My PhD thesis introduced model prototyping as a practice-based process that helps envision machine learning in situation with stakeholders before engineering it.
Model prototypes may be considered as design artifacts for machine learning techniques in interactive systems. They enable to test interactive data workflows with concrete algorithmic implementations before starting the engineering of a final learning model. While standard approaches to machine learning often consider interaction only after a model is trained, model prototypes enable to generate new design ideas for machine learning in situation with a diversity of stakeholders.
During my thesis, model prototyping enabled to study four machine learning techniques in relation to four music practices: unsupervised learning, reinforcement learning, deep reinforcement learning, and active learning. Current research focuses on applying model prototyping to collaborative art and design projects.
PhD thesis (2020)