The UX of Interactive Machine Learning

Maliheh Ghajargar, Jan Persson, Jeffrey Bardzell, Lars Holmberg, Agnes Tegen

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish (US)
Title of host publicationNordiCHI 2020 - Proceedings of the 11th Nordic Conference on Human-Computer Interaction
Subtitle of host publicationShaping Experiences, Shaping Society
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450375795
StatePublished - Oct 25 2020
Event11th Nordic Conference on Human-Computer Interaction: Shaping Experiences, Shaping Society, NordiCHI 2020 - Virtual, Online, Estonia
Duration: Oct 25 2020Oct 29 2020

Publication series

NameACM International Conference Proceeding Series


Conference11th Nordic Conference on Human-Computer Interaction: Shaping Experiences, Shaping Society, NordiCHI 2020
CityVirtual, Online

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

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