Smart home technologies with the ability to learn over time promise to adjust their actions to inhabitants’ unique preferences and circumstances. For example, by learning to anticipate their routines. However, these promises show frictions with the reality of everyday life, which is characterized by its complexity and unpredictability. These systems and their design can thus benefit from meaningful ways of eliciting reflections on potential challenges for integrating learning systems into everyday domestic contexts, both for the inhabitants of the home as for the technologies and their designers. For example, is there a risk that inhabitants’ everyday lives will reshape to accommodate the learning system’s preference for predictability and measurability? To this end, in this paper we build a designer’s interpretation on the Social Practice Imaginaries method as developed by Strengers et al. to create a set of diverse, plausible imaginaries for the year 2030. As a basis for these imaginaries, we have selected three social practices in a domestic context: waking up, doing groceries, and heating/cooling the home. For each practice, we create one imaginary in which the inhabitants’ routine is flawlessly supported by the learning system and one that features everyday crises of that routine. The resulting social practice imaginaries are then viewed through the perspective of the inhabitant, the learning system, and the designer. In doing so, we aim to enable designers and design researchers to uncover a diverse and dynamic set of implications the integration of these systems in everyday life pose.
Everyday domestic life will inevitably change in the future, as smart homes-alongside their inhabitants-will increasingly be able to learn over time. The purpose of my PhD research is to explore what it could look like if learning smart home technologies (SHTs) become integrated into the future domestic everyday. More specifcally, I seek to study what role these technologies and their designers could, would, and should play in afecting households' domestic lives. Existing literature on this topic focuses on making algorithms understandable (XAI) and ofering users more control over how their homes learn. So far, most of these eforts have been reactive, analyzing and responding after these technologies have already entered everyday domestic life. This project, however, adopts a more dynamic and anticipatory approach. This means investigating how these learning technologies interact with the dynamics of the domestic-often unpredictable-everyday.
CCS CONCEPTS• Human-centered computing → HCI theory, concepts and models.
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