EPIC2019 Panel, Providence, Rhode Island
DAWN NAFUS, Senior Researcher, Intel Labs
MELISSA CEFKIN, Principal Researcher, Alliance Innovation Lab Silicon Valley
MICHAEL LITTMAN, Professor of Computer Science & Co-Director of the Humanity Centered Robotics Initiative, Brown University
CLAPPERTON CHAKANETSA MAVHUNGA, Associate Professor of Science, Technology, and Society, MIT
HELI RANTAVUO, Senior Insights Manager, Growth Opportunities Mission, Markets Business Unit, Spotify
Robotics, machine learning, and other technologies are provoking new hopes and fears about human agency. Tropes of the charismatic lone innovator, whether hero or villain, are also starting to lose popular currency. When we acknowledge that the agents of the built world are not just people who call themselves “innovators” but are made up of many kinds of people, and physical materials, new questions arise. How do issues of responsibility, accountability, attribution, and even regulation get solved in situations of distributed...
by DAWN NAFUS (Intel), EPIC2018 Co-chair
We chose Evidence as the EPIC2018 theme in part to explore this question of why some things constitute evidence and not others. There are lots of factors we could point to, but since I’m standing next to a data scientist the first one I’ll talk about is digitization.
Digitization changes how people live, and it creates forms of evidence about people’s lives that we need to reckon with methodologically. Many of us are in the thick of organizations that handle some complicated datasets, traces of people and their environments, and so on. We’ve got to figure out how to engage with them, and I think that means we need new approaches if we are going to meaningfully intervene.
The toolbox of user experience is only going to get us so far. So we’re going to need some friends, particularly those data scientists who are, like us, committed to the idea that datasets ought to be moored in some kind of social reality, and that they can’t just be built based on what’s expedient at the time. While...
by TYE RATTENBURY (Salesforce) & DAWN NAFUS (Intel)
As EPIC2018 program co-chairs, we developed the conference theme Evidence to explore how evidence is created, used, and abused. We’ll consider the core types of evidence ethnographers make and use through participant observation, cultural analysis, filmmaking, interviewing, digital and mobile techniques, and other essential methods, as well as new approaches in interdisciplinary and cross-functional teams.1
We’ve also made a special invitation to data scientists to join us in Honolulu to advance the intersection of computational and ethnographic approaches. Why?
One of us is a data scientist (Tye) and the other an ethnographer (Dawn), both working in industry. We regularly see data science and ethnography conceptualized as polar ends of a research spectrum—one as a crunching of colossal data sets, the other as a slow simmer of experiential immersion. Unfortunately, we also see occasional professional stereotyping. A naïve view of “crunching” can make it seem...
EPIC2017 Platinum Panel
Moderated by: CHRIS HAMMOND (IBM)
Panelists: MARK BURRELL (IBM), MELISSA CEFKIN (Nissan Research Center), CHRISTIAN MADSBJERG (ReD Associates) & DAWN NAFUS (Intel)
Increasingly, experiences are being created that incorporate augmented intelligence, promising to make us smarter, more efficient, and more effective. Doctors can recommend more comprehensive personalized treatment plans, teachers can provide lesson plans tailored to individual students, and farmers can vary crop irrigation and fertilization cycles in response to predicted weather patterns. Human capabilities (some might say intelligence) are being augmented, aided by machine learning algorithms that interpret and find meaning in vast quantities of both structured and unstructured data.
This panel addresses challenges of doing design research in a cognitive world where predictive analytics, conversational interfaces, and augmented intelligence are core aspects of the technology solutions being designed. What skills...
Tutorial Instructor: DAWN NAFUS, Intel
Activity trackers, instrumented environments, and other kinds of electronic monitors offer new possibilities and new challenges for ethnographic research. They provide a trace of what goes on when the researcher isn't there, and can help research participants reflect on their lives in a new way. In the right contexts, sensor data can help bridge the gap between ethnographic and data science approaches. At the same time, sensors can be challenging to set up, and occasionally mislead if the context is poorly understood.
This tutorial will help you determine when and how to use sensor data in an ethnographic research practice. We'll talk about some of the practical pitfalls to watch out for, when you do and don't need a data scientist, and some of the trickier aspects of inviting research participants to reflect on the data collected about them. Participants will learn how to:
Assess sensors for maximum research value
Ensure the research setup is feasible
The EPIC community has been wrestling with ways to integrate quantitative and qualitative methods in light of the increasing role that digital data plays in business practices. Some focus on methodological issues (digital data as method), while others point to the consumer value in data products (data as thing in the world). This paper argues that “digital data as method” and “digital data as thing in the world” are becoming increasingly intertwined. We are not merely witnessing ethnographers’ haulting embrace of digital data, but a wider process of the domestication of data, in which we, alongside the people we study, are participants. The domestication of data involves everyday situations in which ordinary people develop their own sense-making methods—methods remarkably similar to ethnographic knowledge production. In this way, the domestication process tightens the connection between data as thing in the world and data as method. I argue that seeing the interconnection gives us the...
by DAWN NAFUS, Intel Corporation
*Join Dawn Nafus on September 1 when she hosts Ethnography & Quantified Self at EPIC2016.
A few years ago, a colleague had asked me about the adoptability of biosensors—a rapidly evolving category of sensors that detect an ever-expanding array of stuff about the body or the environment. Water quality, air quality, hormones, temperature, microbiomes are increasingly possible to measure with consumer-grade devices and services. He had seen how medical sensing technologies had become smaller and cheaper, increasingly reworked into consumer devices for use outside of clinical settings. How much appetite would there be for an expanded reperotoire of data in ordinary people’s hands? Is that appetite really a consumer one, or one that was more likely to come into play in institutional contexts like biomedical research or technology-delivered healthcare?
These were complicated social and cultural questions, made even more complicated by the fact that, at the time, there were really only limited...
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...
DAWN NAFUS, ROGERIO DE PAULA and KEN ANDERSON
This paper explores notions of ‘voice’ as it relates to Web 2.0. We begin by tracing the social meanings of Web 2.0 technologies Brazil. There the notions of ‘voice’ as conceived of in the American media are absent, yet significant collective action took place online through a kind of speaking out. Next the paper describes the conflation of voice with a notion of social networks to explain how the American media misread the Brazilian action. This is achieved by an incredible plasticity and abstraction of the ‘Web 2.0’ construct, which flattens otherwise qualitatively meaningful distinctions. This puts us on some ground to raise the issue of how abstractions might become relationships. This, we argue, is evidenced both in terms of how Brazilians might interpret online relationships, and how Web 2.0 hype betrays a politics of abstraction at work in the wider economy....
DAWN NAFUS and KEN ANDERSON
This paper explores discourses of the ‘real’ in commercial ethnographic research, and the transitions and transformations those discourses make possible and impossible. A common strategy to legitimize industrial ethnography is to claim a special relationship to ‘real people’, or argue that one is capturing what is ‘really’ happening in ‘natural’ observation. Distancing language describes ‘insights’ into a situation somehow separate from ourselves, ‘findings’ and ‘quotes’ that we seemingly extract from one context and plunk in another. Whether it is chimps (in Jane Goodall’s case) or consumers (in ours); we know what is going on or not. This model of ethnographic knowing has adopted the naturalistic science discourse of the behavioralist—the neutral observers in an environment. Here we explore how this epistemic culture has been created and its ‘real’ consequences. What we do not attempt is an assertion of the merits of one kind of ethnography over another, or a rehash of...
DAWN NAFUS, ROGERIO DE PAULA, KATHI KITNER, RENEE KURIYAN and SCOTT D. MAINWARING
This paper documents the beginnings of Intel’s recently launched Consumerization project, and uses these early experiences as a way into exploring new paths to business relevance and impact. These paths weave in and out of the increasingly institutionalized position of corporate ethnography as research that takes place before products are designed. These paths are one response to wider transformations in the business environment, and are not a general prescription, “ethnography should now do X in corporations.” However, this project does embody a significant move away from past modalities of conducting and applying research, and in doing so reveals broader possibilities for ethnography that may prove viable for others in different contexts. We begin by providing some institutional history and exploring the wider industry transformations that compelled us to design a research project in the way that we did. The paper goes on to describe our approach...
KEN ANDERSON, DAWN NAFUS, TYE RATTENBURY and RYAN AIPPERSPACH
Field research holds a special place for those who conduct it. It is also our anchor for relevance in the corporation. This paper explores the authors’ experiences with “ethno-mining”, a way of joining data base mining and ethnography. Since 2004 we have been using a variety of sensing and behavioral tracking technologies in conducting field research. We will present the main characteristics of doing ethno-mining, compare ethno-mining to other field research technologies, highlight the strengths of ethno-mining in co-creating data with participants and conclude by noting how the representations have opened new conversations and discourses inside the corporation. In this way, these new opportunities to collect sometimes counterintuitive data contributes to the research itself as well as the ongoing process of constructing oneself as relevant....