The advancement of AI-tools for musical performance has inspired exciting opportunities for interaction with musical-AI-agents. Interactions between humans and AI-agents in musical settings entail dynamic exchanges of control and power, and framings of AI-agents’ roles by human performers. We probe these framings and power-control exchanges through qualitative thematic lenses, drawing from post-phenomenology, matters of fact and concern and feminist science and technology studies. We contribute with a novel interdisciplinary analytical method as a tool for developers and designers of AI systems to help visibilise and examine the implicit, the wider connections and entangled filaments in Human–AI musical interactions.

By definition, traditional music is in a constant state of friction with innovation, exemplified by resistance to ‘outside’ influences such as different instruments, different ways of learning, and forces of commercialisation. An emerging external influence is artificial intelligence (AI), which is now capable of synthesising music collections at scales dwarfing those crafted by people and communities. In this paper, we examine the impact of research and development of AI on Irish traditional music through case studies of two generative AI systems: folk-rnn and Suno. How can researchers and engineers (academic or industrial) who develop and apply AI to specific practices of music make meaningful and non-harmful contributions to those practices? To answer this question, we critically reflect on the tensions that arise between tradition and innovation, how Irish traditional music becomes subject to data colonialism, and the interdisciplinary challenges of ethically engaging as researchers with a traditional music community. We ask what perspectives are needed to balance the interests of academic research and value systems in traditional music communities, and provide three ways forward for computer science to deepen the considerations of their impacts on communities of practice.

In this paper, we use critical and agonistic modes of inquiry to analyse and critique a specific application of AI to music practice. It records a structured interdisciplinary dialogue between 1) a musicologist and social scientist and 2) an engineer in music and computer science, focusing on folk-rnn and Irish Traditional Music (ITM) as a case study. We debate the role of data ethics in AI music applications, the dynamics of inclusion and exclusion, and the nature of embedded value systems and power asymmetries inherent in applying AI to music. We discuss how identifying the value of AI music applications is critical for ensuring research efforts make musical contributions along with academic and technical ones. Overall, this agonistic dialogue exemplifies how questions of right and wrong — the core of ethics — can be examined as AI is applied more and more to music practice.

As music generated using artificial intelligence (AI music) becomes more prevalent — originating not only from individuals but also commercial services — the need to study it and its impacts becomes important. How can this material and its sources be meaningfully studied and critically engaged with, especially considering the unprecedented scales possible with generative AI? The paper begins to answer this question by considering AI music along seven aspects: 1) the company providing an AI music service; 2) its founders and employees; 3) the use of the service; 4) the users; 5) the algorithms; 6) the music; and 7) the sustainability. We make our discussion more concrete by considering the contemporary AI music service Boomy. While our investigations are preliminary and focused on a single AI music service, we argue that they open several interesting avenues of exploration for many disciplines and their intersections to help prepare for the coming flood of AI music. This paper asks many more questions than it answers, which is a feature (not a bug) of it advocating for a new domain of study: AI Music Studies.

Feverish narratives of artistic AI-revolution obscure the fact that empirical documentation of the actual impacts of artificial intelligence on artistic practices is still sparse. This paper focuses on the frictions of working with AI artistically. Based on interviews with 20 AI-artists, we 1) demonstrate that frictions experienced with the technological elements of the work processes with AI are inseparably intertwined with the artists’ socio-material realities and the inherent asymmetries of access, and 2) show how frictional ambivalence and unpredictability in artistic interactions with AI tools function both as restrictive and productive elements of the art-making processes, presenting opportunities to reframe the core notions of artistic agency, authorship, and the ontology of art.We discuss these findings in the context of HCI and critical data studies and provide three invitations for designing with and for frictions. Our empirical work contributes to a deeper understanding of the emerging community of AI-artists and invites new mindful perspectives for the design and development of Creative AI applications.

The environmental footprint of Generative AI and other Deep Learning (DL) technologies is increasing. To understand the scale of the problem and to identify solutions for avoiding excessive energy use in DL research at communities such as ISMIR, more knowledge is needed of the current energy cost of the undertaken research. In this paper, we provide a scoping inquiry of how the ISMIR research concerning automatic music generation (AMG) and computing-heavy music analysis currently discloses information related to environmental impact. We present a study based on two corpora that document 1) ISMIR papers published in the years 2017–2023 that introduce an AMG model, and 2) ISMIR papers from the years 2022–2023 that propose music analysis models and include heavy computations with GPUs. Our study demonstrates a lack of transparency in model training documentation. It provides the first estimates of energy consumption related to model training at ISMIR, as a baseline for making more systematic estimates about the energy footprint of the ISMIR conference in relation to other machine learning events. Furthermore, we map the geographical distribution of generative model contributions and discuss the corporate role in the funding and model choices in this body of work.

Artistic uses of AI technologies are fast gaining prominence in a number of creative domains. In this paper, we describe our preliminary research exploring the challenges and changes that working with AI poses to artists based on interviews with N=20 artists. We present preliminary themes relating to challenges and changes that artists are encountering and highlight the importance of studying AI further in situated artistic practices.