Monsters, Metaphors, and Machine Learning

要旨

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.

キーワード
Machine Learning
UX Design
Generative Metaphor
Monster Theory
著者
Graham Dove
New York University, New York, NY, USA
Anne-Laure Fayard
New York University, Brooklyn, NY, USA
DOI

10.1145/3313831.3376275

論文URL

https://doi.org/10.1145/3313831.3376275

会議: CHI 2020

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2020.acm.org/)

セッション: AI/ML & seeing through the black box

Paper session
313C O'AHU
5 件の発表
2020-04-29 20:00:00
2020-04-29 21:15:00
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