Responsible AI international community to reduce bias in AI music generation and analysis

This 12 month project will build an international community to address Responsible AI (RAI) challenges of bias in AI music generation and analysis.

Current over-reliance on huge training datasets for deep learning leads to AI models biased towards Western classical and pop music and marginalises other music genres. We will bring together an international and interdisciplinary team of researchers, musicians, and industry experts to make available AI tools, expertise, and datasets which improve access to marginalised music genres. This will directly benefit musicians and audiences engaging with a wider range of musical genres and benefits creative industries by offering new forms of music consumption.


Lead: Prof. Nick Bryan-Kinns (University of the Arts London, UK; UAL)
Prof. Rebecca Fiebrink (UAL)
Dr. Phoenix Perry (UAL)
Prof. Zijin Li (Central Conservatory of Music, China; CCoM)
Dr. Nuno Correia (Tallinn University, Estonia; TU)
Dr. Alex Lerch (Georgia Tech, USA; GT)
Prof. Sid Fels (University of British Columbia, Canada; UBC)
Dr. Gabriel Vigliensoni (Concordia University, Canada; CU)
Dr. Andrei Coronel and Dr. Raphael Alampay (Ateneo de Manila University, Philippines; AdMU)
Prof. Rikard Lindell (Dalarna University, Sweden; DU)


Music Hackspace (UK)
Steinberg (Germany)
Bela (UK)



To get involved please contact Prof. Nick Bryan-Kinns


Funded by Responsible Artificial Intelligence (RAI) UK International Partnerships (UKRI EPSRC grant reference EP/Y009800/1)

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