interdisciplinary research

Screenplay, Novel, and Poem: The Value of Borrowing From Three Literary Genres to Frame Our Thinking as We Gather, Analyze, and Elevate Data in Applied Ethnographic Work

MARIA CURY ReD Associates MICHELE CHANG-MCGRATH ReD Associates Applied ethnography still struggles with the fundamental challenges of (1) framing research to obtain ‘thick’ data, (2) making sense of data in teams and with clients, and (3) making a convincing case with data in challenging environments. We have observed that borrowing from literary genres can be effective in addressing these challenges. We therefore argue that in an age of data science, it is just as important to draw from the literary arts when gathering, analyzing, and elevating evidence to inspire change in applied ethnographic work. We raise three specific applications of literary genres to distinct project phases, to improve how data is collected and analyzed, and how data travels. In this paper we show: (1) how the screenplay can help solve challenges in research framing, to obtain thicker data; (2) how the novel can help solve challenges in analysis, to turn data into meaningful evidence; (3) how poetry can help solve challenges in the opportunities-development...

Designing for Interactions with Automated Vehicles: Ethnography at the Boundary of Quantitative-Data-Driven Disciplines

MARKUS ROTHMÜLLER School of Architecture, Design and Planning, Aalborg University Copenhagen, Denmark and Shift Insights & Innovation Consulting PERNILLE HOLM RASMUSSEN School of Architecture, Design and Planning, Aalborg University Copenhagen, Denmark SIGNE ALEXANDRA VENDELBO-LARSEN School of Architecture, Design and Planning, Aalborg University Copenhagen, Denmark Case Study—This case study presents ethnographic work in the midst of two fields of technological innovation: automated vehicles (AV) and virtual reality (VR). It showcases the work of three MSc. Techno-Anthropology students and their collaboration with the EU H2020 project ‘interACT’, sharing the goal to develop external human-machine interfaces (e-HMI) for AVs to cooperate with human road users in urban traffic in the future. The authors reflect on their collaboration with human factor researchers, data scientists, engineers, experimental researchers, VR-developers and HMI-designers, and on experienced challenges between the paradigms of qualitative and quantitative...

Contextual Analytics: Towards a Practical Integration of Human and Data Science Approaches in the Development of Algorithms

MILLIE P. ARORA MIKKEL KRENCHEL JACOB MCAULIFFE ReD Associates POORNIMA RAMASWAMY Cognizant As algorithms play an increasingly important role in the lives of people and corporations, finding more effective, ethical, and empathetic ways of developing them has become an industry imperative. Ethnography, and the contextual understanding derived from it, has the potential to fundamentally change the way that data science is done. Reciprocally, engaging with data science can help ethnographers focus their efforts, build stronger and more precise insights, and ultimately have greater impact once their work is incorporated into the algorithms that increasingly power our society. In practice, building contextually-informed algorithms requires collaboration between human science and data science teams who are willing to extend their frame of reference beyond their core skill areas. This paper aims to first address the features of ethnography and data science that make collaboration between the two more valuable than the sum of their respective...

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 Perfect uberPOOL: A Case Study on Trade-Offs

JENNY LO Uber Technologies STEVE MORSEMAN Uber Technologies Case Study—One of Uber’s company missions is to make carpooling more affordable and reliable for riders, and effortless for drivers. In 2014 the company launched uberPOOL to make it easy for riders to share their trip with others heading in the same direction. Fundamental to the mechanics of uberPOOL is the intelligence that matches riders for a trip, which can introduce various uncertainties into the user experience. Core to the business objective is understanding how to deliver a ‘Perfect POOL’—an ideal situation where 3 people in the vehicle are able to get in and out at the same time and location allowing for a more predictable and affordable experience. This case study argues that, for a reduced fare and a more direct route, riders are willing to forego the convenience of getting picked up at their door in exchange for waiting and walking a set amount to meet their driver. This case study explores the integration of qualitative and quantitative research...

Humans Can Be Cranky and Data Is Naive: Using Subjective Evidence to Drive Automated Decisions at Airbnb

STEPHANIE CARTER Airbnb RICHARD DEAR Airbnb Case Study—How can we build fairness into automated systems, and what evidence is needed to do so? Recently, Airbnb grappled with this question to brainstorm ways to re-envision the way hosts review guests who stay with them. Reviews are key to how Airbnb builds trust between strangers. In 2018 we started to think about new ways to leverage host reviews for decision making at scale, such as identifying exceptional guests for a potential loyalty program or notifying guests that need to be warned about poor behavior. The challenge is that the evidence available to use for automated decisions, star ratings and reviews left by hosts, are inherently subjective and sensitive to the cross-cultural contexts in which they were created. This case study explores how the collaboration between research and data science revealed that the underlying constraint for Airbnb to leverage subjective evidence is a fundamental difference between ‘public’ and ‘private’ feedback. The outcome of this integrated,...

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...