Julie Zhu

Music is touted as “universal” under a rhetoric of neutrality, but it is fundamentally a social behavior—yes, there is the impartial sound wave, but there is also how we think about it, what we do with it, and what socially recognized meanings we may assign to it. Today, non-human processes such as AI and automation curate and analyze our music, but their reliance in training data upon historical biases perpetuates oppressive relations connecting music and meaning. In my research, I hope to acknowledge the breadth of diversity in music by assigning equal importance to a best-practice list of categories beyond the well-trodden formal analyses, implicating a social and ethical dimension to music that is direly needed in scholarship and practice, as well as develop methodologies for inclusive AI in music.