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

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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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.
Original languageEnglish
Title of host publicationCHI'21 Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery
Number of pages14
ISBN (Print)978-1-4503-8096-6
DOIs
Publication statusPublished - 7 May 2021
EventConference on Human Factors in Computing Systems, CHI 2021 - Virtual Conference
Duration: 8 May 202113 May 2021

Conference

ConferenceConference on Human Factors in Computing Systems, CHI 2021
Abbreviated titleCHI 2021
CityVirtual Conference
Period8/05/2113/05/21

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