Expanding Modes of Reflection in Design Futuring

Kozubæv, Sandjar; Elsden, Chris; Howell, Noura; Søndergaard, Marie Louise Juul; Merrill, Nick; Schulte, Britta; Wong, Richmond Y.

doi:10.1145/3313831.3376526

Cited by 75 publications

(34 citation statements)

References 86 publications

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“…directly affecting the shape of present and future, can be used to actively interrogate how and whether distinct ML algorithms and respective forms of model uncertainty generate specific relations to 'temporal shapes.' Specifically, futures creep may be used in "attending to temporal representations" [38] in design fiction. For example, researchers may interrogate whether a higher degree in variance concerning ML-driven predictions may bring about novel political or civic norms in speculative scenarios, e.g.…”

Section: Futures Creepmentioning

confidence: 99%

Machine Learning Uncertainty as a Design Material: A Post-Phenomenological Inquiry

Benjamin

Berger

Merrill

et al. 2021

Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

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Design research is important for understanding and interrogating how emerging technologies shape human experience. However, design research with Machine Learning (ML) is relatively underdeveloped. Crucially, designers have not found a grasp on ML uncertainty as a design opportunity rather than an obstacle. The technical literature points to data and model uncertainties as two main properties of ML. Through post-phenomenology, we position uncertainty as one defining material attribute of ML processes which mediate human experience. To understand ML uncertainty as a design material, we investigate four design research case studies involving ML. We derive three provocative concepts: thingly uncertainty: ML-driven artefacts have uncertain, variable relations to their environments; pattern leakage: ML uncertainty can lead to patterns shaping the world they are meant to represent; and futures creep: ML technologies texture human relations to time with uncertainty. Finally, we outline design research trajectories and sketch a post-phenomenological approach to human-ML relations. CCS CONCEPTS• Human-centered computing → HCI theory, concepts and models.

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Section: Futures Creepmentioning

confidence: 99%

Machine Learning Uncertainty as a Design Material: A Post-Phenomenological Inquiry

Benjamin

Berger

Merrill

et al. 2021

Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

Self Cite

View full text Add to dashboard Cite

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“…If we want to get past that, we need to define a course of action over a longer time span and innovate not only on a technological but also on a social and organizational level. This can be done through methodologies, such as data timelines (Villalba et al, 2018), design futuring (Kozubaev et al, 2020;Smith and Ashby, 2020), or speculative design (Dunne and Raby, 2013) to "determine what citizens and patients desire from their doctors, nurses, services and technologies" (Strachan, 2016, p. e17).…”

Section: Recommendationsmentioning

confidence: 99%

Breaking up data-enabled design: expanding and scaling up for the clinical context

Noortman

Lövei

Funk

et al. 2022

AIEDAM

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Data-enabled design (DED) is a promising new methodology for designing with users from within their own context in an iterative and hands-on fashion. However, the agile and flexible qualities of the methodology do not directly translate to every context. In this article, we reflect on the design process of an intelligent ecosystem, called ORBIT, and a proposed evaluative study planned with it. This was part of a DED project in collaboration with a medical hospital to study the post-operative behavior in the (remote) context of bariatric patients. The design and preparation of this project and the process towards an eventual study rejection from the medical ethical committee (METC) provide rich insights into (1) what it means to conduct DED research in a clinical context, and (2) where the boundaries of the method might lie in this specific application area. We highlight insights from carefully designing the substantial infrastructure for the study, and how different aspects of DED translated less easily to the clinical context. We analyze the proposed study setup through the lenses of several modifications we made to DED and further reflect on how to expand and scale up the methodology and adapt the process for the clinical context.

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“…Design fiction is an already established approach in Human-Computer Interaction (HCI). It is to explore possible futures [35,42]. In general, design fiction does not search for a "solution" to a problem.…”

Section: Some Notes On Design Fictionmentioning

confidence: 99%

Kiro

Ringfort-Felner

Laschke

Sadeghian

et al. 2022

Proc. ACM Hum.-Comput. Interact.

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Soon, voice assistants might be able to engage in fully-fledged social conversations with people, rather than merely providing a voice-operated interface to functionality and data. So far, not much is known about designing such "social" voice assistants and the potential social experiences, which could and should emerge in everyday situations. In the present paper, we created a design fiction to explore a sophisticated social voice assistant in the context of the car. Based on models from psychology and psychotherapy, we designed the fictional "virtual passenger" Kiro. We created a website for Kiro (http://www.heykiro.com/), distributed it, and collected responses in various ways (e.g., comments). We further ran a market research-type focus group. In general, we found people to accept Kiro as a conversation partner but not as a replacement for human-human conversations. We suggest designing social voice assistants in a way to enable novel types of socially fulfilling, yet distinct human-machine conversations.

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Expanding Modes of Reflection in Design Futuring

Cited by 75 publications

References 86 publications

Machine Learning Uncertainty as a Design Material: A Post-Phenomenological Inquiry

Machine Learning Uncertainty as a Design Material: A Post-Phenomenological Inquiry

Breaking up data-enabled design: expanding and scaling up for the clinical context

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