University of Edinburgh
University of Edinburgh
A key challenge in carrying out product design research is obtaining rich contextual information about use in the wild. We present a method that algorithmically mediates between participants, researchers, and objects in order to enable real-time collaborative sensemaking. It facilitates contextual inquiry, revealing behaviours and motivations that frame product use in the wild. In particular, we are interested in developing a practice of use driven design, where products become research tools that generate design insights grounded in user experiences. The value of this method was explored through the deployment of a collection of Bluetooth speakers that capture and stream live data to remote but co-present researchers about their movement and operation. Researchers monitored a visualisation of the real-time data to build up a picture...
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....
Few professions appear more at odds, at least on the surface, than ethnography and data science. The first deals in qualitative “truths,” gleaned by human researchers, based on careful, deep observation of only a small number of human subjects, typically. The latter deals in quantitative “truths,” mined through computer-executed algorithms, based on vast swaths of anonymous data points. To the ethnographer, “truth” involves an understanding of how and why things are truly the way they are. To the data scientist, “truth” is more about designing algorithms that make guesses that are empirically correct a good portion of the time. Data science driven products, like those that Uptake builds, are most powerful and functional when they leverage the core strengths of both data science and ethnographic insights: what we call Human-Centered Data Science. I will argue that data science, including the collection and manipulation of data, is a practice that is in many ways as human-centered and subjective...
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...
by LAURA FORLANO, IIT Institute of Design
Article 4 in the series Data, Design and Civics: Ethnographic Perspectives
On April 1, Secretary of Defense Ashton Carter announced a $317 million federally funded initiative in textile innovation and manufacturing—a national consortium of public and private organizations to be led by MIT. It’s only the most recent project of the Obama administration’s National Network for Manufacturing Innovation, a major effort to re-invigorate the American economy. This ambitious initiative to build manufacturing infrastructure nationwide plans an initial network of 45 Manufacturing Innovation Institutes over 10 years. Led by non-profit organizations, the institutes partner universities, businesses and government agencies with the aim of bridging the gap between basic and applied research in key manufacturing areas such as additive manufacturing (eg, 3D printing), digital manufacturing, lightweight metals, semiconductors, advanced composites, flexible hybrid electronics and integrated photonics.
by SUZANNE CURRIE (GE Digital) & CHRIS MASSOT (Claro Partners), EPIC2015 Salon Hosts
IoT (the Internet of Things) took center stage at CES last January. Many watchers of the giant Consumer Electronics Show opined the array of new products entering this space (many aimed at mainstream consumers) was the main story from Las Vegas this year.
Rewind a few months earlier to EPIC2015 in São Paulo, Brazil, and twenty-five ethnographers are sitting together in a room to consider how IoT fits with human behavior (and how our discipline can forge a better fit).
This was EPIC’s Ethnography & IoT Salon, where attendees explored the question:
With sensors being placed seemingly everywhere (including our bodies) allowing ‘things’ to ‘talk’ to each other, what sustained benefits do these measures provide?
One Salon participant noted that so far, “Billions of dollars are being spent on IoT efforts that don’t make sense.”
We think the issues uncovered in São Paulo are core to the growth of the IoT industry sub-field for this...
by DAWN NAFUS, Intel
There has been a good deal of discussion of the relationship between the EPIC community and new practices of big data. Will the data scientists have the final word on what people value? Are we ethnographers effectively getting disrupted by cheaper and worse data? In a wider sense, what kind of a culture would we live in when stories of lived experience get increasingly sidestepped in favor of a newly re-empowered aggregate? Story would surely still matter, but the population of people in any position to tell stories with data would narrow drastically. This is not an inevitability, of course, and members of the EPIC community have written about reclaiming quantification in various ways (above, also contributions from Neal Patel and yours truly here).
It turns out we are not the only ones asking these larger questions. The Quantified Self community is too, albeit for different reasons. I began my research in quantified self, admittedly, because the name alone suggested some of my worst fears about what technology...