AI-tribution: Enabling Training Data Attribution for Users of Generative Music Models

Abstract

Generative music models are growing in popularity, but users remain in the dark about the influences on their supposedly novel creations. Existing systems provide no mechanisms to identify highly similar songs or to help users learn how model outputs are influenced by training data. We present AI-tribution: a tool designed to inform users of generative music models about the training songs most similar to their generated outputs. We detail the interface, methodology, and a user study (n=26) where musicians, media makers, and music consumers interact with the tool in 30-minute sessions. We analyze how the tool changes user knowledge of generated outputs, examine differences across user groups, and qualitatively analyze users' attitudes towards the tool. We find an overwhelmingly positive reception, show how the tool encourages deeper engagement with influences, and shed light on how different groups would use it in practice.