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

TL;DR

This paper explores how uncertainty in machine learning can be used as a creative design material, shifting the perspective from obstacle to opportunity through a post-phenomenological lens.

Contribution

It introduces a novel post-phenomenological framework to understand ML uncertainty as a fundamental design material, supported by four case studies and three provocative concepts.

Findings

ML uncertainty influences human experience through three key concepts.

Design opportunities arise from embracing ML uncertainty as a material.

Proposes a post-phenomenological approach for future design research.

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 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.