TY - GEN
T1 - Monsters, Metaphors, and Machine Learning
AU - Dove, Graham
AU - Fayard, Anne Laure
N1 - Funding Information:
We would like to thank all participants in our workshops, and acknowledge the contribution that reviewers of a previous version of this paper had on focusing our thinking. We would also like to thank the staff and students from the Design Lab at NYU MakerSpace for helping to make our workshops possible. This research was funded in part by National Science Foundation award 1544753.
Publisher Copyright:
© 2020 ACM.
PY - 2020/4/21
Y1 - 2020/4/21
N2 - 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.
AB - 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.
KW - generative metaphor
KW - machine learning
KW - monster theory
KW - ux design
UR - http://www.scopus.com/inward/record.url?scp=85090767219&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85090767219&partnerID=8YFLogxK
U2 - 10.1145/3313831.3376275
DO - 10.1145/3313831.3376275
M3 - Conference contribution
AN - SCOPUS:85090767219
T3 - Conference on Human Factors in Computing Systems - Proceedings
BT - CHI 2020 - Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
PB - Association for Computing Machinery
T2 - 2020 ACM CHI Conference on Human Factors in Computing Systems, CHI 2020
Y2 - 25 April 2020 through 30 April 2020
ER -