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

arXiv: https://arxiv.org/abs/2403.09374 · HTML: https://mlsanity.com/paper/2403.09374/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2024.

## Built on by (most cited first, 7 of 7)

- [Un-Straightening Generative AI: How Queer Artists Surface and Challenge the Normativity of Generative AI Models](https://mlsanity.com/paper/2503.09805/) (2025)
- [POET: Supporting Prompting Creativity and Personalization with Automated Expansion of Text-to-Image Generation](https://mlsanity.com/paper/2504.13392/) (2025)
- [Reimagining Personal Data: Unlocking the Potential of AI-Generated Images in Personal Data Meaning-Making](https://mlsanity.com/paper/2502.18853/) (2025)
- [From Sound to Sight: Towards AI-authored Music Videos](https://mlsanity.com/paper/2509.00029/) (2025)
- ["Impressively Scary:" Exploring User Perceptions and Reactions to Unraveling Machine Learning Models in Social Media Applications](https://mlsanity.com/paper/2503.03927/) (2025)
- [Human-AI Coordination Zones: A Framework for Designing Human-in-the-Loop Experiences with Agentic AI](https://mlsanity.com/paper/2606.09848/) (2026)
- [The Aura in the Machine: Genealogy and the Status of the Work of Art in the Generative Era](https://mlsanity.com/paper/2607.17940/) (2026)

## Abstract (arXiv)

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.