologies used within enterprises are constantly evolving. It is possible to continue to do research in this field.,The paper is meaningful because highlights the development of AI toward creative activities typically of human resources. It is also interesting because it analyzes the exploratory use of AI in increasingly human work, generating positive and negative externalities.
10.
Drott, Eric
Copyright, compensation, and commons in the music AI industry Journal Article
In: Creative Industries Journal, vol. 14, no. 2, pp. 190–207, 2021, ISSN: 1751-0694, (Publisher: Routledge _eprint: https://doi.org/10.1080/17510694.2020.1839702).
@article{drott_copyright_2021,
title = {Copyright, compensation, and commons in the music AI industry},
author = {Eric Drott},
url = {https://doi.org/10.1080/17510694.2020.1839702},
doi = {10.1080/17510694.2020.1839702},
issn = {1751-0694},
year = {2021},
date = {2021-08-01},
urldate = {2024-11-22},
journal = {Creative Industries Journal},
volume = {14},
number = {2},
pages = {190–207},
abstract = {Since 2015 a number of startups has emerged seeking to commercialise music AI. Two types of firm stand out. One markets services directly to consumers, in the form of adaptive music that responds to contextual and/or activity-related cues; another group markets AI-generated music to cultural producers, in the form of algorithmically-generated, royalty-free production music. Initiatives like these have generated debate among legal scholars about notions of copyright and authorship. But until recently discussion has focused on who (or what) should be awarded rights over the products of so-called ‘expressive AI’: Its programmers? Its users? Or the AI itself? Largely overlooked in such debates is the status of another repertoire: not the music put out by an AI, but that which is put into it, the music that constitutes the training set necessary for machine learners to learn. Given the massive datasets mobilised to train machine learners, existing copyright regimes prove inadequate in the face of the questions of distributive justice that such commercial systems raise. Specifically, commercial practices premised on the extraction of value from a special kind of common-pool resource – the shared knowledge of a given music community – demand remedies grounded not in the methodological individualism of copyright law, but commons-based responses instead. As such, the article sketches a couple of alternative models (levy-based trust funds, ownership funds) that could provide a more equitable institutional and economic framework for sustaining the musical commons.},
note = {Publisher: Routledge
_eprint: https://doi.org/10.1080/17510694.2020.1839702},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Since 2015 a number of startups has emerged seeking to commercialise music AI. Two types of firm stand out. One markets services directly to consumers, in the form of adaptive music that responds to contextual and/or activity-related cues; another group markets AI-generated music to cultural producers, in the form of algorithmically-generated, royalty-free production music. Initiatives like these have generated debate among legal scholars about notions of copyright and authorship. But until recently discussion has focused on who (or what) should be awarded rights over the products of so-called ‘expressive AI’: Its programmers? Its users? Or the AI itself? Largely overlooked in such debates is the status of another repertoire: not the music put out by an AI, but that which is put into it, the music that constitutes the training set necessary for machine learners to learn. Given the massive datasets mobilised to train machine learners, existing copyright regimes prove inadequate in the face of the questions of distributive justice that such commercial systems raise. Specifically, commercial practices premised on the extraction of value from a special kind of common-pool resource – the shared knowledge of a given music community – demand remedies grounded not in the methodological individualism of copyright law, but commons-based responses instead. As such, the article sketches a couple of alternative models (levy-based trust funds, ownership funds) that could provide a more equitable institutional and economic framework for sustaining the musical commons.