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

Sivertsen, Christian; Salimbeni, Guido; Løvlie, Anders Sundnes; Benford, Steven David; Zhu, Jichen

Abstract

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.

Citation

Sivertsen, C., Salimbeni, G., Løvlie, A. S., Benford, S. D., & Zhu, J. (2024, May). Machine Learning Processes As Sources of Ambiguity: Insights from AI Art. Presented at CHI '24: CHI Conference on Human Factors in Computing Systems, Honolulu HI USA

• Presentation Conference Type: Edited Proceedings
• Conference Name: CHI '24: CHI Conference on Human Factors in Computing Systems
• Start Date: May 11, 2024
• End Date: May 16, 2024
• Publication Date: May 11, 2024
• Deposit Date: Mar 5, 2025
• Publisher: Association for Computing Machinery (ACM)
• Peer Reviewed: Peer Reviewed
• Article Number: 165
• Book Title: CHI '24: Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems
• ISBN: 9798400703300
• DOI: https://doi.org/10.1145/3613904.3642855
• Public URL: https://nottingham-repository.worktribe.com/output/34875134
• Publisher URL: https://dl.acm.org/doi/10.1145/3613904.3642855