In response to the escalating impact of mindless consumption in the fashion and IT industry, we began to think of and with a constraint-based approach to interaction design. This paper describes a research through design investigation into a paradigm of constraint-based design, founded on the practical and perceived constraints of solar-powered internet. Our intention is not to examine individual consumer as a site for sustainable transition, but the industries and industry practitioners at the interface with consumers. We employed strategies that included optimisation as a form of minimisation, visibility as a means to mark existing absence, offloading from automation, and the design of dead-ends. We discuss the challenges in learning to design against the cornucopian paradigm. While the overall vision of an internet powered by the sun seems at once desirable and achievable, the pursuit of a constraints-based interaction design highlights the desire to confirm the dominant paradigm of abundance.
https://dl.acm.org/doi/10.1145/3706598.3713101
Life cycle assessment (LCA) is a methodology for holistically measuring the environmental impact of a product from initial manufacturing to end-of-life disposal. However, the extent to which LCA informs the design of computing devices remains unclear. To understand how this information is collected and applied, we interviewed 17 industry professionals with experience in LCA or electronics design, systematically coded the interviews, and investigated common themes. These themes highlight the challenge of LCA data collection and reveal distributed decision-making processes where responsibility for sustainable design choices—and their associated costs—is often ambiguous. Our analysis identifies opportunities for HCI technologies to support LCA computation and its integration into the design process to facilitate sustainability-oriented decision-making. While this work provides a nuanced discussion about sustainable design in the information and communication technologies (ICT) hardware industry, we hope our insights will also be valuable to other sectors.
https://dl.acm.org/doi/10.1145/3706598.3713299
People with energy-limiting conditions, such as chronic fatigue syndrome (ME/CFS) and Long COVID, need to limit their activity levels and balance exertion with rest and restorative activities. This practice is known as “pacing”. There is an opportunity for technology to help people with this process, but conducting research with this population can be difficult given their limited and unpredictable energy levels. This research explores how we can use crip theory to inform the development of co-design methods suitable for this cohort, and as an analytical lens to explore how these tools should be designed outside of normative and abelist assumptions about fatigue and productivity. This is done through a 5 week Asynchronous Remote Community study utilising various co-design techniques. These findings point to future designs of pacing technologies and contribute insights about developing more accessible approaches to conducting research with people with energy-limiting conditions.
https://dl.acm.org/doi/10.1145/3706598.3713990
Many narratives around AI systems promise a utopian vision of empowerment, inclusivity, and democratization, yet there remains a gap in how to concretely pursue such a promise. In this paper, we review and analyze a curated set of AI-driven healthcare products, leveraging sociologist Ruth Levitas' three distinct but interrelated forms of utopian thinking—archaeology, ontology, and architecture. We contribute to HCI's Human-AI Interaction agenda by applying this theory to critically examine how AI technologies embed societal ideals, shape user identities, and project alternative futures. This allows us to consider the values and users these systems illustrate as images of the ``good society.'' In doing so, we also make visible the normativity and repetitive nature of technology hype cycles and raise important questions about the future these technologies are shaping.
https://dl.acm.org/doi/10.1145/3706598.3713118
The global environmental crises continue to get worse, fast approaching various irreversible thresholds. While a vast array of approaches to solving sustainability problems are found under the umbrella of Sustainable HCI, their contributions are sometimes hard to compare. In this essay, we describe a set of assumptions that influence what is considered meaningful and important areas of sustainability research, along four dimensions of sustainability: 1) the depth and nature of the sustainability challenges; 2) the role of technological innovation in sustainability; 3) what gets defined as "externalities" to a design or system; and 4) the time perspective used to consider sustainability. We argue that what one assumes within each of these dimensions directly influences what one means by the term "sustainability", which is then reflected in the questions that are asked, the methods chosen, the proposed solutions and the developed systems. By describing these assumptions and some of their commensurate actions, we offer a framework that may enable members of the SHCI community to reflect on and better position their own work and that of others in the field. Our intention is for the framework to lead to better transparency and more constructive conversations about where we might collectively direct our efforts moving forward.
https://dl.acm.org/doi/10.1145/3706598.3714001
The efficacy of digital solutions to increase energy efficiency, including technical optimisations and behavioural influence, has long been a subject of debate within sustainable HCI (SHCI). While the viewpoints of policymakers and academics are frequently published (and often contradictory), less is known about the views of those on the ground. In this paper we ask: What are energy professionals' views of digital energy-saving interventions and their users? What are the challenges they face implementing these interventions? Based on a university campus case study with twelve semi-structured interviews and a focus group with energy and facilities' professionals, we illustrate how they strongly advocate digital efficiency as a pathway to sustainability; yet, this optimism is in apparent tension with key barriers they identify to realising 'their seamless visions', particularly the complexities of the human behaviour they are seeking to optimise. These findings underscore the seductiveness of techno-optimism and the need for more systemic change.
https://dl.acm.org/doi/10.1145/3706598.3713754
The increasing accessibility of large machine learning (ML) models has resulted in their widespread adoption in everyday products, with a correspondingly negative environmental impact. Selecting more suitable ML models could not only improve training time and achievable accuracy, but also long-term sustainability. However, ML developers' model selection process remains underexplored, especially with respect to sustainability trade-offs. Our interviews with 13 ML developers showed that participants select models mainly based on familiarity, accuracy and interpretability, but often overlook sustainability. They critically reflected on the current trends of large models and the lack of available information regarding model sustainability. We present implications for the ML and HCI communities, emphasizing the importance of critical reflection on model selection in education and practice. Based on our insights, we provide initial recommendations for promoting model sustainability evaluation and how the HCI community can assist in making sustainable model alternatives more accessible.
https://dl.acm.org/doi/10.1145/3706598.3713240