Ethnography for a data-saturated world

Hannah Knox, Dawn Nafus

• Publisher: Manchester University Press
• Year: 2018
• ISBN:
  9781526127617
• Edition: 1
• Topic: Social Sciences
• Subtopic: Anthropology
• Index: Social Sciences

This edited collection aims to reimagine and extend ethnography for a data-saturated world. The book brings together leading scholars in the social sciences who have been interrogating and collaborating with data scientists working in a range of different settings. The book explores how a repurposed form of ethnography might illuminate the kinds of knowledge that are being produced by data science. It also describes how collaborations between ethnographers and data scientists might lead to new forms of social analysis

1
Introduction: ethnography for a data-saturated world

Hannah Knox and Dawn Nafus

It is increasingly difficult to attend to social and political relations in the contemporary world without recognising that they are in some way constituted by digitally generated data. From censuses that describe national populations to polls that predict and chart election outcomes, from audience surveys and click-counters that are used to price advertising to credit ratings and market analyses that determine financial relations, social worlds are entangled with data that is produced, circulated and analysed using computational devices. To paraphrase Walter Benjamin's famous aphorism about the effects of the then new technologies of film and photography on human engagement with the world, ‘every day it seems the urge grows stronger to get hold of a subject at very close range by way of its [data]’ (Benjamin 2008 [1939]).

During the 2000s, with the continued increase in computational information processing capacity and the huge spread of smartphones and sensors there has been an increasing public concern about the challenge of data's ‘bigness’ (Anderson 2008; Bowker 2014). Practices of data collection and collation seem to have exploded in recent years with the proliferation of electronically connected devices that are capable of sensing and producing data about the world and circulating that data to a range of users including governments, corporations and individuals. Using analogies from older industries, the economic and social potential of data has led to its characterisation as the ‘new oil’, offering potentially new revenue streams, new ways of imagining and governing populations and new methods of verification and accountability. Those who are more concerned about the political structures and effects of this new resource also talk of data as ‘exhaust’ – the byproduct of human interaction that needs to be both ‘captured’ by the analytic converter of data science and properly managed and governed to mitigate the dangers associated with ambiguous attribution, security, corporate monopoly and nefarious techniques of surveillance and control.

Most recently, other social, political and ethical questions have arisen about the implications of automation and machine learning.¹ Newer computational techniques for parsing large datasets focus mainly on what machines can and cannot recognise, asking whether some data has enough of the same features as some other data such that a machine can determine that they are both indeed a picture of a dog, or a stressed tone of voice. These automation practices intensify a sense of opaqueness. Many worry that machine learning systems can grow so complex that it can be difficult even for the very people who designed the system in the first place to say how machines make the determinations that they do. Consideration of the social implications of automation has provided a new realm of debate about data and its ethical implications. No longer are questions about data merely a matter of how objects and subjects become known through different quantities and qualities of data collection, nor are they about who has and should have access to that knowledge. They have been extended to incorporate more fundamental questions about what happens to our sense of what knowledge is when the agents of knowledge production are no longer necessarily even human.

New data relations thus not only raise questions about how to better know and act upon the world, but also shed light on the very foundations of what we consider knowledge to be. This book starts from the conceit that attention to digital data opens up the possibility of interrogating more broadly the presuppositions, techniques, methods and practices out of which claims about the value and purpose of knowledge gain power. To talk of digital data is to talk of one facet of a broader terrain of knowledge production, of which numerical or digital data is only one part. Seeing data practices and concerns as a matter of how to more broadly understand and make the world demands then that we locate digitally collected data as one of many ways of knowing, which include critical reflection, affective experience and, most importantly for this collection, ethnography.

In spite of the level of enthusiasm and debate about the possibilities and challenges of big data, grounded empirical studies of the knowledge practices entailed in contemporary data analytics are surprisingly few and far between. The journal Big Data & Society has done much to generate a social response to big data issues but this is one of very few places where ethnographic accounts of big data as a field of practice exist at all. In part this is no doubt due to the time that it takes for ethnographies to work their way through the publishing system. There are some important studies in the pipeline such as Nick Seaver's (2015) doctoral study on music recommendation analysts and Asta Vonderau's current research project on cloud computing, but at the date of writing these are yet to be published. Meanwhile other data-related phenomena such as practices of modelling and visualisation in scientific settings (Dumit 2004; Myers 2015) the appearance of bitcoin (Maurer 2012) and the building of databases for the collation and navigation of hybrid and indigenous knowledge forms (Shrinivasan et al. 2009; Verran and Christie 2014) provide an important starting point from which to approach big data practices ethnographically. Such studies are much needed as a way of cutting through the media hype in business press around big data and its promises (see Boellstorff and Maurer (2015) for early work in this area). But ethnographic studies also offer more than just empirical detail that can provide a reality-check on otherwise hyped phenomena. Ethnography done well also holds the promise of generating a new way of theorising and understanding digital data by building novel analytical concepts that are appropriate to the kinds of relations of knowledge production that digital data itself entails.²

This book therefore aims to fill this gap of ethnographic approaches to contemporary digital data by providing a window on to the cultures, practices and infrastructures and epistemologies of digital data production, analysis and use. Understanding the production and use of digital data and its implications for knowledge is an issue that cuts across a huge array of different areas of practice (science, commerce, government, development, engineering etc.) and covering this terrain in its entirety is far beyond the ability of any one volume. In order to provide a path through this complexity we therefore take as our core focus the way in which digital data is troubling and reconstituting expertise. This focus on expertise allows us to do something that is relatively unusual in an edited collection: both to provide a comparative description of a number of empirical fieldsites where communities of experts are self-consciously forming around the new possibilities put on the table by digital data; and to consider how our understanding of the ways experts make and remake digital data might reframe our own expertise as ethnographers. This is not a methods book, but it is a book about what digital data is doing to empirical methods that sustain claims to expertise, with a particular focus on its implications for ethnography.

We approach digital data then, as a comment on the relationship between knowledge, expertise and the methods through which knowledge is produced. We do this in order to interrogate whether data practices might be part of a broader unsettling of how to know the social. We focus specifically on the interplay between digital data and ethnography as two ways of understanding contemporary possibilities available for knowing, formatting and intervening in the world. This is not just a book about how ethnographic knowledge can fill in the gaps of data science (e.g. boyd and Crawford 2012) nor is it just a demonstration of how ethnography can shed light on what data science actually is and the effects it produces (although both of these are touched upon in this volume). Rather, the book sets out a more ambitious aim of exploring what might be happening to social knowledge production at the interface of data and ethnography, with a view to outlining new directions in social research and simultaneously attending to the epistemological foundations of that research.

Past experiments in digital data and ethnography

The conversations that this book charts between digital data³ and ethnography offer, we suggest, a fresh terrain in which to ask questions about the social production of knowledge. However, the question of how to combine data-oriented and qualitative approaches in ethnographic research is not new.⁴ Anthropologists have, since at least the 1960s, periodically turned to the possibilities that computation might hold for assisting with anthropological analysis. Gregory Bateson and Margaret Mead's forays into cybernetics as a method for analysing social systems offer an early example of how information-theoretical thinking was incorporated into anthropology and used to reshape a distinctive approach to the discipline (Bateson 1972; Mead 1968). Ecological anthropologist Roy Rappaport's groundbreaking study of the relationship between ritual and ecology offered a similarly systems-theoretical method of socio-natural analysis to chart the relationship between the abundance or scarcity of ecological resources and ritual process, an approach which has more recently been taken up in computer simulation work in Bali by Stephen Lansing and colleagues (Rappaport 1977). Lévi-Strauss meanwhile explored the conceptual potential of computers in the development of structural anthropology and was conversant with the logic of information theory and its influence on structural linguistics (see Seaver 2014b; Geoghegan 2011). Whilst these first theoretical explorations into systems theory and structural analysis took place in the 1950s and 1960s, their influence has gained traction again in recent years and is now felt in much contemporary anthropology, particularly amongst those who study ecological relations and technology (Boyer 2013; Kohn 2013).

As computers developed and became more affordable, a number of anthropologists were quick to explore the broader methodological potential of these new computational devices for assisting with the collection and analysis of field materials. This is outlined in books like Dell Hymes's 1965 volume on the use of computers in anthropology (Hymes 1965). Studies such as Marie Corbin and Paul Stirling's database-supported analysis of kinship and family in Spain in the 1970s established a precedent for the use of computers in anthropological analysis. The Centre for Social Anthropology and Computing (CSAC) was established at the University of Kent in 1986. This remains a key location for discussions and collaborations around the use of computers in anthropological analysis (Ellen 2014).

A parallel field in which anthropologists have played an important part is the study of human–computer interaction. Human–computer interaction (HCI) scholars have a well- established history of entangling digitally produced data with ethnography. During the 1980s for example, anthropologists working at the Palo Alto Research Centre at Xerox Park were noted for bringing ethnomethodological approaches to HCI, which drew on numerical and video data alongside ethnographic fieldnotes to understand the social dynamics of situat...

Table of contents

• Cover
• Series page
• Title page
• Copyright page
• Dedication
• Contents
• List of figures
• Notes on contributors
• Preface and acknowledgements
• 1 Introduction: ethnography for a data-saturated world
• 2 Data scientists: a new faction of the transnational field of statistics
• 3 Becoming a real data scientist: expertise, flexibility and lifelong learning
• 4 Engineering ethnography
• 5 ‘If everything is information’: archives and collecting on the frontiers of data-driven science
• 6 Baseless data? Modelling, ethnography and the challenge of the anthropocene
• 7 Operative ethnographies and large numbers
• 8 Transversal collaboration: an ethnography in/of computational social science
• 9 The data walkshop and radical bottom-up data knowledge
• 10 Working ethnographically with sensor data
• 11 The other ninety per cent: thinking with data science, creating data studies – an interview with Joseph Dumit
• Index