evidence

Livable Relations with Metrics

by MINNA RUCKENSTEIN, University of Helsinki It is easy to become pessimistic, if not dystopic, about tracking technologies. The current digital services landscape promotes scoring, selecting and sorting of people for the purposes of maximizing profit. Machine logics rely on profiling characteristics and predicting actions, and management by algorithms appears to be disproportionately affecting those with temporary and low-income jobs. Tracking technologies become complicit in deepening and accelerating social divisions and inequalities. The most vulnerable in societies have no say in how their actions are monitored and lives are harmed by algorithmically produced metrics. In this context, Quantified Self (QS) – an international community of ‘self-trackers’ that shares insights gained through self-quantification and data analysis – seems rarified, an example of the privileged techno-elite positioned to use tracking data to pursue their own values and goals. With this limitation, QS hardly appears to be a useful prism...

How Is Evidence Created, Used & Abused? EPIC2018 Opening Remarks

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...

ReHumanizing Hospital Satisfaction Data: Text Analysis, the Lifeworld, and Contesting Stakeholders’ Beliefs in Evidence

JULIA WIGNALL Seattle Children's Hospital DWIGHT BARRY Seattle Children's Hospital Case Study—Declining clinician engagement, increasing rates of burnout, and stagnant patient and family experience scores have led hospital leadership at Seattle Children's Hospital to submit requests to a data scientist and an anthropologist to identify key themes of survey comments and provide recommendations to improve experience and satisfaction. This study explored ways of understanding satisfaction as well as analytic approaches to textual data, and found that various modes of evidence, while seemingly ideal to leaders, are hard pressed to meet their expectations. Examining satisfaction survey comments via text mining, content analysis, and ethnographic investigation uncovered several specific challenges to stakeholder requests for actionable insights. Despite its hype, text mining struggled to identify actionable themes, accurate sentiment, or group distinctions that are readily identified by both content analysis and end users, while more...

The Stakes of Uncertainty: Developing and Integrating Machine Learning in Clinical Care

MADELEINE CLARE ELISH Data & Society Research Institute The wide-spread deployment of machine learning tools within healthcare is on the horizon. However, the hype around “AI” tends to divert attention toward the spectacular, and away from the more mundane and ground-level aspects of new technologies that shape technological adoption and integration. This paper examines the development of a machine learning-driven sepsis risk detection tool in a hospital Emergency Department in order to interrogate the contingent and deeply contextual ways in which AI technologies are likely be adopted in healthcare. In particular, the paper bring into focus the epistemological implications of introducing a machine learning-driven tool into a clinical setting by analyzing shifting categories of trust, evidence, and authority. The paper further explores the conditions of certainty in the disciplinary contexts of data science and ethnography, and offers a potential reframing of the work of doing data science and machine learning as “computational...

Cooperation without Submission: Some Insights on Knowing, Not-Knowing and Their Relations from Hopi-US Engagements

JUSTIN B. RICHLAND Associate Professor of Anthropology, UC Irvine; Faculty Fellow, American Bar Foundation; Associate Justice, The Hopi Appellate Court EPIC2018 Keynote Address...

Empathy Is not Evidence: Four Traps of Commodified Empathy

RACHEL ROBERTSON Shopify PENNY ALLEN Shopify Product teams, including our own, often interpret empathy as evidence. However, in practice, empathy is actually something that drives us to seek evidence. By observing and evaluating various examples within Shopify, we have identified 4 traps that are common in the way empathy is manifested. We modelled the relationship between empathy, problems, evidence, and decisions to provide strategies for how to use empathy effectively while being sympathetic to its limitations. Since empathy drives us to seek evidence, and thus cannot be considered evidence itself, empathy must be used at an appropriate level of abstraction throughout the product decision-making process in order to influence good decisions....

Can I Get a Witness? The Limits of Evidence in Healthcare Quality Evaluation Systems in American Hospitals

LINDSAY FERRIS Ad Hoc, LLC DR. NICHOLE CARELOCK Ad Hoc, LLC “I got verbals, but verbals don’t hold up in court….I need it in black and white.” After Sheila submits hospital quality data to the Center for Medicaid and Medicare Services (CMS), reports indicate that her data hasn’t been received. She makes countless calls to the CMS Help Desk to get answers. They reassure her numerous times that they have her data, yet Sheila is insistent that she needs to see the change explicitly stated in the report. Sheila makes it her personal crusade to obtain material evidence because only written testimony will prove that her data has been submitted successfully and protect her facility from CMS penalties. At a time when we are becoming increasingly reliant on data and technology as the ultimate bearers of truth, Sheila exemplifies how people become stewards of evidence in service to these technical systems. As she moves her facilities’ data through CMS’ error-ridden reporting system, the burden of proof is on her to provide the...

Expedience, Exigence and Ethics

by ELIZABETH CHURCHILL, Vice President, ACM This is a cautionary tale featuring a well-structured memo and an effective, carefully designed infographic. Both of these artifacts could be considered excellent examples of their respective crafts—the first of technical communication, and the second of graphical information design. Both are also examples of how ethics can be subsumed to expedience, and how the everyday practices of their production were subject to the exigencies of locally acceptable rhetorics and social order. I believe the stories of these artifacts are cautionary tales for us and our own work. Through the (admittedly dark) cautionary tales of these artifacts, I invite us to consider the conditions in which we, EPIC attendees and our international community, produce artifacts that convey “evidence”. I invite us to question the milieux and “atmospheres” in which we work, the sources from which we collect data, our practices and processes when producing evidencing rhetorics, and the role of such evidence in...

What Is Going on with the Weather? Reflections on Gaps Between Data and Experience

by HANNAH KNOX, University College London If it’s summer in your part of the world (or even if it’s winter), you’ve probably been feeling the heat. On 5 July, Ouargla, Algeria recorded 51.3°C (124.3°F), the highest temperature ever reported in Africa. A few days later, Areni, Armenia hit a record 42.6°C (108.7°F), and on 17 July, Badufuss in Norway topped its charts at 33.5°C (92.3°F). Perhaps most disturbing were reports of people collapsing in the fields in Japan, where high humidity exacerbated record-breaking temperatures of over 40°C (104°F). Japan declared a natural disaster, a designation normally reserved for earthquakes and tsunamis.1 “Something is going on” – people feel – “but what?” Of course, climate scientists have been beating their drums for decades, pushing out papers, reports, and campaigns about the risks of anthropogenic climate change. But dramatic and even deadly weather events are, it turns out, rather effective at opening opportunities for speaking about climate change across...

Ethnography, Economics and the Limits of Evidence

by ERIN B. TAYLOR, Canela Consulting & Holland FinTech Evidence produced within quantitative disciplines like economics and finance carries an aura of gospel. The numbers, models, and forecasts we see in economic reports and market analyses in the news and reports seem certain, authoritative, and unarguable. Built on large data sets that are analyzed with widely accepted theories and tools, economic and financial evidence have become hugely influential in governance and business—so much so that more qualitative approaches have been sidelined. Even political economy, the original economics, has been pushed away in favor of what’s now called ‘evidence-based decision-making’. The presumption is that numerical data is the only solid information, and that the analytical tools used in economic and market analysis are reliable. Of course, as we know now, this faith in economic evidence can be dangerous. As markets crashed around the world during the global financial crisis of 2007–2008, confidence in all kinds of quantitative...

Data Science and Ethnography: What’s Our Common Ground, and Why Does It Matter?

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...