[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126888-en":3,"doc-seo-126888-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126888,2336474466712,"Maeve","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Machine Learning Processes as Sources of Ambiguity: Insights from AI Art","Ongoing efforts to treat machine learning (ML) as a design material face persistent challenges in human-computer interaction. This paper examines how artists incorporate ML within AI art, using nine computer-vision and image-synthesis artworks to explore ambiguity creation. Grounded in related HCI theories, the analysis shows how ambiguity arises not only from established design categories, but also from close engagement with the ML workflow, including dataset curation, model training, and deployment. The study argues for reframing ML process aspects as design elements and supplements artifact-centered views with process-centered ones, addressing assumptions about uncertainty, dependability, and explainability.","arXiv :2403 .09374v1 [ cs .HC] 14 Mar 2024  \nMachine Learning Processes as Sources of Ambiguity: Insights from AI Art  \nCHRISTIAN SIVERTSEN, IT University of Copenhagen, Denmark GUIDO SALIMBENI, University of Nottingham, United Kingdom ANDERS SUNDNES LØVLIE, IT University of Copenhagen, Denmark STEVE BENFORD, University of Nottingham, United Kingdom JICHEN ZHU, IT University of Copenhagen, Denmark  \nOngoing 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.  \nCCS Concepts: • Human-centered computing → HCI theory, concepts and models; • Applied computing → Media arts. Additional Key Words and Phrases: ambiguity, machine learning, artificial intelligence, art, computer vision, generative art  \nACM Reference Format:  \nChristian Sivertsen, Guido Salimbeni, Anders Sundnes Løvlie, Steve Benford, and Jichen Zhu. 2024. Machine Learning Processes as Sources of Ambiguity: Insights from AI Art. In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI’24), May 11–16, 2024, Honolulu, HI, USA. ACM, New York, NY, USA, 24 pages. [https://doi.org/10.1145/3613904.3642855](https://doi.org/10.1145/3613904.3642855)  \n1 INTRODUCTION  \nTo meet the demands raised by new Machine Learning (ML) products, the Human-Computer Interaction (HCI) community is making ongoing efforts to turn ML into a design material [46] . Despite the intense interest, a growing body of research shows that ML is uniquely difficult to design with[13, 33, 53, 93] . In a recent study with industry UX designers, researchers identified two key difficulties of ML as a design material: capability uncertainty (uncertainties surrounding what the system can do and how well it performs) and output complexity (complexity of the outputs that the system might generate, e.g., in adaptive systems)[93] . Due to these ML-specific difficulties, designers face obstacles in all design phases, from conceptualization to prototyping. There is thus a need in the HCI design community for new ways of conceptualizing ML and design criteria for ML experiences.  \nIn this paper, we follow the precedents in interaction design and seek inspiration from art. HCI researchers have found that engaging art and art history can open up new generative ideas for HCI theory and practice (e.g., [10, 11, 37]), especially in domains traditionally dominated by discourses of engineering and productivity (e.g., digital fabrication[31, 83], electronics[51] such as Machine Learning[9, 23, 78]) .  \nThis paper turns to “AI art,” an emerging umbrella term that describes the variety of artistic practices that use AI, including ML, to create aesthetic experiences[17, 99]. Among AI art, we focus on a particularly active area: visual artworks  \n© 2024 Copyright held by the owner/author(s) . Publication rights licensed to ACM. Manuscript submitted to ACM  \nthat are built on computer vision—the technology that allows computers to make sense of images—and image synthesis—the technology that allows computers to create images from some form of user input. Following recent break","cbCaif7146jj78zu","https://ap.wps.com/l/cbCaif7146jj78zu","pdf",1777555,1,24,"English","en",105,"# Introduction\n## Motivation for treating ML as a design material\n## Inspiration from art for HCI theory and practice\n## Focus on AI art and the role of computer vision and image synthesis\n## Ambiguity as an interpretive relationship\n## Purpose and contributions of the paper","[{\"question\":\"Why is it difficult to use machine learning as a design material in HCI?\",\"answer\":\"Research highlights ML-specific difficulties such as capability uncertainty and output complexity, which create obstacles across design phases from conceptualization to prototyping.\"},{\"question\":\"How does the paper study ambiguity in AI art?\",\"answer\":\"It analyzes nine documented AI artworks using computer vision and image synthesis, guided by an HCI theory of ambiguity that emphasizes interpretive, evocative, and mysterious relationships between people and artifacts.\"},{\"question\":\"What does the paper conclude about the role of the ML process in creating ambiguity?\",\"answer\":\"Artists work closely with the full ML pipeline—dataset curation, model training, and application—to evoke ambiguity, suggesting ML should be reframed as design elements rather than only technical details.\"}]","Machine Learning Processes as Sources of Ambiguity: Insights from AI Art | 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is it difficult to use machine learning as a design material in HCI?","Question",{"text":75,"@type":76},"Research highlights ML-specific difficulties such as capability uncertainty and output complexity, which create obstacles across design phases from conceptualization to prototyping.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the paper study ambiguity in AI art?",{"text":80,"@type":76},"It analyzes nine documented AI artworks using computer vision and image synthesis, guided by an HCI theory of ambiguity that emphasizes interpretive, evocative, and mysterious relationships between people and artifacts.",{"name":82,"@type":73,"acceptedAnswer":83},"What does the paper conclude about the role of the ML process in creating ambiguity?",{"text":84,"@type":76},"Artists work closely with the full ML pipeline—dataset curation, model training, and application—to evoke ambiguity, suggesting ML should be reframed as design elements rather than only technical 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