Ambiguity in design

Jorge, Clinton; Nisi, Valentina; Nunes, Nuno; Innella, Giovanni; Caldeira, Miguel; Sousa, Duarte

doi:10.1145/2468356.2468452

Cited by 5 publications

(4 citation statements)

References 10 publications

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“…Simply put, artifacts using ambiguity support multiple interpretations by users. Previous HCI research has proved ambiguity and multiple interpretations to be a fruitful alternative to the usability principles in traditional UX design [7,18,29,37,50,76,77,80]. Similarly, our investigation of AI art indicates alternative approaches to some of the widely accepted desiderata of ML in HCI, such as dependability and explanability.…”

Section: Introductionmentioning

confidence: 63%

“…Ambiguity is recognized as a valuable resource in HCI and design research [7,18,29,37,50,76,77,80]. The notion that ambiguity can be a valuable resource for design, other than being a problem to be solved, was proposed by Gaver and colleagues in 2003 [37].…”

Section: Ambiguity In Hcimentioning

confidence: 99%

“…Their main idea is that an interactive experience can ask questions and thus prompt users to try and establish answers rather than aiming to give answers directly. Later research in HCI has further explored ambiguity from a variety of perspectives [7,18,50], including the areas of affective computing [76], bio-data [29,77] and experience design for museums [12,49,50,74,87].…”

Section: Ambiguity In Hcimentioning

confidence: 99%

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

Sivertsen,

Salimbeni,

Løvlie

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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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.CCS Concepts: • Human-centered computing → HCI theory, concepts and models; • Applied computing → Media arts.

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Section: Introductionmentioning

confidence: 63%

Section: Ambiguity In Hcimentioning

confidence: 99%

Section: Ambiguity In Hcimentioning

confidence: 99%

See 1 more Smart Citation

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

Sivertsen,

Salimbeni,

Løvlie

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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“…Thus, automatic storytelling consists of two components: a semantic representation of story structure, and the ability to automatically visualise or generate a story from this semantic representation using some form of Natural Language Generation (NLG) (Rishes et al 2013). In NLG, notable related work is described, among others, by (Jorge et al 2013;Dionisio et al 2016;Mazeika 2016;Farrell and Ware 2016). While an interesting discipline that is essential to applying any system aimed at automatically generating stories, especially regarding surface realisation, we primarily focus on the generation of the semantic structure of the story.…”

Section: Related Workmentioning

confidence: 99%

Language Technologies for the Challenges of the Digital Age

Rehm¹,

Declerck²

2018

Lecture Notes in Computer Science

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