Human–AI co-creativity is often understood through a tool-use or collaboration frame, obscuring the relational dynamics of artistic practice with machine learning (ML) systems. To advance the field, we recast such artistic practice through Material Engagement Theory (MET), emphasizing how creative and aesthetic processes emerge in sustained relations between artist and system. We examined 18 contemporary artists’ engagements with ML from 54 documents using a qualitative, interpretive, and framework-guided approach. Our findings show how artists and ML systems co-constitute creative and aesthetic properties by mutually discovering and attuning to each other’s affordances, where creativity unfolds along a control-chance continuum driven by creative and aesthetic thinging, enabling a mutual aesthetic becoming through sustained interaction. Key factors include developing artistic intuition for ML, internalisation, and the evolving role of the meta-artist. Herewith, we contribute MET as a framework for enriching our understanding of human–AI co-creativity as emergent and relational.
ACM CHI Conference on Human Factors in Computing Systems