Explainable AI through the Lens of Material Agency: Enabling Musical Interface Design with Neural Audio Models
Authors: Shuoyang Jasper Zheng, Anna Xambó Sedó, Nick Bryan-Kinns
Organizations: Centre for Digital Music, Queen Mary University of London, London, United Kingdom · Creative Computing Institute, University of the Arts London, London, United Kingdom
Recent work in Human-Computer Interaction (HCI) increasingly treats AI models as design materials that have distinctive computational properties to shape design artifacts. Artists learn to work with the model "at play" to explore their emerging properties. The aim of explainability, in this view, is to make visible a crafting and hacking space to enable sustained creative practices with AI. In this chapter, we propose material explainability as a range of activities and artifacts that transform AI models into accessible and inclusive design materials in the workspace of artists, designers, and makers. We present a case study of building a repository of resources to enable artistic explorations of neural audio models in New Interfaces for Musical Expression (NIME) design. Reflecting on our community-building journey and the making of a collection of musical interface designs with a group of artists, we raise three recommendations on enabling the exploration of AI as materials in artistic practices to inspire future XAI design for artists.
Machine generation of symbolic music and digital audio are hot topics but there have been relatively few digital musical instruments that integrate generative AI. Present musical AI tools are not artist centred and do not support experimentation or integrating into musical instruments or practices. This work introduces an inexpensive generative AI instrument platform based on a single board computer that connects via MIDI to other musical devices. The platform uses artist-collected datasets with models trained on a regular computer. This paper asks what the design space of intelligent musical instruments might look like when accessible and portable AI systems are available for artistic exploration. I contribute five examples of instruments created and tested through a two-year first-person artistic research process. These show that (re)mapping can replace retraining for discovering AI interaction, that fast input interleaving is a new co-creative strategy, that small-data AI models can be a transportable design resource, and that cheap hardware can lower barriers to inclusion. This work could enable artists to explore new interaction and performance schemes with intelligent musical instruments.
Explainable AI (XAI) in creative practice can be less about technocentric explanation and more about enabling artists to inspect modify and debug models as part of making Yet largescale texttoimage diffusion systems are typically presented as opaque endtoend tools limiting this kind of material engagement We argue that even large models can function as creative materials when their internal structure is made visible and manipulable To support this we propose a handson approach to explainability centred on experimentation and intervention We instantiate this approach with a model bending and an interactive (inspection) interface integrated into ComfyUIs nodebased workflow including interactive layer selection and intervention controls Through qualitative and quantitative analysis of bending interventions in Stable Diffusion 15 we show how manipulating specific components of a diffusion pipeline produces relatively consistent families of visual effects allowing artists to build practical layerlevel intuition about how different parts of the model shape generated images
Music and dance are social practices of expression and connection, yet most HCI work in human-AI co-creation centers the solo performer. As generative music matures, we ask not only what AI can compose but what social encounters it can organize around sound. We present Encypher, a collaborative generative music system that translates collective movement qualities into text prompts conditioning real-time music generation for dance cyphers. Through five weeks of co-design with local dancers, a user study with unacquainted participants, a public museum event, and a live performance, we found that users developed shared agency, perceiving the music as a response to the room's energy. While newcomers felt uncertain, the system fostered social presence by prompting them to look to each other for cues. By treating sociality as a design concern rather than a downstream effect, we offer a framework and design implications for AI systems for collaborative, embodied expression.