Home automation has made big promises for utilizing intelligent technology to help the lives of everyday people, but the potential of the technology can only be as good as our understanding of the world we are trying to improve. In this PechaKucha, I share insights from my years of conducting ethnography in homes where families have lived alongside AI and automated technology. Our initial tries at intelligent technology in the home were modeled after our own assumptions, but it failed to account for the full variables of the ‘household’, which had an agency of its own. When technology has the potential to disrupt not only our workflows, but relationships between people in the home, it's the responsibility of technologists and ethnographers to provide the critical human perspective necessary for technology to live in harmony with people.
LaiYee Ho is the co-founder of Delve (www.delvetool.com), where she pours her years of experience as a UX researcher and designer into creating...
Design Council & Royal College of Art
This paper describes an experiment, designed and developed with the ultimate aim of fostering low-pollution and low-carbon social innovation. It offers an evidence-based practical alternative to conventional, technological approaches and narratives of smart cities aimed at sensing air pollution and mitigating the effects of climate change.
In this experiment a new voice user interface is designed, developed and tested with input from participants – to explore the potential of a new, more socially minded adaptation to current AI assistant devices in the home and enhance the field of smart technology design. The experiment is developed with a group of participants to demonstrate how design research can raise novel questions and inform disciplines with an interest in behaviour change, environmental pollution and smart homes. This work demonstrates the potential for technologies to increase the degree of participation in reducing pollution in cities and facilitate the articulation...
Case Study—We consider new expectations for ethnographic observation and sensemaking in the next 20-25 years, as technology industry ethnographers' work unfolds in the increasing presence of the type of analytical capabilities specially trained (and self-training) machines can do ‘better’ and ‘cheaper’ than humans as they can take in, analyze and model digital data at much higher volumes and with an attention to nuance not achievable through human cognition alone. We do so by re-imagining three of our existing ethnographic research projects with the addition of very specific applications of machine learning, computer vision, and Internet of Things sensing and connectivity technologies. We draw speculative conclusions about: (1) how data in-and-of-the world that drives tech innovation will be collected and analyzed, (2) how ethnographers will approach analysis and findings, and (3) how the evidence produced by ethnographers will be evaluated and validated....
ELIZABETH A. KELLEY
ILLUME Advising, LLC
AMANDA E. DWELLEY
ILLUME Advising, LLC
Case Study—This case draws on work in the energy efficiency industry where many utilities rely on data-driven insights and decision-making to encourage consumers to adopt energy-saving products and behaviors. In this highly regulated industry, utility staff must show value through big data, and studies often rely exclusively on quantitative data analytics to create behavioral models to explain or predict behavior. However, purely data-driven research often fails to answer questions about why customers behave a certain way, and what product or program managers and marketers can do about it. In this case study, the team from ILLUME Advising LLC (ILLUME), a research consultancy in the clean energy industry, illustrates how their cross-functional team paired qualitative and quantitative research on residential home energy use. The case study draws on an exploratory market and segmentation study for an electric utility interested in engaging customers...
PechaKucha—Our homes are becoming instrumented glass houses where even the most intimate and personal acts may leave data footprints that companies providing services (and potentially others) can access. As homes become instrumented with data-generating technologies, existing information boundaries will be tested, and householders will take on the burden of creating new boundaries on information about their homes lives. Existing low-tech methods of obfuscating activities will no longer suffice. As ethnographers working on smart home solutions, we wonder: what information about which daily activities and home conditions will make householders uncomfortable living in glass houses? Who do people imagine will be looking through those glass facades, and what do they worry about them ‘seeing’? Even when the activities they consider sensitive are self-described as ‘normal’, how do we design smart home solutions so...