Bei Hu
Assistant Professor
Washington University in St. Louis
As artificial intelligence increasingly participates in creative processes, the distinction between human authorship and machine generation grows more complex. Traditionally, creative computation—rooted in open-source culture, conditional design methodologies, and human-authored algorithms—has emphasized transparency, rule-based emergence, and collaborative process. In contrast, contemporary AI models often function as opaque systems, producing outputs that blur the boundary between human intention and machine autonomy.
This paper examines how creative computation can serve as a critical lens for navigating this shifting boundary. It argues for a framework that sustains human agency even within projects that incorporate AI technologies. Drawing on case studies from a Conditional Design course—where students created emergent works through simple rules and collective negotiation—alongside examples from the author’s own open-source creative coding practice, the research explores how negotiated systems encourage relational authorship and systemic sensitivity.
The paper proposes an approach that embraces AI’s generative capacities while reaffirming the importance of process, openness, and participation. Through this lens, creativity is reimagined not as a product of isolated genius, nor as a fully automated output, but as an emergent, co-constructed process shaped through conditions, collaboration, and responsiveness.
By reframing creative computation as a site of critical engagement, the paper advocates for practices that foreground relationality, transparency, and negotiated complexity—offering pathways for sustaining deeply human forms of creativity within an increasingly algorithmic cultural landscape.
This design research is presented at Design Incubation Colloquium 11.3: Virtual Summer on Friday, June 20, 2025.