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.

翻译：将机器学习作为设计材料的持续努力遇到了一定限制。本文考察新兴的AI艺术领域，以理解艺术家如何在创作中融入机器学习。基于相关人机交互理论，我们通过分析九件使用计算机视觉与图像合成的AI艺术作品，探究艺术家如何营造歧义。我们的分析表明，除现有歧义类型外，艺术家深度参与机器学习流程（数据集构建、模型训练与应用），并发展出多种技术来唤起流程性歧义。研究结果表明，当前将机器学习作为设计材料的认知需要重构——将机器学习流程视为设计要素而非技术细节。最后，本文反思了人机交互领域关于机器学习不确定性、可靠性与可解释性的常见假设，主张以流程为中心的设计观补充现有的以制品为中心的机器学习设计视角。