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

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

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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.
TitelProceedings of the 2024 CHI Conference on Human Factors in Computing Systems
Antal sider14
UdgivelsesstedNew York, NY, USA
ForlagAssociation for Computing Machinery
Publikationsdato11 maj 2024
ISBN (Elektronisk)9798400703300
StatusUdgivet - 11 maj 2024
BegivenhedCHI 2024: Surfing the World - Honolulu, USA
Varighed: 11 maj 202416 maj 2024


KonferenceCHI 2024


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