  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
defining 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:
thingly uncertainty: ML-driven artefacts have uncertain, variable relations to
their environments; pattern leakage: ML uncertainty can lead to patterns
shaping the world they are meant to represent; and futures creep: ML
technologies texture human relations to time with uncertainty. Finally, we
outline design research trajectories and sketch a post-phenomenological
approach to human-ML relations.
