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Design research is important for understanding and interrogating how emergingtechnologies shape human experience. However, design research with MachineLearning (ML) is relatively underdeveloped. Crucially, designers have not founda grasp on ML uncertainty as a design opportunity rather than an obstacle. Thetechnical literature points to data and model uncertainties as two mainproperties of ML. Through post-phenomenology, we position uncertainty as onedefining material attribute of ML processes which mediate human experience. Tounderstand ML uncertainty as a design material, we investigate four designresearch case studies involving ML. We derive three provocative concepts:thingly uncertainty: ML-driven artefacts have uncertain, variable relations totheir environments; pattern leakage: ML uncertainty can lead to patternsshaping the world they are meant to represent; and futures creep: MLtechnologies texture human relations to time with uncertainty. Finally, weoutline design research trajectories and sketch a post-phenomenologicalapproach to human-ML relations.

Machine Learning Uncertainty as a Design Material: A Post-Phenomenological Inquiry