Machine learning uncertainty as a design material: A post-phenomenological inquiry

Abstract

Design research is important for understanding and interrogating how emerging technologies shape human experience. However, design research with Machine Learning (ML) is relatively underdeveloped. Crucially, designers have not found a grasp on ML uncertainty as a design opportunity rather than an obstacle. The technical literature points to data and model uncertainties as two main properties of ML. Through post-phenomenology, we position uncertainty as one defning material attribute of ML processes which mediate human experience. To understand ML uncertainty as a design material, we investigate four design research case studies involving ML. We derive three provocative concepts: thinglyuncertainty: ML-driven artefacts have uncertain, variable relationsto their environments; pattern leakage: ML uncertainty can leadto patterns shaping the world they are meant to represent; and futures creep: ML technologies texture human relations to time withuncertainty. Finally, we outline design research trajectories and sketch a post-phenomenological approach to human-ML relations.