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Monsters, Metaphors, and Machine Learning

Published:23 April 2020Publication History

ABSTRACT

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.

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  1. Monsters, Metaphors, and Machine Learning

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      CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
      April 2020
      10688 pages
      ISBN:9781450367080
      DOI:10.1145/3313831

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