artificial intelligence

Automation Otherwise: A Review of “Automating Inequality”

by DANYA GLABAU, Implosion Labs What if we thought differently about how to integrate human and machine agencies?  Automating Inequality: How High-Tech Tools Profile, Police, and Punish the PoorVirginia Eubanks2018, 272 pp, St. Martin's Press As I sat down in to write this review of Virginia Eubanks’ latest book, Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor, I couldn’t help but consider it in light of the growing restiveness among tech workers in response to their companies’ perceived ethical lapses. Rank and file employees have begun to speak out against the use of big data-driven software systems and infrastructure for ethically questionable ends like warfare, policing, and family separation at the United States-Mexico border. To date, these protests have mired several public-private contracts between government agencies and some of the world’s biggest tech companies in controversy, including Google’s Project Maven, a collaboration with the Pentagon to target...

Just Add Water: Lessons Learned from Mixing Data Science and Design Research Methods to Improve Customer Service

OVETTA SAMPSON IDEO Chicago and DePaul University Case Study—This case study provides an inside look at what occurs when methods from the data science and ethnographic fields are mixed to solve perennial customer service problems within the call center and cruise industries. The paper details this particular blend of ethnographic practitioners with a data scientist resulted in changes to design approaches, debunking myths about qualitative and quantitative research methods being at odds and altering team member perspectives about the value of both. The project also led to the creation of innovative blended design research and data science methods to discover and leverage the right customer data to the benefit of both the customer and the call center agents who serve them. This paper offers insight into the untold value design teams can unlock when data scientists and ethnographers work together to solve a problem. The result was a design solution that gives a top-performing company an edge to grow even better by leveraging the millions...

Scale, Nuance, and New Expectations in Ethnographic Observation and Sensemaking

ALEXANDRA ZAFIROGLU Intel Corporation YEN-NING CHANG Intel Corporation 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....

Human-Centered Data Science: A New Paradigm for Industrial IoT

MATTHEW YAPCHAIAN Uptake 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...

Who and What Drives Algorithm Development: Ethnographic Study of AI Start-up Organizational Formation

RODNEY SAPPINGTON Founder, CEO, Acesio Inc. LAIMA SERKSNYTE Head of Behavioral and Organizational Research, Acesio Inc. The focus of this paper is to investigate deep learning algorithm development in an early stage start-up in which edges of knowledge formation and organizational formation were unsettled and contested. We use a debate by anthropologists Clifford Geertz and Claude Levi-Strauss to examine these contested computational forms of knowledge through a contemporary lens. We set out to explore these epistemological edges as they shift over time and as they have real practical implications in how expertise and people are valued as useful or non-useful, integrated or rejected by the practice of deep learning algorithm R&D. We discuss the nuances of epistemic silences and acknowledgments of domain knowledge and universalizing machine learning knowledge in an organization that was rapidly attempting to develop algorithms for diagnostic insights. We conclude with reflections on how an AI-Inflected Ethnography perspective...

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...

“Empathizing” with Machines

CHRIS BUTLER Philosophie PechaKucha Presentation When we study human systems and organizations we have a job that requires to empathize or at the very least be compassionate towards the experiences others are having. This allows to understand their goals, problems, and how we can best make their lives better. When machines start to do things that we can't imagine how do we continue to work with them? What is necessary to create great combinations of humans and machines? What is a machine's purpose? Very simply: it is to serve human purposes. As technology continues to build facades that hide the human element we need to pull back the curtain (like the one in the Wizard of Oz) and see that the tools we build are really us reflected back. We have the choice to make tools that are good or bad for us. Chris Butler, is the Director of AI at Philosophie and frequently speaks on the intersection of product, design, and AI. He has extensive experience from Microsoft, Waze, KAYAK, among others. Through his practice he has created...

Design & Data

Chair: JEANETTE BLOMBERG, Distinguished Researcher, IBM Almaden Research Center Panelists: MARC BÖHLEN, Professor, Department of Art, Emerging Practices, State University of New York at Buffalo TOM LEE, Director of Data Science, Fisher Center for Business Analytics, University of California Berkeley What does a data expert see when they look at a design problem? This panel immerses us in the practices of two data experts, both of whom have collaborated with ethnographers, as they navigate through design challenges in different ways. Chair Jeanette Blomberg draws the panelists and audience into conversation about synergies and challenges for interdisciplinary design collaborations. Jeanette Blomberg is Distinguished Researcher at the IBM Almaden Research Center and Adjunct Professor at Roskilde University in Denmark. She has done foundational work on ethnography in design processes over three decades, and her current research is focused on organizational analytics and the linkages between human action, digital data production, data...

Human and Artificial Intelligence: The Same, Different or Differentiated?

by SIMON ROBERTS, Stripe Partners Today I turned left out of London Bridge station. I usually turn right and take the Tube but instead I went in the other direction and took the bus. I can’t explain why I did that. Perhaps I was responding to a barely discernible change in crowd density or the fact that it was a bit warm today and I didn’t want to ride the Tube. Either way, I was trusting instincts that I am not able to translate into words. Often when I travel around London I reach for the CityMapper app. I rely on it to tell me how best to get from A to Z but I don’t really know how it makes the recommendations it does. Likely it has access to information about the performance of the Tube today or real time knowledge of snarl-ups on London’s medieval roads. It’s clever and I love it. It knows more than I do about these things and what to do about them. The workings of CityMapper are a mystery to me—but so are the workings of my brain. Even if I had a sophisticated understanding of neuroscience, physiology...

Doing Design Research in a Cognitive World

panelists
EPIC2017 Platinum Panel Moderated by: CHRIS HAMMOND (IBM) Panelists: MARK BURRELL (IBM), MELISSA CEFKIN (Nissan Research Center), CHRISTIAN MADSBJERG (ReD Associates) & DAWN NAFUS (Intel) Overview 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...

Towards Multi-Dimensional Ethnography

JULIA KATHERINE HAINES Google, Inc. In this paper, I argue for the value of multi-dimensional ethnography. I explore the potential for ethnography to venture beyond sites, into different dimensions. As an example of work moving in this direction, I present a new approach, dubbed TRACES, which emphasizes the assemblages that constitute our lives, interweaving digital, embodied, and internal experiences. Various data streams and sources provide different vantage points for analysis and synthesis. I illustrate how we have used these to gain greater insight into the human lives we study, with different data sources providing different perspectives on a world, then delve into our use of tools, data sources, and methods from other traditions and other fields, which, combined, give us not only a more holistic picture, but a truer one, which refutes the false dichotomy of the digital and the real. I argue that we must continue to adapt and extend ethnography today into such spaces, and that reformulating the sites of ethnography as dimensions...

Have We Lost Our Anthropological Imagination?

by SAKARI TAMMINEN, Gemic Ever since the 1970s, the promise of increased productivity through technology has been under intense scrutiny. It’s a promise that has pushed questions about nature and the role of technology in society into the hands of scholars, including anthropologists. For those working in industry – really, one of the few places where anthropologists can engage with technology the real, rather than technology the theory – the question always boils down to value. Whether it’s big data, AI, biotech, nanotechnology, robots, smart dust or driverless cars, the one question we’re always looking to answer is: What’s the value of a new technology? Economically, the promise and gains of technological efficiency – particularly information technology – is known as the productivity paradox. Whether a paradox or a series of assumptions about the impact of technology on productivity, the question of the value of technology sparked heated debate among economists over the first wave of computerization. In 1987,...

A Researcher’s Perspective on People Who Build with AI

by ELLEN KOLSTO, IBM Two years ago, I arrived at IBM Design’s Studio in Austin to work on Watson. I didn’t know how to code, thought mastering the set up of my iPhone was a technical achievement and had never researched the world of the developer. Yet here I was, venturing into the very technical realm of artificial intelligence (AI). AI is generally defined by IBM as systems (machines) that can deeply understand a domain, reason towards specific goals, learn continuously from experience and interact naturally with humans. The focus of this definition is on the machine itself. What I have discovered about AI is that while it is certainly about machines, the building of AI is very much about humans. And for my research, it’s about the humans building the machine…the makers. These makers are learning and inventing what it means to actually create a machine that deeply understands a domain or can interact naturally with humans. The definition of these activities is being discovered and reworked everyday. As a researcher...

Have We Lost Our Minds?

by CHRISTIAN MADSBJERG, ReD Associates Since the mid-nineties, the story about IT has been that the “New Information Economy” would give way to vast gains in productivity. We’ve been told that if we simply implement ERP, CRM, and God knows what other kinds of systems, our companies, public services, cities, and infrastructure would be smarter and more efficient. That we humans would be supercharged by technology and become vastly more productive as a result. After 20+ years, one would think there would be indications of this productivity boost at all levels of society, beyond just the valuations of the companies selling us the message. Yet that is not the case. Let’s look at education. According to the Organization for Economic Co-operation and Development (OECD), which tracked the relationship between math performance and access to information and communication technology in schools from 2001 to 2012, there is actually an inverse relationship between how well our kids learn math and how many computers we put in our classrooms....

Speculating about Autonomous Futures: Is This Ethnographic?

by MELISSA CEFKIN & ERIK STAYTON, Nissan Research Center As researchers working on automated vehicles, we are grappling with fundamental questions about how to do research and design for the future. Or, to be more precise, how can we tap into and participate in futures that are in the process of being made, that may both reproduce and rearrange experiences of today? One of the questions we must ask is, what is autonomy to begin with? In the era of the rise of increasingly self-acting machines, what exactly will these machines be autonomous from? How are people grappling with shifting perceptions and experiences of autonomy? Our research has explored how people confront ideas about what the future may hold and, more profoundly, how reconfigurations of socio-technical systems today confront them in their own notions of autonomy. Our paper about one of our research projects on this topic was accepted for EPIC2017, but not without some interesting debate. Anonymous peer reviewers raised a question about whether the work we...