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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: 2021-06-07Bibliographically approved
In thesis
1. Designing with Machine Learning in Digital Pathology: Augmenting Medical Specialists through Interaction Design
Open this publication in new window or tab >>Designing with Machine Learning in Digital Pathology: Augmenting Medical Specialists through Interaction Design
2021 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Recent advancements in machine learning (ML) have led to a dramatic increase in AI capabilities for medical diagnostic tasks. Despite technical advances, developers of predictive AI models struggle to integrate their work into routine clinical workflows. Inefficient human-AI interactions, poor sociotechnical fit and a lack of interactive strategies for dealing with the imperfect nature of predictions are known factors contributing to this lack of adoption.

User-centred design methods are typically aimed at discovering and realising desirable qualities in use, pragmatically oriented around finding solutions despite the limitations of material- and human resources. However, existing methods often rely on designers possessing knowledge of suitable interactive metaphors and idioms, as well as skills in evaluating ideas through low-fidelity prototyping and rapid iteration methods—all of which are challenged by the data-driven nature of machine learning and the unpredictable outputs from AI models.

Using a constructive design research approach, my work explores how we might design systems with AI components that aid clinical decision-making in a human-centred and iterative fashion. Findings are derived from experiments and experiences from four exploratory projects conducted in collaboration with professional physicians, all aiming to probe this design space by producing novel interactive systems for or with ML components.

Contributions include identifying practical and theoretical design challenges, suggesting novel interaction strategies for human-AI collaboration, framing ML competence for designers and presenting empirical descriptions of conducted design processes. Specifically, this compilation thesis contains three works that address effective human-machine teaching and two works that address the challenge of designing interactions that afford successful decision-making despite the uncertainty and imperfections inherent in machine predictions.

Finally, two works directly address design-researchers working with ML, arguing for a systematic approach to increase the repertoire available for theoretical annotation and understanding of the properties of ML as a designerly material.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2021
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 2157
National Category
Design Human Computer Interaction
Identifiers
urn:nbn:se:liu:diva-176117 (URN)10.3384/diss.diva-176117 (DOI)978-91-7929-604-9 (ISBN)
Public defence
2021-09-23, K3, Kåkenhus, Campus Norrköping, Norrköping, 09:00 (English)
Opponent
Supervisors
Available from: 2021-08-30 Created: 2021-06-07 Last updated: 2022-12-08Bibliographically approved

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Other links

https://arxiv.org/abs/2001.07455

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Lindvall, Martin

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Citation style
  • apa
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Output format
  • html
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  • asciidoc
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