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Designing for the Long Tail of Machine Learning
Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV. Linköpings universitet, Institutionen för teknik och naturvetenskap, Medie- och Informationsteknik. Linköpings universitet, Tekniska fakulteten. Sectra AB, Linköping, Sweden.ORCID-id: 0000-0002-7014-8874
Sectra AB, Linköping, Sweden.
2020 (engelsk)Manuskript (preprint) (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
2020.
Emneord [en]
digital design, interaction design, machine learning
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-163530OAI: oai:DiVA.org:liu-163530DiVA, id: diva2:1392325
Merknad

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

Tilgjengelig fra: 2020-02-07 Laget: 2020-02-07 Sist oppdatert: 2020-02-19bibliografisk kontrollert

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https://arxiv.org/abs/2001.07455

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Lindvall, Martin

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