TY - JOUR
KW - Artificial intelligence
KW - COVERT RESEARCH
KW - heritage
KW - Irish traditional music
KW - WORK
KW - ethics
KW - ethnography
KW - Innovation
KW - research methodology
AU - Elin Kanhov
AU - Anna-Kaisa Kaila
AU - Bob Sturm
AB - 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.
DO - 10.1080/09298215.2024.2442359
N1 - Num Pages: 17 Place: Abingdon Publisher: Routledge Journals, Taylor \& Francis Ltd Web of Science ID: WOS:001408665500001
N2 - 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.
TI - Innovation, data colonialism and ethics: critical reflections on the impacts of AI on Irish traditional music
UR - https://www.tandfonline.com/doi/full/10.1080/09298215.2024.2442359
SN - 0929-8215, 1744-5027
ER -