  This paper presents a mapping strategy for interacting with the latent spaces
of generative AI models. Our approach involves using unsupervised feature
learning to encode a human control space and mapping it to an audio synthesis
model's latent space. To demonstrate how this mapping strategy can turn
high-dimensional sensor data into control mechanisms of a deep generative
model, we present a proof-of-concept system that uses visual sketches to
control an audio synthesis model. We draw on emerging discourses in XAIxArts to
discuss how this approach can contribute to XAI in artistic and creative
contexts, we also discuss its current limitations and propose future research
directions.
