Machine Learning Processes as Sources of Ambiguity: Insights from AI Art

Christian Sivertsen, Guido Salimbeni, Anders Sundnes Løvlie, Steve Benford, Jichen Zhu

Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review


Ongoing efforts to turn Machine Learning (ML) into a design material have encountered limited success. This paper examines the burgeoning area of AI art to understand how artists incorporate ML in their creative work. Drawing upon related HCI theories, we investigate how artists create ambiguity by analyzing nine AI artworks that use computer vision and image synthesis. Our analysis shows that, in addition to the established types of ambiguity, artists worked closely with the ML process (dataset curation, model training, and application) and developed various techniques to evoke the ambiguity of processes. Our finding indicates that the current conceptualization of ML as a design material needs to reframe the ML process as design elements, instead of technical details. Finally, this paper offers reflections on commonly held assumptions in HCI about ML uncertainty, dependability, and explainability, and advocates to supplement the artifact-centered design perspective of ML with a process-centered one.
Original languageEnglish
Title of host publicationProceedings of the 2024 CHI Conference on Human Factors in Computing Systems
Number of pages14
Place of PublicationNew York, NY, USA
PublisherAssociation for Computing Machinery
Publication date11 May 2024
Article number165
ISBN (Electronic)9798400703300
Publication statusPublished - 11 May 2024
EventCHI 2024: Surfing the World - Honolulu, United States
Duration: 11 May 202416 May 2024


ConferenceCHI 2024
Country/TerritoryUnited States
Internet address


  • ambiguity
  • machine learning
  • artificial intelligence
  • art
  • computer vision
  • generative art


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