AI in
Context

The Labor of
Integrating New
Technologies

Alexandra Mateescu
Madeleine Clare ElishContents

4

Executive Summary

8

Introduction

12
15

Human Infrastructures of AI
The Hyped Machine

18

Farm Management

34

Grocery Retail

50

Conclusion:

53

Acknowledgments

21
Shifting Roles:
		
The Labor of Integration
28
Unevenly Distributed Costs of Experimentation:		Don’t Be the Sea Lion

38
Shifting Roles:
		
The Erosion of Frontline Grocery Retail Jobs
44
Unevenly Distributed Costs of Experimentation:
		
Minding The Machines		
The Who, What, and Where of Disruption

Alexandra Mateescu Researcher, Data & Society;MA 2013, Anthropology, University of Chicago
Madeleine Clare
Elish

Research Lead, Data & Society;MS 2010, Comparative Media Studies, Massachusetts Instituteof Technology; PhD 2018, Anthropology, Columbia University
This research was conducted with support from the Ethics
and Governance of AI Fund.

AI in Context

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“‘If it weren’t for the people,
the god-damn people’ said
Finnerty, ‘always getting
tangled up in the machinery.
If it weren’t for them, theworld would be an engineer’s
paradise.’”Kurt Vonnegut—Player Piano (1952)

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4

Executive Summary

In this report, we provide new evidence and frameworks for thinking about the near-term impacts of automated and AI technologies
on society. Our report demonstrates the necessity to integrate
rather than deploy AI technologies and to account for how AI
technologies reconfigure work practices rather than replace workers. We examine two American work environments in which AI
technologies often are imagined to have great disruptive potential:
agriculture and retail. Specifically, we analyze technologies being
used in farm management and grocery retail, contrasting theutopian (and dystopian) visions that tend to accompany AI with themundane realities of current automated technologies.
Our report demonstrates the ways in which automated and AI
technologies tend to mask the human labor that allows them to be
fully integrated into a social context while profoundly changing theconditions and quality of labor that is at stake. In our discussion, we
focus on the ways in which this dynamic shifts ingrained community
norms, interpersonal relationships, daily routines, and skill sets. We
find that although new skills are often required, they are usually
unacknowledged and uncompensated. We also find that adopting
new AI technologies produces economic risks that are not evenly
distributed among stakeholders, wherein more vulnerable or precarious communities are exposed to greater risks and harms thanthose who control the design or use of AI technologies.
In the agricultural context of family-owned farm management technologies, we argue for the necessity to frame theintroduction of new AI technologies as being integrated
rather than deployed, emphasizing that a technology must be

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5

Executive Summary

used by specific people within existing norms and practices.
This distinction is crucial if we want to understand not only how
AI technologies like precision agriculture are changing farming
practices but also how these changes are being adopted or
rejected and who is likely to benefit. In this context, our research points to the ways in which data-intensive technologies,
like crop management tools and “smart” tractors, require newwork routines and changes to physical infrastructure, likesecuring rural broadband internet and reorganizing the layout of barns or fields to facilitate optimal sensor readings.
In addition to these physical requirements, cultural shifts inthe business logics of family-owned farms are also required.
For instance, a physical field must now be conceptualized
as a complex dataset to be managed through other digital
information and digital tools. Dominant narratives around thefuture of agriculture focus on the benefits of providing farmers with data-driven insights that can help them make more
informed decisions. However, these narratives too quickly
gloss over the labor and social conditions that are required tointegrate AI technologies and which must be in place to fully
realize the promised benefits of AI.
In the retail context of grocery store technologies, we orient
our research toward how AI technologies reconfigure rather
than replace frontline jobs. We focus on the context and useof self-checkout machines, which are representative of thepiecemeal way in which retailers pick up and roll out newtechnologies. We find that retail experiments, like self-checkout or customer-operated scanners, tend to rely on humans
to smooth out technology’s rough edges. In other words, the“success” of technologies like self-checkout machines is inlarge part produced by the human effort necessary to maintain

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6

Executive Summary

the technologies, from guiding confused customers throughthe checkout process to fixing the machines when they breakdown to quite literally searching for customers aisle by aisle
when GPS systems fail. The impact of these retail technologies
has generally not been one of replacing human labor. Rather,
they enable employers to place greater pressures on frontline
workers to absorb the frontline risks and consequences of cost
cutting experiments. Much of the work that employees must
do on the ground to facilitate new systems is often invisible
and undervalued, even as popular perceptions of automation
frame these roles as increasingly obsolete.
The frames and contexts presented in this report point toward how
we might improve future development, assessment, and regulationof AI technologies. Focusing on the integration of AI and the laborthat is reconfigured brings into view structures of power that are
at stake. This can help us anticipate who will be empowered andwho will be left without a voice. Our findings suggest that public
understanding and debates would be strengthened if reporting on
AI focuses not just on a technology’s potential capacity or ideal use
case, but also on the labor of integration and the humans who areeither left in the lurch or relied upon to smooth out a technology’srough edges. Similarly, if designers keep these human operators andusers at the center of development, AI technologies can enhanceand complement existing skills and expertise, rather than foreclosethe discretionary power of frontline workers. Finally, our researchsuggests that policy makers and advocates should keep watch of
the unevenly distributed costs of experimenting and implementing
new technologies. Advocates may consider how the labor of AI
integration is often under-acknowledged, and the consequences
for how workers are compensated and supported. Given that thelabor of AI integration is often invisible or under-acknowledged,

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Executive Summary

it is all the more important to ensure that those who work with andalongside AI systems are adequately protected, supported, andcompensated. While “transitional” or “intermediate” phases are bydefinition temporary, they still are actualities that affect workers in
the present and need to be addressed.
AI technologies hold great promise in advancing society andaddressing existing problems. However, the potential benefits must
not obscure the potential perils of these technologies. These perils have nothing to do with “killer robots” or the coming of “robot
overlords.” Instead, they will be found in the everyday structuringpotentials of AI that will benefit some members of society but leave
many others behind.

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8Introduction
12
15

Human Infrastructures of AI
The Hyped Machine

Our concern in this paper iswith everyday experience and theperspectives of people whose
livelihoods are intertwined with
automated technologies.
It’s a familiar story these days: the era of Artificial Intelligence (AI)
has arrived, and AI will soon render human labor and decisionmaking obsolete. In this report, we tell a different story. We
contrast the utopian (and dystopian) visions that tend to accompany
AI with the mundane realities of current automated technologies.
There are many ways to talk about the consequences of automation
and AI; one might talk about regulatory or legal changes, industry
reconfigurations, macro- or microeconomic trends, or effects on
the environment. Our concern in this paper is with everyday experience and the perspectives of people whose livelihoods areintertwined with automated technologies. Our research has been
undertaken from an empirical perspective, investigating on-theground realities of automated technologies that are often spoken
about abstractly, without accounting for human experiences.
The technologies we examine range from cutting-edgemachine learning models to relatively simple statistical models andautomated kiosks. Each tends to be perceived as progressing us toward a better, more autonomous technological future in which thesetools will work more efficiently and effectively than humans. Our
report troubles this “near-certain” assumption and argues for the

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9Introduction

ways in which these technologies mask the human labor that allows
them to be fully integrated into a social context while profoundlychanging the conditions and quality of labor that is at stake.
In this report, we focus on two American work environments
within the broad industries of agri-food: family-owned farm management and corporate grocery retail. These contexts represent
different workplace dynamics and types of work, allowing us toanalyze AI adoption in contrasting contexts. We develop two frames
to demonstrate the necessity to integrate rather than deploy AI
technologies and to account for how AI technologies reconfigure
work practices rather than replace workers. These frames also serveto bring into focus the ways in which automated technologies have
had significant but under-examined consequences for everyday
life and formations of power.

Both of these frames are useful in unpacking particular implicationsof AI technologies. In this report, we contextualize our analysis
within the frame that we find draws out the most salient aspects
at stake. In our discussion of farm management technologies, wechoose the term integrate rather than the more common deploy

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10Introduction

to describe the processes through which technologies are introduced into the family-owned American farm context. Integrate
indicates that a technology must be used by specific people within
existing norms and practices. The word itself demands more
context: “of,” “into,” or “with what” is that technology being integrated? On the other hand, deploy, often used in the context ofmilitary action, connotes a kind of context-less dropping in. To
deploy a technology puts the rhetorical focus on the technology
itself rather than the human.
In the case of family-owned farms, this distinction is crucial if
we want to understand not only how AI technologies are changing
farming practices but also how these changes are being adopted
or rejected and who is likely to benefit. The reconfiguration of
established agricultural practices to facilitate data-intensive technologies, like crop management tools and “smart” tractors, requires
new work routines and changes to physical infrastructure, as wellas cultural shifts in the business logics of family-owned farms. The
labor of farm management has shifted away from the field and into
the office, creating the need for new kinds of digital fluency andrethinking farmland in terms of vast digital datasets. These new
ways of thinking can be in tension with existing practices andbeliefs. Moreover, while tech companies promise greater profitsand efficiency, small-scale farmers are often unable to compete
alongside larger, finance-backed conglomerates that can moreeasily bear the costs of experimentation.

While grocery technologies havepromised seamless integration
or decreased need for human labor,
this has often not been the case.
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Introduction

In our discussion of grocery retail technologies, we orient ouranalysis toward how AI technologies reconfigure rather thanreplace frontline jobs. Many discussions around the future of work
focus on whether or not certain jobs will be eliminated by AI and
automated technologies. However, there are fewer discussions
about how those technologies will disrupt the conditions of jobs
that continue to exist. By assuming a future absence, all the ways
in which work practices remain present are obscured. Reframing
automated and AI technologies as producing reconfigurations, andobfuscations of new labor practices allows us to focus on the qualityof the jobs that remain, as well as on those who bear the brunt oftechnological shortcomings. While grocery technologies havepromised seamless integration or decreased need for human labor,
this has often not been the case. The introduction of self-service
technologies for grocery retail workers has shifted customer service
roles to hovering human chaperones mediating between customer
and machine. Grocery workers are often called on to shift theirskillset in response to automation, performing new, yet unacknowledged, work in the background to enable technologies to function
seamlessly.
Our analysis is based on qualitative research methods, including content analysis of general audience media, industry-specificpublications, and academic literature reviews. We also conducted
15 interviews and approximately 10 hours of observations in thewinter and spring of 2018. This included attending and interviewing
visitors at the National Farm Machinery Show in Louisville, Kentucky,
and interviewing and observing unionized workers and managerial
staff at grocery retailers in Los Angeles, California. In contrast to
the universalizing tendencies of AI technologies, the arguments that
we make call attention to the specific contexts that determine thecontours, limits, and potential consequences of AI integration.

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12Introduction—Human Infrastructures of AI

HUMAN INFRASTRUCTURES OF AIThis research project began at the checkout station of a suburban
supermarket. One of the co-authors was using a self-checkoutmachine and had placed a bunch of bananas on the scanner. Immediately, the code for bananas was applied, and the machine’s
screen displayed the weight and price and then moved on, waiting
for the next item. The co-author thought to herself, “That’s a pretty
impressive machine vision algorithm on this nondescript machine!”
Impressed by the high-functioning technology, she continued toscan her items. There was, as there always seems to be, some sortof error that required a cashier to come over to the self-checkoutmachine and provide an override code to complete the order. The
co-author mentioned to her, “This thing is pretty smart! I didn’tknow it could sense what fruits and vegetables I had!” The attendant laughed and replied, “Oh, it can’t! I saw what you had and put
in the code myself from my machine.” She gestured toward herworkstation in front of the rows of self-checkout machines.1

The human worker is an example
of what we will call “human infrastructure,” the integral human
component of a socio-technicalsystem without which that systemcannot properly function.
1

Astra Taylor describes a similar incident, wherein an impressed customer wondered aloud how arestaurant app knew his order was ready and the cashier explained, “That was actually me.” Astra
Taylor, “The Automation Charade,” Logic no. 5 (2018), https://logicmag.io/05-the-automationcharade/.

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Introduction—Human Infrastructures of AI

This mistaken attribution of intelligence, in which a digitally savvy
customer assumed the effective functionality of a computer systemat the expense of the human worker who enabled its intelligent
performance, is typical of much of today’s AI, or “smart,” technologies. 2 The human worker is an example of what we will call “humaninfrastructure,” the integral human component of a socio-technicalsystem without which that system cannot properly function. We
define infrastructure broadly as the underlying foundation necessary for a system to function as designed and the notion of humaninfrastructures3 within automation and AI as a way to encapsulate
how many technologies can perform intelligently only if there are
human attendants creating and maintaining the conditions of theirintelligence. This might range from replacing batteries or cleaning
parts to updating outdated code or even modifying speaking patterns in order to be intelligible to a smart speaker by talking slowly
or masking an accent.
These kinds of human infrastructures are often imagined as astopgap measure and mere intermediate step on the way to better
AI and more autonomous machines. However, as increasingly more
research argues, the intelligence or autonomy of machines is made2

Hamid R. Ekbia and Bonnie A. Nardi articulate the dynamic of heteromation as a way to call attention
to the unique ways in which new forms of computational media require user engagement to produce
value but at the same time do not compensate or value that user engagement as enumerable work.
Hamid R. Ekbia and Bonnie A. Nardi, “Heteromation and its (Dis)contents: The Invisible Division ofLabor Between Humans and Machines,” First Monday, vol. 19, no. 6 (2014), https://firstmonday.org/
ojs/index.php/fm/article/view/5331.

3

For the last several decades, infrastructure has proven a useful analytic for theorizing how the socialworld generates and is generated by material form. Classically articulated by Star (1999), anthropological and Science Technology and Society (STS) studies of infrastructure have provided insight
into a wide range of social phenomena (Appel et al., 2015). It could be argued that infrastructure has
become an over-relied on analytic trope. Nonetheless, in this report infrastructure provides a usefulprovocation to conceptualize the human roles within automated and autonomous systems. Moreover,infrastructure is most commonly associated with physical structures such as roads or electric grids—not people. However, the reduction of human to object or interchangeable part is central to successful automation. The term human infrastructure encapsulates the tension between calling out
humans as infrastructure and the reduction of human to infrastructural object. Susan Leigh Star,“The Ethnography of Infrastructure,” American Behavioral Scientist vol. 43, no. 3 (1999): 377–91;
Brian Larkin, “The Politics and Poetics of Infrastructure,” Annual Review of Anthropology vol. 42(2013): 327–43; Hannah Appel, Anand Nikhil, and Akhil Gupta, “The Infrastructure Toolbox,” Cultural
Anthropology (September 2015), https://culanth.org/fieldsights/725-the-infrastructure-toolbox.

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Introduction—Human Infrastructures of AI

possible through the obfuscation of attendant human labor.4 This
argument is supported by studies on the history of automation inwhich time and again the promise of eliminating human labor has
given way to new forms of uncompensated, invisible, or undervalued labor. 5 The concept of labor is core to our inquiry; however,
the invocation of human infrastructure pushes us to consider not
only how humans labor alongside AI but also how an AI system’sfunctioning becomes possible only through the human labor thatanimates it.6 This then enables a fundamental reframing of how wethink about the intelligence in artificial intelligence.

We use the term human infrastructure to call attention to thepeople who are often renderedinvisible when AI is discussed orplanned for.
Our interest in articulating the concept is to draw attention to thecurrent realities in which AI technologies operate. We use the term
human infrastructure to call attention to the people who are often
rendered invisible when AI is discussed or planned for. When we4

See for instance Lily Irani, “The Cultural Work of Microwork,” New Media and Society (2013), https://doi.org/10.1177/1461444813511926; Lily Irani, “Justice for ‘Data Janitors,” Public Books (January2015), http://www.publicbooks.org/justice-for-data-janitors/; Mary L. Gray and Siddharth Suri, “The
Humans Working Behind the AI Curtain,” Harvard Business Review, January 9, 2018, https://hbr.
org/2017/01/the-humans-working-behind-the-ai-curtain, accessed October 25, 2018; M.C. Elish andTim Hwang, Praise the Machine! Punish the Human! The Contradictory History of Accountability in
Automated Aviation, Data & Society Research Institute, February 14, 2015, https://www.datasociety.
net/pubs/ia/Elish-Hwang_AccountabilityAutomatedAviation.pdf.

5

Ruth Schwartz Cowan, More Work for Mother: The Ironies of Household Technology from the OpenHearth to the Microwave (New York: Basic Books, 1985); Karl Marx, Capital, Volume One. trans. Ben
Fowkes (New York: Penguin, 1990); David Mindell, Our Robots, Ourselves: Robotics and the Mythsof Autonomy (New York: Penguin, 2015); David Noble, Forces of Production: A Social History ofIndustrial Production. (New York: Knopf, 1984).

6

Lucy Suchman, Human-Machine Reconfigurations: Plans and Situated Actions, 2nd edition (NewYork: Cambridge University Press, 2007).

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Introduction—The Hyped Machine

keep in mind that AI requires human infrastructures to work, weare better able to look for and see people who will most immediately be affected by AI and trace out the consequences.

THE HYPED MACHINE
Artificial intelligence is often described as some set of capabilities
exhibited by a computer that resembles intelligent behavior.
However, intelligence is not defined by a stable or specific set ofcharacteristics but is instead defined differently over time and in
relation to existing beliefs, attitudes, or technological capabilities.
Computer scientists Stuart Russell and Peter Norvig have written
about the varying emphasis on four possible goals for intelligencein the context of intelligent machines: “systems that think likehumans, systems that act like humans, systems that think rationally,systems that act rationally.”7
Moreover, when it comes to understanding the impact of AI,
the social perceptions of a technology’s capabilities are equallyimportant to technical definitions. Elsewhere we have observed that
non-expert understandings of AI are often shaped by marketing
rhetoric, which sometimes suggests capabilities that are not yet
technically possible. 8 For many developers of AI systems, thispotential fuzziness is “not a bug but a feature,” so to speak. The
public perception of AI is often leveraged to drum up excitement
or stand in for a range of automated technologies that haven’t
yet become fully actualized. The fluctuating understandings of
AI will not be universally resolved, and so it is necessary to
account for the consequences of AI as defined through bothtechnical definitions and social representations.
7

Stuart Jonathan Russell and Peter Norvig, Artificial Intelligence: A Modern Approach (New York:
Pearson, 2009).

8

M.C. Elish and danah boyd, “Situating Methods in the Magic of Big Data and AI,” Communications
Monographs 85 no 1, (October 2017): 57-80, https://doi.org/10.1080/03637751.2017.1375130.

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Introduction—The Hyped Machine

In the context of this paper, we define AI loosely in order to capture
its current technical and social valences. When we refer to AI, we
primarily mean to reference a computational system that performs,
partially or fully, a type of task that has been or might theoreticallybe performed by a human or a human team. This might involve
deep learning or other machine learning techniques, in whichalgorithms detect and predict patterns in vast datasets. This mightalso involve complex automated machines that perform a discreet
set of tasks relatively autonomously, involving sensors that are able
to receive input from an external environment and take actions
according to pre-programmed rules. In the following sections,
we explore automated and AI technologies as broadly existing on
the same continuum and focus less on technical specifications or
capacities and more on highlighting human behaviors and perceptions of technology in use.

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18

Farm Management
21
28

Shifting Roles
Unevenly Distributed Costs of Experimentation“The future of agriculture is in the hands of the machines,” proclaimed
Wired magazine in 2016.9 Although agricultural machines are far fromnew, agriculture is widely believed to be about to undergo a “revolution.”10 Major agribusiness corporations, from fertilizer vendors toequipment manufacturers, have pivoted to big data and smart technologies. Industry reports often paint a picture of farming as “on the vergeof turning into a high-tech industry,”11 oriented toward a future brightwith innovation. According to many industry observers, a watershed
moment occurred in 2013, when agribusiness giant Monsanto paid
nearly $1 billion to acquire a six-year-old start-up called The Climate
Corporation, which leverages vast datasets and finely tuned data models for predicting weather. The Climate Corporation’s original productwas insurance, but the acquisition signaled a broader commitment todigital, data-driven services.12 In addition to these major corporations,
smaller tech companies, many emerging from Silicon Valley, are competing to gain farmers as clients.13 Investment in “agtech,” short for
agricultural technologies, has been steadily growing, and in 2017, over
$1.5 billion was invested in agtech start-ups globally.14
9Matt Simon, “The Future of Humanity’s Food Supply Is in the Hands of AI,” Wired, May 25, 2016,
https://www.wired.com/2016/05/future-humanitys-food-supply-hands-ai/.

10

Anthony King, “Technology: The Future of Agriculture,” Nature, April 26, 2017, https://doi.org/10.1038/544S21a.

11

Thierry Laugerette and Franziska Stöckel, From Agriculture to AgTech: An Industry Transformed
Beyond Molecules and Chemicals, Monitor Deloitte, August 2016, https://www2.deloitte.com/content/dam/Deloitte/de/Documents/consumer-industrial-products/Deloitte-Tranformation-from-Agriculture-to-AgTech-2016.pdf.

12

Bruce Upbin, “Monsanto Buys Climate Corp for $930 Million,” Forbes, October 2, 2013, accessed September 27, 2018, https://www.forbes.com/sites/bruceupbin/2013/10/02/monsanto-buys-climatecorp-for-930-million/.

13

See, for example, conferences like the multi-city AgTech Summits, run by ForbesLive: https://www.
forbes.com/forbes-live/event/agtech-summit-2018/.

14

Arama Kukutai and Spencer Maughan, “How the AgTech Investment Boom Will Create a Wave ofAgriculture Unicorns,” Forbes, January 16, 2018, https://www.forbes.com/sites/outofasia/2018/01/16/
how-the-agtech-investment-boom-will-create-a-wave-of-agriculture-unicorns/.

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Farm Management

Precision agriculture has become one of the fastest growing applications of AI in agriculture.15 It encompasses an approach tofarming rather than a specific product and relies on the collection,interpretation, and analysis of diverse forms of digital data. For
instance, some of the simplest precision agriculture technologies
include GPS-equipped combine harvesters that produce georeferenced data.16 Tools that can track harvest yield allow for the analysis
of field variability, like differences in soil makeup, water, or fungus.
Variable rate technologies provide predictions and analysis sothat farmers may customize the amount of input, such as fertilizer
or pesticide, on any given portion of a field.17
For farmers, precision agriculture promises the tools to help
them optimize resources and minimize costs effectively, and by
doing so increase yield and profits. Existing alongside these promises, the commercial drive to augment farming practices with AI
often invokes rising global concerns about food security, climatechange, and the capacity for current practices to effectively feed
increasing populations.18 Agtech innovations are invoked not only
as efficient but also imperative in the context of saving the earth
and its human and animal populations.
While these narratives drive much of the rhetoric around theagtech “revolution,” local decisions around farmer adoption arecomplicated by more imminent concerns and limitations around15

Kelsey Nowakowski, “Farming: There’s an App for That,” National Geographic, June 5, 2018, https://www.nationalgeographic.com/environment/future-of-food/food-future-precision-agriculture/.

16

Combine harvesters, more often called simply combines, are automated machines that combine threeharvesting functions (reaping, threshing, and winnowing) and have been iteratively developed andused since the mid-19th century.

17

Remi Scmaltz, “What Is Precision Agriculture and How Is Technology Enabling It?” AgFunderNews,
April 24, 2017, https://agfundernews.com/what-is-precision-agriculture.html/.

18

See, for example, Lyndsey Gilpin, “How Big Data Is Going to Help Feed Nine Billion People by 2050,”
TechRepublic, accessed October 11, 2018, https://www.techrepublic.com/article/how-big-data-isgoing-to-help-feed-9-billion-people-by-2050/. Scholar Michael Carolan has characterized discourses
around big data and precision agriculture as both “anticipatory” and “moralizing,” specifically in thecontext of climate change. Michael Carolan, “Publicizing Food: Big Data, Precision Agriculture,
and Co-Experimental Techniques of Addition,” Sociologia Ruralis vol. 57, no. 2 (April 1, 2017), 135–54,
https://doi.org/10.1111/soru.12120.

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Farm Management

integrating new tools into existing practices, as well as weighing therisks that experimenting with new technologies entails. This section
argues that there is a disconnect between the dominant vision of
AI-driven agriculture – which assumes a model of deployment – andthe actual labor, resources, and reconfigurations needed to facilitate
integration.

This section argues that there isa disconnect between the dominantvision of AI-driven agriculture –which assumes a model of
deployment – and the actual labor,
resources, and reconfigurations
needed to facilitate integration.
Our field interviews draw from only one small subset of the larger
ecosystem: family-owned commodity-crop farmers in mid-Atlantic
and mid-Western United States. These farms are where the majority
of US commodity crops – that is, crops sold to the commodities
market, such as soybeans and corn – are grown. Because the kinds oftechnologies currently in use in this context vary widely, we did not
focus on a single type of tool but on data-driven technologies usedin precision agriculture in this context. These are tools that directly
impact farm management, as opposed to other categories, suchas robotics, that automate manual farm labor like crop harvesting
and which are largely in prototype phasing now.19

19

Emma Cosgrove, “Harvesting Robotics Market to Reach $5.5bn from Early Adopters Alone — Report,”
AgFunderNews, January 10a, 2018, https://agfundernews.com/harvesting-robotics-market-earlyadopters-report.html/.

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Farm Management—
Shifting Roles: The Labor of Integration

SHIFTING ROLES: THE LABOR OF INTEGRATION
The adoption of precision farming technologies has not been as
rapid as many proponents had hoped, although use is slowly rising.
According to a 2014 American Farm Bureau Federation survey of US
farmers, more than half of respondents reported plans to increase
their investment in data-driven technologies within the following
two years. 20 Yet despite the touted benefits, existing researchshows that adoption of data-driven technologies has been uneven.21
This section briefly explores some of the labor involved in integrating precision agriculture technologies on the ground. At thelevel of farm management decisions, this work can include shifts
in ingrained community norms, interpersonal relationships, daily
routines, and skill sets. Additionally, there is the labor of reconfiguring physical infrastructure to render farmland amenable to thedata collection that makes AI possible. This is not always a smoothprocess, and the extent to which farmers can benefit from precision
agriculture tools often depends on the conditions and resources
already available.
Agtech companies generally market themselves as a solution
to a problem farmers have long faced: how to collect better, moreaccurate data about farm conditions and how to use it to makeeffective decisions that improve farm outcomes. For instance,
OnFarm, a California-based start-up, claims on its website to havethe “largest network of connected devices and data in agriculture”
and promises to automate the collection of data to “fit your specific20

AFBF, “American Farm Bureau Survey Shows Big Data Use Increasing, Big Questions Remain,” American Farm Bureau Federation Newsroom, October 14, 2014, http://www.fb.org/newsroom/american-farm-bureau-survey-shows-big-data-use-increasing-big-questions-rem.

21

David Schimmelpfennig and Robert Ebel, “On the Doorsteps of the Information Age: Recent Adoption of Precision Agriculture,” US Department of Agriculture Economic Research Service, August
2011, https://www.ers.usda.gov/webdocs/publications/44573/5732_eib80_1_.pdf?v=0. See also
the annual CropLife/Purdue Precision Dealer Survey at http://agribusiness.purdue.edu/precision-ag-survey.

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Farm Management—
Shifting Roles: The Labor of Integrationneeds without worrying about managing complex data.”22 Importantly, tools like sensor-equipped farm machinery promise not only
to make higher- quality data available to farmers but also to provide
a broader scale of data. Often, these tools aggregate multiple typesof data to provide analyses and recommendations.

Many of the marketing promises of these technologies are based on
optimistic assumptions or projections of farmers’ available resources
and physical infrastructures. Nonetheless, on the most basic level,
broadband internet may be unavailable to many farmers. As Ruth, afarmer from North Dakota on a family-owned farm said, “You don’t
really have internet connectivity in the farms and a lot of places
there’s a lot of hurdles.” During a question- and -answer session
at a product booth at the 2018 National Farm Machinery Show inLouisville, she asked an analyst answering questions about precision
agriculture ecosystems whether “the rural internet can support all
this tech.” His response, similar to the predictions of other analysts,
was that while the answer is currently no, demand for more accesswill grow supply and capacity. However, these future supply andcapacity expectations are far from certain. US Department of Agriculture data from 2017 reports that farmers’ connectivity has grownin the last decade, with 71% reporting internet access via computer
but showing smaller percentages of access across other methods,
22

http://www.onfarm.com/data-from-anywhere.

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23

Farm Management—
Shifting Roles: The Labor of Integrationsuch as mobile. 23 Broadband access continues to lag significantly
behind in rural area as compared to the rest of the US.24 In addition tointernet connectivity, new physical resources may be required to use
precision agriculture techniques. This may involve reorganizing fields
to fit new machines or reorganizing the layout of barns or silos tofacilitate optimal sensor readings. To become legible – and sometimes
quite literally malleable – to digital sensors and techniques, physicalreconfigurations are often required.

To become legible – and sometimes
quite literally malleable – to digitalsensors and techniques, physicalreconfigurations are often required.
The abundance of data itself also presents new challenges. Industry
publications in recent years have reported on the problem of the
farmer “awash in data” collected by farm machinery, with farmers
often lacking the training and resources to integrate new data-driven
systems with their farm equipment.25 In 2016, the Wall Street Journal
reported a decline in venture capital funding for precision agriculture technology, with many companies closing down or redirecting
their businesses to farm robotics and biotechnology.26 An importantfactor cited in the article was the difficulty farmers have faced ininterpreting the vast amounts of data these systems aggregate, as
well as discerning their correlation with boosts in farm productivity.
23

Farm Computer Usage and Ownership, USDA National Agricultural Statistics Service, August 2017,
http://usda.mannlib.cornell.edu/usda/current/FarmComp/FarmComp-08-18-2017_correction.pdf.

242018 Broadband Deployment Report, Federal Communications Commission, February 5, 2018, https://www.fcc.gov/reports-research/reports/broadband-progress-reports/2018-broadband-deployment-report.

25

Hembree Brandon, “Today’s Farms Awash in Data: But What to Do with It?” Delta Farm Press, December
2, 2014, https://www.deltafarmpress.com/agriculture-technology/today-s-farms-awash-data-what-do-it.

26

Eliot Brown, “Why Big Data Hasn’t Yet Made a Dent on Farms,” Wall Street Journal, sec. Business, May
15, 2017, https://www.wsj.com/articles/why-big-data-hasnt-yet-made-a-dent-on-farms-1494813720.

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Farm Management—
Shifting Roles: The Labor of Integration

In the field management context, tools for data collection andanalysis do not save labor as much as they increase efficiency peracre. In principle, tools like variable-rate technology reduce input
costs and increase crop yields. But in practice, a wide range of variables affect the extent to which farmers see these benefits, often
requiring more labor on the part of the farmer to experiment withand interpret changes in farm yields. The factors contributing toa given yield may be difficult to discern, given that farms are notcontrolled environments and external variables like weather patterns or pests can obscure cause and effect. 27 Even the order andcombinations in which farmers adopt new technologies can be asignificant factor in the efficacy of precision agriculture tools. 28

This contradiction underscoresthat adoption is driven not only byavailability but also by the capacityand desire to use such technologies.
Some industry analyses interpret these complications as temporary
hurdles. A 2016 industry report from consulting firm Accenture
paints a picture of the present state of precision agriculture in theform of the “confused farmer,” who is “overwhelmed by data.”29
He is represented by an illustration of a farmer scratching his headas his smartphone buzzes with myriad data inputs. His plight is27David Schimmelpfennig, Farm Profits and Adoption of Precision Agriculture, US Department ofAgriculture Economic Research Service, October 2016, https://www.ers.usda.gov/webdocs/
publications/80326/err-217.pdf?v=0; Joseph Byrum, “The Challenges for Artificial Intelligence inAgriculture,” AGFunderNews, February 20, 2017, https://agfundernews.com/the-challenges-forartificial-intelligence-in-agriculture.html/.

28

David Schimmelpfennig, Cost Savings from Precision Agriculture Technologies on U.S. Corn Farms,
US Department of Agriculture Economic Research Service, May 2, 2016, https://www.ers.usda.gov/
amber-waves/2016/may/cost-savings-from-precision-agriculture-technologies-on-us-corn-farms/.

29

Digital Agriculture: Improving Profitability, Accenture, https://www.accenture.com/nz-en/insightaccenture-digital-agriculture-solutions.

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Farm Management—
Shifting Roles: The Labor of Integration

presented as an awkward transitional phase on the path to a brighter
future of data insight through fully integrated technologies. 30 In
reality, however, the issues described above, in addition to otheremergent concerns around data privacy, often lead farmers to opt
out of using available technologies.31
According to an industry analyst we spoke to, official numbers
in reported uses of precision agriculture tools are likely misleading;most John Deere equipment, for instance, comes with pre-installedsoftware, but there’s no indication of whether it is actually being
used. Other reports similarly suggest that some farmers may capture data using these tools but leave it on the field machine and donothing with it.32 This contradiction underscores that adoption isdriven not only by availability but also by the capacity and desire touse such technologies.
Unsurprisingly, with new technologies comes the need for new
skills and new forms of expertise. Agtech technologies often requirea reconceptualization of the work and business of farming, which
we discuss in further detail below. This is in part because precision
agriculture technologies not only automate but also “informate,” toborrow a term from Shoshana Zuboff, who wrote about this distinction in the context of information technologies in the workplace in the
1980s.33 Informating an environment describes the work that is done
to render actions and objects as digital information (data) that canbe acted upon. In this case, for instance, a field becomes a complex
dataset to be managed through other digital information and digital
tools. Not all farmers have experience in working with digital data in
30

Digital Appetite in Rural America: Innovative Agricultural Technologies and the Potential for USDA,
Accenture, January 2017, https://www.accenture.com/t20170420T220015__w__/us-en/_acnmedia/PDF-50/Accenture-USDA-Agriculture-PoV-v08.pdf.

31

Karen Levy, Solon Barocas, and Alexandra Mateescu, “Reap What You Sow? The Privacy of Agricultural
Data,” Northeast Privacy Scholars Workshop, October 20, 2018, Fordham University School of Law,
New York, New York.

32

Jennifer Alsever, “Is There an AgTech Bubble?” Fortune, July 25, 2016, http://fortune.com/2016/07/25/agriculture-farming-tech-startup-bubble.

33

Shoshana Zuboff, In the Age of the Smart Machine: The Future of Work and Power (New York: Basic
Books, 1988).

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Farm Management—
Shifting Roles: The Labor of Integration

this way. At the same time, farmers often need to adjust and calibrate
tools or sensors to fit local contexts and needs, requiring specializedskills in agtech software and hardware.34 Finding ways to develop
the necessary skills or experience can be a challenge.
However, we found a marked difference between farmers’
attitudes toward these changes, often falling along generationallines. Matt, a man in his early 20s who works on his uncle’s corn andsoy farm in North Dakota, was typical of what is often describedas “the new generation” of farmers. This new generation has grown
up with iPhones and iPads and is comfortable with using digitaltechnologies in every aspect of life. Matt is happy to embracesomething like an iPad or screen in a tractor, but his uncle finds this“mentally fatiguing.”
Finally, adoption of precision agriculture tools also impacts
the experience of living and working as a farmer. The model of what
farming looks like on the ground is slowly changing, raising questions
about how those changes may come into tension with existing
self-perceived identities. One farmer from Indiana explained, “I wish
I could go out in the fields more, but it’s more valuable for me to bein the office in front my computer.” This sentiment was widespreadamong the farmers with whom we spoke. Industry publications have
similarly noted that the “plethora of sensors, software, and embedded interface systems” may grate against some farmers’ preferences
for “hands-on” involvement in the field, as well as their already established methods and routines.35 Unlike many professions, farming
34

Most prominently, this issue has emerged in the “right to repair” movement that advocates for farmers’
rights to modify farm equipment software themselves, as well as to do their own repairs rather thandepend on manufacturer technicians. Companies like John Deere have used copyright law and license
agreements to try to restrict these practices. In response, some farmers have formed DIY “tractor
hacking” communities, and a black market for pirated equipment software has emerged. Kyle Wiens
and Elizabeth Chamberlain, “John Deere Just Swindled Farmers Out of Their Right to Repair,” Wired,
September 19, 2018, https://www.wired.com/story/john-deere-farmers-right-to-repair/; John Naughton, “Why American Farmers Are Hacking Their Own Tractors,” The Guardian, March 26, 2017, https://www.theguardian.com/commentisfree/2017/mar/26/why-american-farmers-hacking-own-tractors.
For an analysis of farm hacking communities, see Michael Carolan, “‘Smart’ Farming Techniques as
Political Ontology: Access, Sovereignty and the Performance of Neoliberal and Not-So-Neoliberal
Worlds,” Sociologia Ruralis vol. 58, no. 4 (October 1, 2018), 745–64, https://doi.org/10.1111/soru.12202.

35

Sensors Staff, “AgTech Sounds Good for Some, Not All,” Sensors Online, August 15, 2016, https://www.sensorsmag.com/components/agtech-sounds-good-for-some-not-all.

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Farm Management—
Shifting Roles: The Labor of Integration

is an industry people are typically born into, as part of a wider farming community dominated by family-owned businesses.36 As such,the industry depends on establishing continuity between generations. 37 For instance, Matt, the young tech-savvy farmer above,
expressed disdain for the growing popularity of “urban farming”—
often touted by proponents as a key to building food sustainability.38
He explained, “You can’t just start up a farm. You need to have been
in it.... It’s hard to describe if you’re not in it. It’s like a lifestyle, nota business.”
As this section has shown, it is not enough to bring new technologies into the hands of farmers. Dominant narratives aroundthe future of precision agriculture focus on the potential benefitsof providing farmers with data-driven insights that can help themmake more informed decisions. However, including all of the activities necessary for integration reveals the labor that must happen
around AI technologies in order to realize their promised benefits.

36

Within our research, every farmer we spoke with – from those in their late teens to those in their 50s
and 60s – had been a member of Future Farmers of America (FFA). The organization, founded in the1928, was and continues to be a way to nurture community and pride around farming and to developinterest and knowledge in a range of agricultural careers. Currently, there are over 650,000 members in more than 8,500 chapters throughout 50 states. “FFA History: National FFA Organization,”
Future Farmers of America, accessed September 24, 2018, https://www.ffa.org/about/what-is-ffa/
ffa-history.

37

On cultural mythologies surrounding the American farmer’s identity and history, see Richard
Hofstadter, “The Myth of the Happy Yeoman,” American Heritage, vol. 7, no. 3 (April 1956), https://www.americanheritage.com/content/myth-happy-yeoman.

38

Amy Crawford, “Big Data Suggests Big Potential for Urban Farming.” Wired, February 20, 2018,https://www.wired.com/story/big-data-suggests-big-potential-for-urban-farming/.

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28

Farm Management—
Unevenly Distributed Costs of Experimentation:
Don’t Be the Sea LionUNEVENLY DISTRIBUTED COSTS OF EXPERIMENTATION:
DON’T BE THE SEA LION
Farmers take on serious risks when adopting any new form oftechnology. As the previous section discussed, the promised benefitsof precision agriculture technologies are not guaranteed but require
the marshaling of labor, resources, and time to adequately integrate.
This section examines the ways that precision agriculture adoption
places much of the risk of adopting new technologies onto farmers.
The business model and organizational structure of farms vary
widely. There are important differences between commodity or fieldcrop producers and fruit and vegetable producers, as well as between
family-owned farms and corporate-owned farms.39 The economics ofa farm likewise varies widely between small farms and large industrial
farms. These differences are consequential for farming labor practices, as well as the potential returns from investments in technologies;
as scholars have observed, the adoption of new farming technologies
requires resource-rich farms, enforcing a cycle in which inequities
among farmers are reproduced and entrenched.40 Smaller farms willsee diminishing returns when investing in costly technologies.41 Large
industry operations, by contrast, are better poised to benefit from
economies of scale, in which even a 0.25% increase in yield productivity can be a huge sum, from purchasing farm inputs (such as fertilizer)
in bulk to possessing the capital to invest in new technologies.42
39

Robert Hoppe, US Farms Large and Small, US Department of Agriculture Economic Research Center,
January 13, 2015, https://www.usda.gov/media/blog/2015/01/13/us-farms-large-and-small; Lydia
DePillis, “Farms Are Gigantic Now. Even the ‘Family Owned’ Ones,” The Washington Post, August 11,
2013, https://www.washingtonpost.com/news/wonk/wp/2013/08/11/farms-are-gigantic-now-eventhe-family-owned-ones/?utm_term=.c9d2718597ef.

40

To start, see Lindsay Ferris and Zara Rahman, Responsible Data in Agriculture, Global Open Data for
Agriculture & Nutrition (Godan), September 15, 2016, https://www.godan.info/documents/responsible-data-agriculture.

41

Michael Hickins, “For Small Farmers, Big Data Adds Modern Problems to Ancient Ones,” Wall Street
Journal, February 26, 2014, https://blogs.wsj.com/cio/2014/02/25/for-small-farmers-big-data-addsmodern-problems-to-ancient-ones/.

42

Roni Neff, Introduction to the US Food System: Public Health, Environment, and Equity, (Hoboken,
NJ: John Wiley & Sons, 2014), 271.

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Farm Management—
Unevenly Distributed Costs of Experimentation:
Don’t Be the Sea Lion

Secondly, farms are not self-enclosed systems: agriculturaloperations are facilitated by an array of stakeholders and business
relationships. The traditional vision of the US farmer is mismatchedwith the current reality. For instance, the prototypical farmer issomeone who owns a farm, works the land, and sells his (often the
traditional image is of a man) crop or livestock for profit. However,these characteristics are no longer always contained within thesame person. Producers, those who manage a farm and sell the
goods, may call themselves or be referred to as farmers. Yet togrow a crop, a farmer’s resources – including land to farm, seeds togrow, and expertise and technology to manage and harvest, as wellas labor – are likely provided and controlled by different actors.
For instance, many farmers do not own the land they farm, instead
leasing it from a landlord. If they do own some land, they may lease
additional land. According to 2012 US census data, 39% of farmland
acres were rented out from landowners, 87% of whom were notfarm operators.43
Financial institutions are an important part of this ecosystem.
In farming, as in other industries, analytics and AI technologies
are facilitating the growth of financialization, which economists
describe as the trend toward increasing the size, role, and influence of the financial industry, including financial institutions,
instruments, and markets. 44 One example of this is the role ofinsurance companies, which are relying on increasingly large
amounts of data to calculate the risks taken on by farmers. 45
432012 Census of Agriculture Highlights: Farmland Ownership and Tenure, USDA National Agricultural
Statistics Service, September 2015, 1, https://www.nass.usda.gov/Publications/Highlights/2015/
TOTAL_Highlights.pdf.

44

On financialization of agriculture, see James W. Williams, “Feeding Finance: A Critical Account of the
Shifting Relationships Between Finance, Food and Farming,” Economy and Society vol. 43, no. 3 (July 3,2014), 401–31, https://doi.org/10.1080/03085147.2014.892797; Sarah Ruth Sippel, Nicolette Larder, andGeoffrey Lawrence, “Grounding the Financialization of Farmland: Perspectives on Financial Actors as
New Land Owners in Rural Australia,” Agriculture and Human Values vol. 34, no. 2 (June 1, 2017), 251–65,
https://doi.org/10.1007/s10460-016-9707-2. See also Mike Konczal and Nell Abernathy, “Defining Financialization,” Roosevelt Institute, July 27, 2015, http://rooseveltinstitute.org/defining-financialization/.

45

Ari Libarikian et al., “Harnessing the Potential of Data in Insurance,” McKinsey, May 2017, https://www.
mckinsey.com/industries/financial-services/our-insights/harnessing-the-potential-of-data-in-insurance.

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30

Farm Management—
Unevenly Distributed Costs of Experimentation:
Don’t Be the Sea Lion

The abundance of agricultural data has also raised concerns about
the possibility of market manipulation. 46 Furthermore, in everyday
practice, the turn to automation in the market threatens more
traditional farming experiences. Following a trade show presentation on markets, one farmer in his 60s explained, “In my day, you
had a relationship with the trader in Chicago ... but now it’s justsome mathematical algorithm that’s driving everything. It’s losta personality, and it’s very isolated. There’s no camaraderie. Not
fun to live through it.”Financialization has also contributed to farmers’ experiencesof financial precarity. A landowner we interviewed who no longer
farms confided that she just didn’t understand how farmers weremanaging these days: “I know what I pay at the grocery store forthings, I know what they make, I know what their expenses are, andI just know they aren’t making enough.” Another soybean farmer
explained, “Nothing drives a farmer nuts like having prices go crazyout of supply and demand. There’s an emotional toll to have tosuffer those shocks.” Many people we spoke with gave the sensethat everyone was taking a piece, and farmers were being left withscraps. One grain producer relayed a story he found emblematic
about how a farmer, feeling uncertain about market prices, sold toa buyer “low,” then watched as his buyer turned around and “sold it
to another place, right after, for a higher price. It’s all about those
few cents mounding up.”At the 2018 National Farm Machinery Show in Louisville,
Kentucky, an analyst advised a packed room of farmers, producers,
and landowners, “The funds are the sharks in the ocean, and you
shouldn’t be the sea lions they feed on.” He continued, “Be the fishes
feeding around them. Work with the shark. Work with the shark.”
The funds the analyst was referencing were people who trade46

Katherine Noyes, “Cropping Up on Every Farm: Big Data Technology,” Fortune, May 30, 2014, http://
fortune.com/2014/05/30/cropping-up-on-every-farm-big-data-technology/; Neal Rasmussen, “FromPrecision Agriculture to Markey Manipulation: A New Frontier in the Legal Community,” Minnesota
Journal of Law, Science and Technology vol. 17, no. 1 (February 2016), https://scholarship.law.umn.
edu/cgi/viewcontent.cgi?article=1008&context=mjlst.

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31

Farm Management—
Unevenly Distributed Costs of Experimentation:
Don’t Be the Sea Lioncommodities and futures in financial markets. Visiting the show
and talking with attendees and exhibitioners, there was apronounced sense that farming was not only being “reduced” to abusiness but also being held hostage by the “boys on the coasts who
like to play in the markets,” as one audience member later put it.47
Similar tensions are also emerging between the businesslogics put forward in tech companies’ marketing rhetoric andestablished farm business practices. Some legacy farm- equipment
companies, which establish their reputations locally at dealerships
and trade shows, rely on marketing rhetoric that speaks to a senseof calling and tradition. 48 Newer, typically Silicon Valley–based,
technology companies appeal to different logics that speak more to
the economic strains faced by farmers, encouraging them to manage
their farms like any other data-driven business. These tools, andothers being developed by smaller start-ups, promise “smart”
solutions through digital data collection. Accompanying this goalis the normative messaging that pervades precision agriculture
tech marketing, which tends to devalue certain forms of expert
knowledge, underscoring that “good farmers do not follow theirgut, they follow data.”49 At a farming trade show session on technology, a representative from a major agricultural tech company
told the audience that farmers need to change the way [they] farm,”
starting with ensuring accurate data collection, in order to “makeprofitable decisions.” The representative warned the audience that“the longer you wait to work with [data-driven tech], the harder itwill be.” But as one crop consultant we met explained, many farmers
weren’t equipped to make this transition:
47

On fund manipulation of crop commodity markets, see James W. Williams and Nikolai M Cook,“Econometrics as Evidence? Examining the ‘Causal’ Connections Between Financial Speculation andCommodities Prices,” Social Studies of Science vol. 46, no. 5 (October 1, 2016), 701–24, https://doi.
org/10.1177/0306312716658980.

48

As one equipment dealer’s slogan declares on its website, “Strong Heritage, Strong People, Stronger
Future!” Helle Farm Equipment, http://www.hellefarmequipment.com/.

49

Michael Carolan, “Publicizing Food: Big Data, Precision Agriculture, and Co-Experimental Techniquesof Addition,” Sociologia Ruralis vol. 57, no. 2 (April 1, 2017), 135–54, https://doi.org/10.1111/soru.12120.

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32

Farm Management—
Unevenly Distributed Costs of Experimentation:
Don’t Be the Sea Lion

[Farmers] haven’t managed these individual fields like anindividual production factory, like a normal business would.
And so they’re not managing a P&L [profits and losses] per
production factory. It’s hard for them without really goodrecords and good tools to improve the efficiency and theeffectiveness of their farm. They don’t know the return on
investment of this field versus that field.
Many AI technology companies have entered the industry withpromises to cut out the middle man and help producers get themost value out of their crops, “to capitalize on the profit that’s
being driven from the top down,” in the words of one “intelligent
storage” start-up. However, within the context of wider trends inthe industry described above, the farmers we spoke to expresseda sense of cynicism toward new intermediaries, especially if they
were new to the agriculture industry and had not gained the trustof farmers. One representative from an agrochemical company
described this disconnect: “Quite often the Silicon Valley company
is trying to apply their capability to agriculture, but they don’t
understand enough about agriculture to know the relevance of what
they’re doing. Quite often it’s apparent they don’t know corn plantsfrom oak trees.” And while new tech intermediaries promise greater
profits, they also entail greater risks and the ability to experimentis limited by practical – and often temporal – constraints. For
example, the timescales and seasonal cycles of farming impose aparticular rhythm that can feel counter to speed of the tech world.
As one market research analyst we spoke with emphasized:
Farmers only have one kind of chance to test technology ayear and the weather changes a lot year by year. ... That reallyslows down technology adoption. Unless you have somethingthat is really, extremely impactful in terms of efficiencies,

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Farm Management—
Unevenly Distributed Costs of Experimentation:
Don’t Be the Sea Lion

farmers will not see that because they’re testing very little
once a year and there’s too many variables that are playing
together.
Although the large-scale, long-term impacts of data-driven and AI
technologies on agricultural economics are unknown, this section
has laid out some of the ways in which adoption of data-driven
technologies comes with certain risks. For some farmers, this newlandscape has prompted anxieties around the future, including thepotentially exploitative role played by new technology intermediaries.50 Importantly, narratives about precision agriculture tools fromvendors, market analysts, and proponents of big data tend to elide
the resultant frictions on the ground.

Quite often the Silicon Valley
company is trying to apply theircapability to agriculture, but they
don’t understand enough about
agriculture to know the relevanceof what they’re doing. Quite often
it’s apparent they don’t know
corn plants from oak trees.”

50

Karen Levy, Solon Barocas, and Alexandra Mateescu, “Reap What You Sow? The Privacy of Agricultural Data,” Northeast Privacy Scholars Workshop, October 20, 2018, Fordham University School ofLaw, New York, New York.

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34

Grocery Retail38
44

Shifting Roles
Unevenly Distributed Costs of Experimentation

The grand opening of the Amazon Go store in January 2018 elicitedconsiderable media coverage, which proclaimed it the death knellof frontline retail jobs. Amazon Go is a grocery store in Seattle
operated by Amazon using a combination of technologies that use
computer vision and sensors throughout the store to detect when
shoppers pick up specific store items and then charge them toshoppers’ accounts as they exit the store. As a result, the store has
no cash registers, cashiers, or other frontline workers, although asmall staff continues to stock shelves and prepare baked goods
behind the scenes. 51
In response to the apparent obviation of human staff, an article
in VentureBeat began with the proclamation that the “illusion ofjob safety for the 3.5 million cashiers in America was shattered”
now that the Amazon Go concept has been proven operational.52
The New York Times likewise identified Amazon Go as a catalyst
to a “global race to automate stores,”53 while Fortune speculated
that nearly half of all retail jobs would now be lost to automation. 54
Following the Seattle store’s opening, Amazon announced that it
would be opening a second store in Seattle, as well as stores in

51

Natt Garun, “Amazon Just Launched a Cashier-Free Convenience Store,” The Verge, December 5,2016,https://www.theverge.com/2016/12/5/13842592/amazon-go-new-cashier-less-conveniencestore.

52

German Chastel, “The Amazon Go Effect: How Bots Fit into the Future Workforce,” VentureBeat,
February 1, 2018, https://venturebeat.com/2018/02/01/the-amazon-go-effect-how-bots-fit-into-thefuture-workforce/.

53

Nick Wingfield, Paul Mozur, and Michael Corkery, “Retailers Race Against Amazon to Automate
Stores,” the New York Times, August 7, 2018, sec. Technology, https://www.nytimes.com/2018/04/01/technology/retailer-stores-automation-amazon.html.

54David Morris, “Retail Automation: Nearly Half of All Retail Jobs Could Be Lost,” May 21, 2017, http://
fortune.com/2017/05/21/automation-retail-job-losses/.

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35Grocery Retail

Chicago, San Francisco, and New York, although the company has
not made clear its long-term plans for Amazon Go’s expansion.55
Although Amazon Go is currently more experimental project
than established retail competitor, its model has been described as
the future toward which legacy grocery retailers will aspire in the
coming years.56 Supermarket, grocery, and drugstore retailers are
one of the most scrutinized retail ecosystems. For these businesses,
the push to deploy artificial intelligence and other technologies is inpart motivated by fears that legacy grocery retailers are being undercut by “category killers” – chain retailers that provide a wide array of
products at heavily discounted prices – like Amazon and Walmart,whether through e-commerce or cutting-edge technologies
deployed within stores or throughout the supply chain.57 Currently,
companies are developing AI applications to help retailers perform
customer service functions, personalize marketing, identify new
trends, predict demand of goods, and streamline logistics andjust-in-time manufacturing.58
Operating on extremely thin profit margins, supermarket
retail has throughout its history relied on technology to cut laborcosts, target consumers, and influence in-store shopper behavior
through architecture and the spatial organization of products. As
media scholar Joseph Turow states, in the last 20 years, supermarket
chains have been inundated with the message from technology
companies that they “will succeed only if they figure out how to
55

Chaim Gartenberg, “Amazon Is Opening a Second Cashier-Less Go Store in Seattle This Fall,” The
Verge, July 3, 2018, https://www.theverge.com/2018/7/3/17532188/amazon-go-seattle-new-store-location-fall-2018; Nat Levy, “Amazon Go Is Coming to New York City, 3rd Market Outside Seattle for
Checkout-Less Grocery Concept,” GeekWire, September 7, 2018, https://www.geekwire.com/2018/
amazon-go-coming-new-york-city-3rd-market-outside-seattle-checkout-less-grocery-concept/.

56

For instance, futurist Martin Ford describes the retail spaces of the future as “scaled-up vending
machines” that would consist of an “automated warehouse with an attached showroom where
customers could examine product samples and place orders.” Martin Ford, Rise of the Robots: Technology and the Threat of a Jobless Future, reprint edition (New York: Basic Books, 2016), 20.

57

Rajiv Lal and Jose B. Alvarez, “Retailing Revolution: Category Killers on the Brink,” HBS Working Knowledge, October 10, 2011, http://hbswk.hbs.edu/item/retailing-revolution-category-killers-on-the-brink.

58

Surviving the Retail Apocalypse: The Technologies and Trends That Can Help Brick-And-Mortar Thrive
Again, CB Insights Research, April 24, 2018, /research/retail-apocalypse-survival-technology-trends/.

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36Grocery Retailtrace, quantify, profile, and discriminate among shoppers as neverbefore.”59 Consumers are increasingly positioned as valuable
not only because of spending power but also as sources to minefor valuable data. By contrast, the future envisioned for frontline
retail workers is either one of obsolescence through automation or arechanneling of the workforce into higher-skilled positions. The
argument goes that these workers will be repurposed for more
fulfilling customer-facing roles, because tiring and repetitive
manual tasks will be performed by AI-powered systems androbots in the background. Less consideration is given to how existing
low-wage service jobs may be reconfigured in other, potentiallydetrimental, ways.

The self-checkout machine is more
representative of the piecemeal
way in which retailers pick up andexperiment with new technologies,
relying on humans to smooth out
the rough edges.
Technology has shaped and reshaped retail spaces for decades.
This section takes a previous technological innovation – the selfcheckout machine – as an avenue for understanding how frontline
workers enable the everyday functioning of technology. Unlike
many technologies poised to disrupt retail, self-checkout does
not rely on machine learning. Instead, it relies on the refashioningof older digital technologies, using system design to externalize
the labor costs of scanning and bagging groceries onto shoppers.
59

Joseph Turow, The Aisles Have Eyes: How Retailers Track Your Shopping, Strip Your Privacy, andDefine Your Power (New Haven: Yale University Press, 2017), 3.

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37Grocery Retail

In many ways the self-checkout machine is more representativeof the piecemeal way in which retailers pick up and experimentwith new technologies, relying on humans to smooth out the roughedges. And unlike Amazon Go’s self-enclosed system, ordinary
supermarkets are patchwork spaces, layering new systems onto
old ones as they contend with the realities and limitations of existing physical infrastructures.
Self-checkout machines initially incited widespread fears
from workers, unions, labor advocates, and others that cashier jobs
would become obsolete, as they would soon be replaced with machines.60 But while they first became a commonplace technology
in the 1990s, by 2013 there were only roughly 191,000 self-checkout
stands in use worldwide.61 Retailers have periodically expanded andscaled back their use over the years. Some, like Target, have begun to expand the number of self-checkout stands in their stores,
while others, like CVS, have reduced their use.62 Typically, retailers
engage in survey work of local markets to gauge the extent to which
installing self-checkout machines will assist or hurt sales. Yet despite
the ebbs and flows of self-checkout’s popularity, its effect has been
similar to that of ATMs on bank teller jobs: while individual bank locations began employing fewer tellers, banks also began to open morebranches, thus increasing the total number of tellers countrywide.63
Similarly, the total number of cashier jobs in the US has actually increased, and most self-checkout areas in stores are monitored by anattendant.64 This is a dynamic we will explore in greater detail below.
60

Christopher K. Andrews, “Do-It-Yourself”: Self-Checkouts, Supermarkets, and the Self-Service Trend in
American Business, PhD diss., University of Maryland, 2009, http://drum.lib.umd.edu/handle/1903/9593.

61

Self-Checkout: A Global Consumer Perspective, NCR, 2013, 2, https://www.ncr.co.jp/wp-content/
uploads/files/solutions/self/fl/fl_wpa/RET_SCO_wp.pdf.

62

Barb Darrow, “Yay! Human Cashiers Prevail over Automation at Some CVS Stores,” Fortune, October
20, 2015, http://fortune.com/2015/10/20/cvs-self-check-out-stations/.

63

James Bessen, “Scarce Skills, Not Scarce Jobs,” the Atlantic, April 27, 2015, https://www.theatlantic.
com/business/archive/2015/04/scarce-skills-not-scarce-jobs/390789/.

64

Bourree Lam, “The Unexpected Resilience of Humans in Retail,” the Atlantic, April 21, 2016, https://www.theatlantic.com/business/archive/2016/04/humans-retail/479223/.

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Grocery Retail—
Shifting Roles: The Erosion of Frontline Grocery Retail Jobs

SHIFTING ROLES:THE EROSION OF FRONTLINE GROCERY RETAIL JOBS
This section provides an overview of some of the dynamics we foundin speaking to frontline grocery workers, including cashiers, selfcheckout attendants, store managers, and others, in the Los Angeles
area, providing insight into some of the ways that self-checkout has
unsettled the roles and expectations of cashiers.

While self-checkout did not kill
the cashier job, it did shift the roles,
responsibilities, and perceptionsof frontline retail workers’ jobs inoften detrimental ways.
While self-checkout did not kill the cashier job, it did shift the roles,
responsibilities, and perceptions of frontline retail workers’ jobs
in often detrimental ways. Long before the introduction of theself-checkout machine, the supermarket workforce was experiencing a trend of casualization, as employers began to erode workers’
wages and benefits.65 Store clerk roles are increasingly part timeand filled by a younger workforce with a higher turnover rate.66
The workforce today is at a crossroads of this progression: evenin our small sample of interviewees, we encountered employees
who had worked in their field for as short a period as a few months
to more than 30 years, from high school students up to those nearretirement age. This split has shaped how employees view their
65

Françoise Carré and Chris Tilly, Where Bad Jobs Are Better: Retail Jobs Across Countries and Companies (New York: Russell Sage Foundation, 2017), 20–21.

66

Christopher K. Andrews, “‘Do-It-Yourself”: Self-Checkouts, Supermarkets, and the Self-Service Trend in
American Business, PhD diss., University of Maryland, 2009, http://drum.lib.umd.edu/handle/1903/9593.

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Grocery Retail—
Shifting Roles: The Erosion of Frontline Grocery Retail Jobs

relationship to their work, as the “old-timer” employees we spoke
to generally felt that, while they had built a career within the grocery industry, they would soon be replaced with casual part-timers
once they retired.
Although the reasons for these shifts are complex, technology
has played a role throughout the broader economic and organizational changes to supermarket retail. In the 1940s, self-service
was popularized by retailers as an embodiment of American valuesof independence and greater consumer choice. Once shoppers
could pick items from shelves themselves, the assistance of store
clerks became less central.67 Stores also introduced the UPC and bar
code scanners in the 1970s, which, along with other trends toward“lean retailing,” meant that frontline workers no longer needed tomemorize even basic product information, such as price.68 By thetime self-checkout machines were introduced in the 1990s, shoppers had already come to expect minimal customer service fromsupermarket employees.
With their roles greatly transformed over the course of several
decades, frontline workers’ productivity became increasingly measured in terms of speed and efficiency. While bar code scanningwas not necessarily faster than a seasoned cashier with a goodmemory, the introduction of the scanners made it possible for retailstores to track cashiers’ scans per hour (SPH) as a metric of productivity.69 Instead of constant managerial oversight, check standworkers could be continuously “scrutinized through a wholly internalized process that actively shapes worker performance” as theytallied up purchases.70
67

Michael Palm, Technologies of Consumer Labor: A History of Self-Service (New York: Routledge,2016).

68

Françoise Carré and Chris Tilly, Where Bad Jobs Are Better, 128.

69

Emek Basker, “Raising the Barcode Scanner: Technology and Productivity in the Retail Sector,”
American Economic Journal: Applied Economics vol. 4, no. 3 (2012), 1–27.

70

Rob Kitchin and Martin Dodge, Code/Space: Software and Everyday Life (Cambridge, MA: The MIT
Press, 2014), 11.

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Shifting Roles: The Erosion of Frontline Grocery Retail Jobs

Speed and efficiency have also been some of the major marketingsells of self-checkout machines, as a solution to customer wait times
in checkout lines.71 Many retail technologies focus on the problemof accelerating and measuring the speed of revenue through thecheck stand. In 2018, for instance, Walmart patented a system forrecording conversations in proximity of cash registers in order tomeasure the speed of customers’ movement through a line.72

Self-checkout machines never achieved the efficiency gains they hadoriginally promised, but many cashiers still feel that the machines
were the point of comparison against which they were measured
and valued by both store management and the shoppers with whomthey interacted daily. Lisa, a cashier in her 40s who has worked ingrocery for the last 24 years, spoke about how she had gotten used
to working both human-operated check stands and monitoring
self-checkout areas. However, she felt frustrated that even when
she worked the human-operated check stands, customers had a71

BRP Consulting, Defining a Next Generation POS Platform Strategy, Fujitsu, June 2017, http://
marketing.us.fujitsu.com/rs/407-MTR-501/images/Defining%20a%20Next-Generation%20
Grocery%20POS%20Platform%20Strategy.pdf.

72

Caroline O’Donovan, “Walmart’s Newly Patented Technology for Eavesdropping on Workers Presents
Privacy Concerns,” BuzzFeed News, July 11, 2018, https://www.buzzfeednews.com/article/
carolineodonovan/walmart-just-patented-audio-surveillance-technology-for.

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41

Grocery Retail—
Shifting Roles: The Erosion of Frontline Grocery Retail Jobs

tendency to perceive her not as a person but as an “extension of themachine.” This affected not only her ability to socially connect withcustomers in small ways but also made her feel dehumanized. She
said, “The ones that see you as a person are willing to be more patient and wait for you. The other ones that see you not as a person,
but more as an extension, they could care less who’s next.” Instead,she added, receiving immediate assistance is their only concern.
At the same time, cashiers often reported feeling like they
face contradictory expectations from management to both provide satisfactory service and to process transactions quickly. Thisputs strain on the relationships that employees have with long-timegrocery shoppers, particularly at local stores with an older customer base. As one cashier explained, “If you have a customer that’s
been coming to the store for who knows how long, X amount ofyears, and they know you, you’re not just going to check them out
as if they’re just an object.” The “human touch” was often cited as
an important part of the work for the cashiers we spoke with andwas often a reason why an employee chose to work in the front
rather than behind the scenes. But often frontline workers foundthemselves too pressed for time to converse with customers.
Self-checkout machines have played a role in the casualizationof retail employment by making it easier for stores to reduce total
worker hours and to more heavily rely on part-time staff, because
the machines can be made active or inactive on an as-needed basis.73 However, explanations for what this flexibility enables in practice differed between the store management and frontline workers
we interviewed. As explained by management in conversation andofficial company statements, self-checkout machines provide faster
service to shoppers while easing pressure on check stand workers,
freeing them up to do other necessary work. As one store manager

73

Christopher K. Andrews, “‘Do-It-Yourself”: Self-Checkouts, Supermarkets, and the Self-Service
Trend in American Business, PhD diss., University of Maryland, 2009, http://drum.lib.umd.edu/handle/1903/9593.

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Shifting Roles: The Erosion of Frontline Grocery Retail Jobs

said, “If you want it, there’s a person there. But [self-checkout] does
free up what would generally be a cashier position to do somethingelse in the store or to help the store in a different way.” She explainedthat the machines ensure one-to-one service between employees
and shoppers in need of help at any given time.
However, frontline employees tended to view self-checkoutmachines as a company tool for cost cutting by reducing worker
hours, placing more pressure on cashiers. Some frontline employees
expressed frustration and confusion over what they believed to be
management’s intentional, routine understaffing of the human-operated check stands. As one cashier explained:
When [self-checkout] was introduced, they basically reduced
the staff at the front, making the customer use these machines
rather than wait in line. But what happens now is that since they
don’t really use the staff up front, they’ll purposely allow thelines to get so long, and the customer of course gets frustrated
and is forced to use the machine.
While self-checkout technology is often marketed as a response to“natural” consumer demand,74 cashiers are often instructed to funnel
shoppers to the self-checkout lanes as much as possible. Storestypically receive quotas from higher levels of management for thedesired percentage of shoppers taking their purchases throughself-checkout. Frontline workers complained that this target percentage for self-checkout was being periodically raised. At one
store we visited, for instance, the staff was working toward
increasing shoppers’ use of self-checkout machines up to a rate of
30%. However, this proved to be challenging because of regional74

For example, a 2014 market study by automated kiosk manufacturer NCR Corporation concluded that“tech-savvy consumers appreciate the convenience and speed of retail self-checkout. Retailers are responding to consumer demand and usage, and the loyalty and impact on the bottom line that the technology can drive in-store.” “Global Study Reveals That Shoppers and Retailers Widely Value and Adopt
Self-Checkout Technology,” NCR, September 30, 2014, https://www.ncr.com/news/newsroom/newsreleases/retail/global-study-reveals-shoppers-retailers-value-adopt-self-checkout-technology.

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Shifting Roles: The Erosion of Frontline Grocery Retail Jobs

and demographic variation in preference for use of self-checkoutmachines; while some shoppers embrace the technology, others
must be convinced. Employees at one store we observed found
it very difficult, for example, to convince elderly shoppers to use
self-checkout machines. Even when they did, these shoppers often
slowed down the line by needing significant assistance.
These trends – the casualization of the workforce, new efficiency pressures, and feelings of devaluation – have likely contributed
to the high worker turnover of supermarket retail. While frontline
workers feel the consequences through periodic understaffing,
store management struggles with hiring, training, and scheduling.
At one supermarket location, a sales manager pointed out that theirstore’s turnover rate for employees had reached a high of 75%. Management held regular meetings to discuss strategies for fostering
higher employee retention, and the company had instituted aninternal program for addressing this issue. But Arnold, a grocery
clerk who has worked in the industry for 25 years, was skeptical ofthese efforts, pointing out that the fundamental root of the problem
was that as employers, supermarkets no longer provided the benefits
and wages that attract and retain employees:
What’s really clear is there’s not the incentives there used to
be for people, that just aren’t working, and you see that in theturnover now. ... They’re not getting paid much, there is nobenefits, so there is a bigger turnover as far as people quitting,and us having to hire new people.
He further observed that processes for training new employees had
been sped up, relying increasingly on computer modules andpamphlets rather than more intensive, one-to-one training that
had been standard in the past. This is in line with broader trends inthe retail industry, where companies have invested in technology-

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Minding The Machines

facilitated training in lieu of more labor-intensive training with humansupervision. In 2018, for instance, Walmart piloted an associatetraining program conducted via virtual reality headset.75
As this section has shown, the impact of these retail technologies has generally not been one of replacing human labor. Rather,
they facilitate cost-cutting measures such as relying more heavily
on part-time employees, understaffing, and intensifying work
activities. In this context, employers can place greater pressures onfrontline workers to absorb the consequences of these businessdecisions. In other words, the “success” of technologies like selfcheckout machines is in large part produced by the human effort
necessary to maintain them.

UNEVENLY DISTRIBUTED COSTS OF EXPERIMENTATION:MINDING THE MACHINES
While intuitively it would seem that the introduction of technologies like self-checkout machines would require supervision fromless-experienced employees, this has not been the case. Two rowsof self-checkout machines line each side of the check stands at
75

Mariella Moon, “Walmart Turns to VR and Oculus Go for Associates’ Training,” Engadget, September
20, 2018, https://www.engadget.com/2018/09/20/walmart-vr-training-oculus-go/.

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Grocery Retail—Unevenly Distributed Costs of Experimentation:
Minding The Machines

a particularly large, bustling Ralphs supermarket in northern Los
Angeles. Checkout employee Lisa deftly alternates between talking
about her work and keeping her eyes and attention fixed on thelong line of shoppers waiting to scan their items. “I hate working theside with six machines,” she says in a tone of frustration, pointing
to the automated station on the side opposite her own, where only
four machines are installed. Overseeing six machines, she explains,pushes the limits of her ability to keep the flow of customers runningsmoothly. The amount of assistance shoppers need to completea transaction seems to vary widely. When shoppers do need assistance, they don’t often ask for it directly but instead freeze
mid-transaction at their stations, uncertain what to do next. Lisa
must keep her eyes on shoppers’ body language to know whento step in, usually to do something as simple as pointing out the“Credit” button on the touch-screen interface. In addition to keeping the line flowing, she is frequently approached by shoppers whocluster near the side of her station to ask questions. Lisa does her
best to assist them even though she cannot leave her station or pullher attention away from the machines for too long.

The human infrastructures ofself-checkout include not only
the know-how and input of theself-checkout attendant but also
the labor of the consumer.
Lisa’s experience, and those of other employees that we interviewed,
underscores the extent to which self-checkout technologies require
human infrastructures to function. The human infrastructures ofself-checkout include not only the know-how and input of the

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Grocery Retail—Unevenly Distributed Costs of Experimentation:
Minding The Machines

self-checkout attendant but also the labor of the consumer. This
practice has commonly been termed shadow work, which sociologist
Craig Lambert defines as “all the unpaid tasks we do on behalf ofbusinesses and organizations” and in which a “technological innovation enables a business, like a restaurant, to remove one or more
employees from transactions.”76 For retail companies, a primary
benefit of self-service technologies has been offloading labor onto
shoppers to reduce labor costs.

Filling the gap between shoppers
and checkout machines requires
a different skill set than that ofsimply operating a check stand,more akin to that of a traffic
officer coordinating vehicles at
a convoluted intersection.
However, shoppers do not make efficient workers. The automatedmessage “unexpected item in the bagging area,” used to indicate
when the weight-based detection system fails to identify a product,
has become iconic of consumers’ frustrations with self-service
technologies.7 7 Filling the gap between shoppers and checkoutmachines requires a different skill set than that of simply operatinga check stand, more akin to that of a traffic officer coordinating
vehicles at a convoluted intersection. As one sales manager said,“Usually, we want our most experienced cashiers on these robots.
76

Craig Lambert, Shadow Work: The Unpaid, Unseen Jobs That Fill Your Day (Berkeley, CA: Counterpoint, 2015), 141.

77

Adriana Hamacher, “The Unpopular Rise of Self-Checkouts (and How to Fix Them),” BBC, May 10, 2017,
http://www.bbc.com/future/story/20170509-the-unpopular-rise-of-self-checkouts-and-how-to-fix-them.

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Grocery Retail—Unevenly Distributed Costs of Experimentation:
Minding The Machines

Just the fact that if you’re not paying attention and if somebody’s
not scanning an item, you’re just giving it away, you know?” Workers
monitoring self-checkout need competencies including diagnosing a
shopper’s source of confusion, being able to spot potential theft,78
and dealing with the fatigue of maintaining attention, multitasking,and standing for long stretches of time. In some cases, frontline
employees had also taught themselves to do basic mechanicaland software repairs, since the machines often broke down andmanagers were reluctant to call in a technician. Luis, a cashier in
his 50s, described how he was often called upon to fix mechanicalissues, such as unjamming the cash dispensers. Although this was
not a part of his official job description, he gained a reputation
among staff as being “mechanically inclined” because of his previous
work experience repairing Bell and Howell equipment in the 1990s.

While retailers experimentwith new ways of reconfiguring
shopping practices throughtechnology, frontline employees
struggle to compensate for these
new systems’ shortcomings.

78

See Emmeline Taylor, “Supermarket Self-Checkouts and Retail Theft: The Curious Case of the SWIPERS,”
Criminology & Criminal Justice, April 18, 2016, https://doi.org/10.1177/1748895816643353. An unintended
consequence of self-checkout technology is that retailers have been plagued with a “shrinkage” problem—the loss of merchandise through theft, intentional or accidental. Self-checkout machines are typically designed with weight-based anti-theft systems, but these measures are often inadequate, as shoppers
often unintentionally walk off with products or have found ways to game these systems. Although we hypothesized that self-checkout monitors would be held liable for thefts occurring on their watch, we foundthat retailers have generally accepted shrinkage as inevitable, although monitors were still perceived as
playing an important role in reducing loss through the self-checkout lanes.

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Grocery Retail—Unevenly Distributed Costs of Experimentation:
Minding The Machines

While retailers experiment with new ways of reconfiguring shopping practices through technology, frontline employees struggle tocompensate for these new systems’ shortcomings. Self-checkout
areas are typically arranged so that one attendant has a clear line ofsight on all of the machines at once. This model grants employees
both a sense of control and a clearly delimited territory for which
they can be held responsible during a given shift. But in recent
years, supermarket retailers have introduced scan-and-go systems
that shift self-service systems from check stands into the aisles.
For instance, major supermarket retailer Kroger has recently introduced a program called Scan, Bag, Go, with which shoppers candownload an app or use a handheld scanner to check out products
as they get them from the shelves.79

Usually, we want our most experienced cashiers on these robots.
An employee at a Kroger subsidiary supermarket where Scan, Bag,
Go had been installed explained that one of the biggest challenges
that scan-and-go systems posed was that they often made it harder
to provide adequate customer service. The app or the handheld
scanner frequently showed error messages, typically with the accompanying message: “Help is on the way! The associate named
below has accepted your help request.” And while in theory the
GPS signal from the shopper’s phone or handheld scanner was to be79

See “Kroger Announces Divisions for Scan, Bag, Go Expansion,” Kroger, January 31, 2018, http://
ir.kroger.com/file/Index?KeyFile=391984350. Thus far, retailers have had mixed results in integrating these systems into the everyday routines within stores, as some retailers have backtracked or
abandoned their use. In early 2018, for instance, Walmart tested its Scan & Go system in over 100
of its stores across the US, but a few months later it abandoned the program due to low consumer participation; see: Gregory Magana, “Walmart Shutters Its Scan & Go Program,” Business Insider, May 17, 2018, https://www.businessinsider.com/walmart-ends-cashierless-checkout-2018-5.
Walmart then introduced a new program called Check Out with Me, in which sales associates walkthrough the aisles, checking out customers on the spot using handheld credit card swipers; see
Sarah Perez, “Walmart Launches ‘Check Out With Me’ For On-The-Spot Checkouts in Hundreds of
US Locations” Tech Crunch, April 19, 2018, https://techcrunch.com/2018/04/19/walmart-launchescheck-out-with-me-for-on-the-spot-checkouts-in-hundreds-of-u-s-stores/.

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Grocery Retail—Unevenly Distributed Costs of Experimentation:
Minding The Machines

used to locate the shopper, often this information was imprecise andthere was a delay before a store employee could access the shareddevice that indicated the shopper’s location. In the meantime,
shoppers tended to quickly lose patience and wander from their
original spot. Additionally, shoppers frequently had trouble angling
their phones or handheld devices in order to scan barcodes andoften got stuck trying to scan an alcohol item. For employees, thetask of providing quality customer service for such systems ischaotic. The transformation of the entire store into one large,
distributed checkout process created a sense of uncertainty and lackof control for workers, whose workspace boundaries were now
nebulously defined.

Our research shows that self-service
technologies have not reduced but
rather reconfigured the skills andresponsibilities that fall on cashiers.
Our research shows that self-service technologies have not
reduced but rather reconfigured the skills and responsibilities thatfall on cashiers. Often, the technologies are intensifying the work ofcustomer service and creating new challenges, such as balancing
between performance pressures from store management and thedemands of shoppers. From a top-down perspective, much of thework that employees must do on the ground to facilitate experimentation with new systems raises questions about how this work
may be invisible and undervalued, even as popular perceptions ofautomation frame these roles as increasingly obsolete.

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50Conclusion:
The Who, What, and Where of Disruption
Examining the realities of AI and automated technologies throughthe lens of integration rather than deployment and reconfiguration rather than replacement allows us to see not only how thesetechnologies are actually working but also for whom and to whose
benefit. While the hype and marketing around AI technologies
point toward broad benefits and universal gains, on-the-groundconsequences are much more complicated. AI technologies must
be integrated into existing social contexts, through the work of particular human bodies and practices. These human infrastructures
are necessary to realize the promises and goals of automated andAI technology.
In the cases of farm management and grocery retail technologies, we articulated two common categories of consequence occasioned by the introduction of automated and AI technologies: shifts
in work roles and expectations, and unevenly distributed costsof experimentation. However, within these two common themes
there are significant differences to articulate. Farmers and checkout workers have different degrees of agency and choice when it
comes to adopting technology. While we described the pressuresof acquiring new resources and skills in the family-owned farmcontext and the kinds of tensions and risks associated with theseacquisitions, farmers are able to act as decision-makers about
adopting new technology. While their decisions may be shaped orconstrained by social and economic forces, they have degrees ofchoice. In contrast, grocery retail workers have little to no agency
in deciding the types of technologies with which they work and theconditions of that work. Looking forward, planning for and assessing
new AI technologies must take into account the kinds of agency
and variable power that different types of populations will be able
to exercise.
The frames and contexts presented in this report point
toward how we might improve future development, assessment,

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Conclusion: The Who, What, and Where of Disruption

and regulation of AI technologies. Focusing on the integration of AI
and the labor that is reconfigured brings into view structures of powerthat are at stake. This can help us anticipate who will be empowered
and who will be left without a voice. Our findings suggest that public understanding and debates would be strengthened if reportingon AI focuses not just on a technology’s potential capacity or ideal
use case, but also on the labor of integration and the humans whoare either left in the lurch or relied upon to smooth out a technology’s rough edges. Similarly, if designers keep these humanoperators and users at the center of development, AI technologies
can enhance and complement existing skills and expertise, rather
than foreclose the discretionary power of frontline workers. Finally,
our research suggests that policy makers and advocates should
keep watch of the unevenly distributed costs of experimenting
and implementing new technologies. Advocates may consider how
the labor of AI integration is often under-acknowledged, and theconsequences for how workers are compensated and supported.
Given that the labor of AI integration is often invisible or underacknowledged, it is all the more important to ensure that those
who work with and alongside AI systems are adequately protected,
supported, and compensated. While “transitional” or “intermediate”
phases are by definition temporary, they are still actualities that
affect workers in the present and need to be addressed.
While machine learning systems are increasingly underpinning interactions with mobile and internet applications(from search and news curation to virtual assistants like Siri orAlexa), applications in everyday physical locations like corn fields
and supermarkets have tended to be more limited, involving simpler automation, relatively limited user bases, or prototype-phase
AI technologies. The integration of AI into every aspect of daily life
may come one day, but it is not here yet. Now is the time to develop
new ways of thinking about and designing AI technologies.

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Conclusion: The Who, What, and Where of Disruption

AI holds great promise for advancing society and addressing
existing problems. However, the potential benefits must not
obscure these technologies’ potential perils. These perils havenothing to do with “killer robots” or the coming of “robot overlords.” They will be found in the everyday structuring potentials of
AI that will benefit some members of society but may leave manyothers behind.

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53

Acknowledgments

This research was conducted with support from the Ethics andGovernance of AI Fund. Portions of the research on agriculture
technologies were conducted in 2016, supported by a grant fromthe Digital Trust Foundation. We are grateful to the interview participants who shared their time and experiences with us, especiallyto Paul Edwards and the staff at the United Food and Commercial
Workers Local 770 in Los Angeles. This work has benefited fromprevious collaborations and ongoing conversations with Karen
Levy and Solon Barocas. We thank Julia Ticona, Alex Rosenblat,
Aiha Nguyen, danah boyd, Sareeta Amrute, Annette Bernhardt,
and Joann Lo for their insightful comments on previous drafts andPatrick Davison for his expert editorial eye. Last but not certainly
not least, we thank Rati Bishnoi, Erica Kermani, Lauren Gray, andthe staff members at Data & Society whose work and expertisemake this report possible.

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54

Data & Society

Data & Society is an independent nonprofit research institutethat advances new frames for understanding the implicationsof data-centric and automated technology. We conduct researchand build the field of actors to ensure that knowledge guides
debate, decision-making, and technical choices.
datasociety.net@datasociety
Designed by Anisa Suthayalai, Default NYC
Illustrated by Jim Cooke

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