Malmö University Publications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
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
The UX of Interactive Machine Learning
Malmö University, Internet of Things and People (IOTAP). Malmö University, Faculty of Culture and Society (KS), School of Arts and Communication (K3).ORCID iD: 0000-0003-1852-3937
Malmö University, Internet of Things and People (IOTAP). Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).ORCID iD: 0000-0002-9471-8405
Pennsylvania State University.
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).ORCID iD: 0000-0001-5676-1931
Show others and affiliations
2020 (English)In: NordiCHI 2020, 11th Nordic Conference on Human-Computer Interaction: Shaping Experiences, Shaping Society, New York, USA: Association for Computing Machinery (ACM), 2020, article id Article No.: 138Conference paper, Published paper (Refereed)
Abstract [en]

Machine Learning (ML) has been a prominent area of research within Artificial Intelligence (AI). ML uses mathematical models to recognize patterns in large and complex data sets to aid decision making in different application areas, such as image and speech recognition, consumer recommendations, fraud detection and more. ML systems typically go through a training period in which the system encounters and learns about the data; further, this training often requires some degree of human intervention. Interactive machine learning (IML) refers to ML applications that depend on continuous user interaction. From an HCI perspective, how humans interact with and experience ML models in training is the main focus of this workshop proposal. In this workshop we focus on the user experience (UX) of Interactive Machine Learning, a topic with implications not only for usability but also for the long-term success of the IML systems themselves.

Place, publisher, year, edition, pages
New York, USA: Association for Computing Machinery (ACM), 2020. article id Article No.: 138
Keywords [en]
UX, User Experience, Machine Learning, Artificial Intelligence, Interaction Design
National Category
Computer Systems Design
Research subject
Interaktionsdesign
Identifiers
URN: urn:nbn:se:mau:diva-24079DOI: 10.1145/3419249.3421236Scopus ID: 2-s2.0-85123040796OAI: oai:DiVA.org:mau-24079DiVA, id: diva2:1485021
Conference
NordiCHI 2020, 11th Nordic Conference on Human-Computer Interaction
Funder
Knowledge FoundationAvailable from: 2020-10-31 Created: 2020-10-31 Last updated: 2024-02-05Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopushttps://dl.acm.org/doi/abs/10.1145/3419249.3421236

Authority records

Ghajargar, MalihehPersson, Jan A.Holmberg, LarsTegen, Agnes

Search in DiVA

By author/editor
Ghajargar, MalihehPersson, Jan A.Holmberg, LarsTegen, Agnes
By organisation
Internet of Things and People (IOTAP)School of Arts and Communication (K3)Department of Computer Science and Media Technology (DVMT)
Computer SystemsDesign

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 287 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
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