Adoption of AI technologies in the arts has faced a divided reception and raised controversies—while some artists critique them, others embrace the opportunities they provide. In this paper, we contribute empirical insights into what motivates artists’ adoption of AI art technologies, as well as what kind of worldviews these motivations for AI adoption in artistic contexts embody. We shed light on these questions by presenting results from a qualitative interview study (N = 19) of AI artists practicing in Nordic countries. Five key themes emerged that describe artists’ motivations for using AI in their work, which we analyse in reflection with Kerschner and Ehlers' Attitudes Towards Technology (ATT) framework in terms of underlying attitudes of AI-Enthusiasm, Pragmatism, Romanticism, and Scepticism. Our analysis diversifies the understanding of why artists use AI, surfacing cultural and political tensions inherent in adoption of AI technologies for artistic and creative use.

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.

Engineering communities that feed the current proliferation of artificial intelligence (AI) have historically been slow to recognise the spectrum of societal impacts of their work. Frequent controversies around AI applications in creative domains demonstrate insufficient consideration of ethical predicaments, but the abstract principles of current AI and data ethics documents provide little practical guidance. Pragmatic methods are urgently needed to support developers in ethical reflection of their work on creative-AI tools. In the wider context of value sensitive, people-oriented design, we present an analytical method that implements an ethically informed and power-sensitive stakeholder identification and mapping: Ethically Aligned Stakeholder Elicitation (EASE). As a case study, we test our method in workshops with six research groups that develop AI in musical contexts. Our results demonstrate that EASE supports critical self-reflection of the research and outreach practices among developers, discloses power relations and value tensions in the development processes, and foregrounds opportunities for stakeholder engagement. This can guide developers and the wider NIME community towards ethically aligned research and development of creative-AI.

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.

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.

The fast proliferation of AI-driven singing voice synthesis (SVS) facilitates wide-scale misappropriation of third-party vocal expressions, while sidelining the interests of practicing vocal artists. Since EU law provides limited protection for the human singing voice, the emerging SVS industry practices and norms are mainly self-regulated through the Terms of Service (ToS). In this study, we survey the ToS of ten commercial SVS services to examine how they respond to the intensifying calls for Responsible AI and articulate their understanding of a fair market. This is the first paper that examines the contractual dispositions of the service providers of generative AI models and their ethical implications in generative music applications for singing. Using the lenses of procedural and substantive fairness, we chart how service providers allocate rights and obligations among the stakeholders and outline the forms of agency the voice artist can be assigned despite the legislative void. Our work contributes to the socio-technical study of fairness in the context of the developing SVS market. It also informs generative AI policies and further advocacy efforts to cultivate responsible innovation and defend the integrity of vocal artists against synthetic exploitations.