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

Jesse Josua Benjamin, Arne Berger, Nick Merrill, James Pierce

Research output: Working paper

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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.

Accepted to ACM 2021 CHI Conference on Human Factors in Computing Systems (CHI 2021)
Original languageEnglish
PublisherarXiv.org
DOIs
Publication statusPublished - 11 Jan 2021

Keywords

  • Post-phenomenology
  • Machine Learning
  • thingly uncertainty
  • Design Research
  • horizonal relations

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