Monsters, Metaphors, and Machine Learning

Graham Dove, Anne Laure Fayard

    Research output: Chapter in Book/Report/Conference proceedingConference contribution


    Machine learning (ML) poses complex challenges for user experience (UX) designers. Typically unpredictable and opaque, it may produce unforeseen outcomes detrimental to particular groups or individuals, yet simultaneously promise amazing breakthroughs in areas as diverse as medical diagnosis and universal translation. This results in a polarized view of ML, which is often manifested through a technology-as-monster metaphor. In this paper, we acknowledge the power and potential of this metaphor by resurfacing historic complexities in human-monster relations. We (re)introduce these liminal and ambiguous creatures, and discuss their relation to ML. We offer a background to designers' use of metaphor, and show how the technology-as-monster metaphor can generatively probe and (re)frame the questions ML poses. We illustrate the effectiveness of this approach through a detailed discussion of an early-stage generative design workshop inquiring into ML approaches to supporting student mental health and well-being.

    Original languageEnglish (US)
    Title of host publicationCHI 2020 - Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
    PublisherAssociation for Computing Machinery
    ISBN (Electronic)9781450367080
    StatePublished - Apr 21 2020
    Event2020 ACM CHI Conference on Human Factors in Computing Systems, CHI 2020 - Honolulu, United States
    Duration: Apr 25 2020Apr 30 2020

    Publication series

    NameConference on Human Factors in Computing Systems - Proceedings


    Conference2020 ACM CHI Conference on Human Factors in Computing Systems, CHI 2020
    Country/TerritoryUnited States


    • generative metaphor
    • machine learning
    • monster theory
    • ux design

    ASJC Scopus subject areas

    • Computer Graphics and Computer-Aided Design
    • Human-Computer Interaction
    • Software


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