Machine learning as a design material: a curated collection of exemplars for visual interaction

DS 91: Proceedings of NordDesign 2018, Linköping, Sweden, 14th - 17th August 2018

Year: 2018
Editor: Ekströmer, Philip; Schütte, Simon and Ölvander, Johan
Author: Luciani, Danwei Tran; Lindvall, Martin; Löwgren, Jonas
Series: NordDESIGN
Institution: Linköping University
ISBN: 978-91-7685-185-2


Although machine learning is not a new phenomenon, it has truly entered the spotlight in recent years. With growing expectations, we see a shift in focus from performance tuning to awareness of meaningful interaction and purpose. Interaction design and UX research is currently in a position to provide important and necessary knowledge contributions to the development of machine learning systems. Machine learning can be viewed as a design material that is arguably more unpredictable, emergent, and “alive” than traditional ones. These characteristics suggest practice-based work along the lines of research-through-design as a promising approach for machine learning system development research. Design researchers using a research-through-design approach agree that a created artefact carries knowledge, but there is no consensus on how such knowledge is best articulated and transferred within academic discourse. Knowledge contributions need to be abstracted from the particular to a higher level. We suggest curated collections, a variation of annotated portfolios, as a way to abstract and communicate intermediate-level knowledge that is suitable and useful for the research-through-design community. A curated collection presents thoughtfully selected and inter-related exemplars, articulating their salient traits. The insights collected in a curated collection can be used to inform future design in related design situations. This paper provides a curated collection addressing the fine-grained details of interaction with machine learning systems. The examples are drawn from highly visual interaction, predominantly in the domain of digital pathology. The collection of interaction examples is used to elicit a set of salient traits, including the preservation of visual context, rapid real-time refinement, leaving traces, and applying judicious automation. Finally, we show how this curated collection could inform the design of a future system in a different domain. The insights are applied to a case of interaction design to support air traffic controllers in their collaboration with future agentive systems

Keywords: digital design, interaction design, machine learning, curated collection


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