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Designing for the Long Tail of Machine Learning
Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. Sectra AB, Linköping, Sweden.ORCID iD: 0000-0002-7014-8874
Sectra AB, Linköping, Sweden.
2020 (English)Manuscript (preprint) (Other academic)
Abstract [en]

Recent technical advances has made machine learning (ML) a promising component to include in end user facing systems. However, user experience (UX) practitioners face challenges in relating ML to existing user-centered design processes and how to navigate the possibilities and constraints of this design space. Drawing on our own experience, we characterize designing within this space as navigating trade-offs between data gathering, model development and designing valuable interactions for a given model performance. We suggest that the theoretical description of how machine learning performance scales with training data can guide designers in these trade-offs as well as having implications for prototyping. We exemplify the learning curve's usage by arguing that a useful pattern is to design an initial system in a bootstrap phase that aims to exploit the training effect of data collected at increasing orders of magnitude.

Place, publisher, year, edition, pages
2020.
Keywords [en]
digital design, interaction design, machine learning
National Category
Design Human Computer Interaction
Identifiers
URN: urn:nbn:se:liu:diva-163530OAI: oai:DiVA.org:liu-163530DiVA, id: diva2:1392325
Note

Accepted for presentation in poster format for the ACM CHI'19 Workshop <Emerging Perspectives in Human-Centered Machine Learning>

Available from: 2020-02-07 Created: 2020-02-07 Last updated: 2020-02-19Bibliographically approved

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https://arxiv.org/abs/2001.07455

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Lindvall, Martin

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Citation style
  • apa
  • ieee
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  • vancouver
  • oxford
  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf