ways of addressing these questions.
Cooperatives have sprung up to offer
worker-owned substitutes for platform economy services like Uber and
Airbnb. Platformcoop.net acts as a
backbone to share information about
the emerging platform cooperativism
movement. Uber drivers, their work
haunted by Uber’s threats to automate them away, have formed a collective bargaining unit in Seattle to give
them a voice in the development of the
transportation system they power.
The biggest change to the future of
computer science, however, remains for
readers like you to tackle. Practice computer science with a curious respect for
the knowledge and skill that is expressed
in the work others do. Think t wice before
promising you can automate it away, and
instead put your technical labors in service of improving workers lives rather
than replacing them.
Parts of this article are adapted from
or reproduced with permission from:
Lilly Irani’s “Justice for ‘Data Janitors’.”
Available at: www.publicbooks.org/
[ 1] Lohr, S. For big-data scientists, ‘janitor work’ is key
hurdle to insights. New York Times, August 17, 2014.
[ 2] Howe, J. Mechanical Turk targets small business.
Blog. August 8, 2008; http://www.crowdsourcing.com/
[ 3] clickhappier. Demographics of Mechanical Turk.
Mturkgrind.com. Forum. July 28, 2014.
[ 4] Berg, J. Income security in the on-demand economy:
findings and policy lessons from a survey of
crowdworkers. Comparative Labor Law & Policy
Journal 37, 3 (2016).
[ 5] Andersen, K. Enthusiasts and skeptics debate
artificial intelligence. Vanity Fair. November
26, 2014. http:// www.vanityfair.com/ne ws/
[ 6] Feigenbaum, E. 2007. Interview with Nils Nilsson
[ Transcript]. Computer History Museum, Mountain
View, CA. CHM Reference number: X3896.2007
Lilly Irani is an assistant professor of communication
and science studies at University of California, San
Diego. Her work examines and intervenes in the cultural
politics of high tech work. She is a co-founder and
maintainer of Turkopticon.
© 2016 Copyright held by Owner(s)/Author(s).
Publication rights licensed to ACM.
skills for the job, employers can afford
to pay less. Workers I have met online
include laid-off teachers, mobility-im-paired professionals, military retirees,
agoraphobic writers, undersupported
college students, stay-at-home parents,
and even Malaysian programmers-in-training. This variety of backgrounds,
skills, and languages benefits employers running surveys, training data sets,
and making sense of complex narratives and images.
The stakes are also financial. Investors, whether in stock markets or
in venture capital, reward companies
that do more with fewer employees on
the books, and they especially reward
technology companies they expect can
scale their operations while minimizing their marginal costs. This point hit
home when I saw a microlabor startup
CEO at a crowdsourcing conference explain how he pitched his company as
a “technology company” rather than
a “labor company” so investors would
value his company more highly.
Technologists keep microworkers at a distance and out of sight for
cultural reasons as well. AMT workers pose a double threat to the idea
of that computer scientists make the
world a better place. First, AMT workers and “data janitors” stand for the
persistent failure of computer scientists to fulfill venture-hyped dreams
of grand technical achievement. Second, AMT workers know and show
the limitations of the dream that everyone can be an entrepreneur. They
know and show the hierarchies of the
networked information workplace.
Should they lean in, take MOOCs,
and be more creative? Among the
most active AMT workers, nearly 58
percent already have a bachelor’s
degree or higher. (An AMT worker,
“clickhappier,” generated this statistic by cross-tabulating data from
NYU professor Panos Ipeirotis [ 3].
An International Labor Organization survey in 2016 largely echoed
these calculations [ 4].) AMT workers
absorb the absence of management
and arbitration from Amazon, running their own web forums where
they consult with employers resolve
problems and design effective tasks.
By hiding these workers and their
ingenuity, technologists hide the ac-
tual human smarts that keep the inter-
net economy firing on all pistons. This
leads to a vicious circle. We mytholo-
gize technology companies. We look
to STEM education, creativity, and en-
trepreneurship as paths to social mo-
bility. We miss how as entrepreneurial
workers climb the value chain, they do
so in part by obscuring and devaluing
the contributions of those they rely on.
The field of computer science passionately pursues artificial intelligence, as if the machine computation
of logic, language, and reason are a
shining achievement that justifies
its costs and elisions. Is AI “manifest
destiny?” Ray Kurzweil [ 5] and Ed Feigenbaum [ 6] have called it so. The
U.S. government colonized the North
American continent, decimating Native American people on their way to
achieve its goal. So manifest was the
destiny, that those in the way simply became barriers to be overcome.
Computer scientists sometimes treat
the people who help their systems
along—data workers who spoon-feed their machines, or tech support
workers, for example—as a temporary form of human support they just
need to get over. What would computer science look like if it did not see
human-algorithmic partnerships as
an embarrassment, but rather as an
ethical project where the humans
were as, or even more, important
than the algorithms? What would it
look like if artificial intelligence and
human-computer interaction put the
human care and feeding of computing at the center rather than hiding it
in the shadows?
Scholars and activists in HCI, de-
sign, and labor experiment with some
and celebrants of
insist on devaluing
and even hiding
this form of work?