Algorithms of Oppression How Search Engines Reinforce Racism Book Questions Though the internet was envisioned as an ideal space for democratic discourse a

Algorithms of Oppression How Search Engines Reinforce Racism Book Questions Though the internet was envisioned as an ideal space for democratic discourse and enlightened values, racism and misogyny still thrive online.

In Algorithms of Oppression,Preview the document Safiya Umoja Noble illustrates how racism is embedded in the design of seemingly “neutral” digital technologies.

In “#Gamergate and The Fappening: How Reddit’s Algorithm, Governance, and Culture Support Toxic Technocultures,” Adrienne Massanari examines how the design of platforms can influence online behavior and amplify objectionable ideas and content.

In this response, please address the following questions:

1. What happened when Noble searched for “black girls” in 2010? What do “glitches” (such as examples involving Google photo-tagging, Maps, and Images) suggest about the neutrality of facial recognition systems and search algorithms?

2. How does Massanari define “toxic technocultures”? What “tactics” and “ideas” are common within these communities?

3. Massarani argues that the design of Reddit’s platform encourages its “toxic technoculture.” How do karma points and r/all amplify content on the site?

Double space, 1 page Safiya Umoja Noble, Algorithms of Oppression: How Search Engines Reinforce Racism,
New York University Press, 2018.
Introduction
The Power of Algorithms
A Society, Searching
This book is about the power of algorithms in the age of neoliberalism
Copyright © 2018. New York University Press. All rights reserved.
Searching
forthose
Black
Girlsdecisions reinforce oppressive social relaand the ways
digital
tionships and enact new modes of racial profiling, which I have termed
technological redlining. By making visible the ways that capital, race, and
gender are factors in creating unequal conditions, I am bringing light
Searching
for People and Communities
to various forms of technological redlining that are on the rise. The
near-ubiquitous use of algorithmically driven software, both visible and
invisible to everyday people, demands a closer inspection of what values
are prioritized
such automated
decisionsystems. Typically,
Searching
forinProtections
from
Searchmaking
Engines
the practice of redlining has been most often used in real estate and
banking circles, creating and deepening inequalities by race, such that,
for example, people of color are more likely to pay higher interest rates
The
Future just
of Knowledge
Public
or premiums
because they in
arethe
Black
or Latino, especially if they live
in low-income neighborhoods. On the Internet and in our everyday uses
of technology, discrimination is also embedded in computer code and,
increasingly,
intelligence
technologies that we are reliant on,
The
Futureinofartificial
Information
Culture
by choice or not. I believe that artificial intelligence will become a major
human rights issue in the twenty-first century. We are only beginning to
understand the long-term consequences of these decision-making tools
Conclusion
in both masking and deepening social inequality. This book is just the
start of trying to make these consequences visible. There will be many
more, by myself and others, who will try to make sense of the consequences of automated decision making through algorithms in society.
Epilogue
Part of the challenge of understanding algorithmic oppression is to
understand that mathematical formulations to drive automated decisions are made by human beings. While we often think of terms such as
“big data” and “algorithms” as being benign, neutral, or objective, they
are anything but. The people who make these decisions hold all types of
1
Noble, Safiya Umoja. Algorithms of Oppression : How Search Engines Reinforce Racism. New York: New York University
Press, 2018. Accessed April 25, 2020. ProQuest Ebook Central.
Created from unh on 2020-04-25 07:19:27.
Copyright © 2018. New York University Press. All rights reserved.
2
| Introduction
values, many of which openly promote racism, sexism, and false notions
of meritocracy, which is well documented in studies of Silicon Valley
and other tech corridors.
For example, in the midst of a federal investigation of Google’s alleged
persistent wage gap, where women are systematically paid less than men
in the company’s workforce, an “antidiversity” manifesto authored by
James Damore went viral in August 2017,1 supported by many Google
employees, arguing that women are psychologically inferior and incapable of being as good at software engineering as men, among other
patently false and sexist assertions. As this book was moving into press,
many Google executives and employees were actively rebuking the assertions of this engineer, who reportedly works on Google search infrastructure. Legal cases have been filed, boycotts of Google from the
political far right in the United States have been invoked, and calls for
greater expressed commitments to gender and racial equity at Google
and in Silicon Valley writ large are under way. What this antidiversity
screed has underscored for me as I write this book is that some of the
very people who are developing search algorithms and architecture are
willing to promote sexist and racist attitudes openly at work and beyond,
while we are supposed to believe that these same employees are developing “neutral” or “objective” decision-making tools. Human beings are
developing the digital platforms we use, and as I present evidence of the
recklessness and lack of regard that is often shown to women and people
of color in some of the output of these systems, it will become increasingly difficult for technology companies to separate their systematic and
inequitable employment practices, and the far-right ideological bents of
some of their employees, from the products they make for the public.
My goal in this book is to further an exploration into some of these
digital sense-making processes and how they have come to be so fundamental to the classification and organization of information and at
what cost. As a result, this book is largely concerned with examining the
commercial co-optation of Black identities, experiences, and communities in the largest and most powerful technology companies to date,
namely, Google. I closely read a few distinct cases of algorithmic oppression for the depth of their social meaning to raise a public discussion of the broader implications of how privately managed, black-boxed
information-sorting tools have become essential to many data-driven
Noble, Safiya Umoja. Algorithms of Oppression : How Search Engines Reinforce Racism. New York: New York University
Press, 2018. Accessed April 25, 2020. ProQuest Ebook Central.
Created from unh on 2020-04-25 07:19:27.
Copyright © 2018. New York University Press. All rights reserved.
Introduction
|
3
decisions. I want us to have broader public conversations about the implications of the artificial intelligentsia for people who are already systematically marginalized and oppressed. I will also provide evidence and
argue, ultimately, that large technology monopolies such as Google need
to be broken up and regulated, because their consolidated power and
cultural influence make competition largely impossible. This monopoly
in the information sector is a threat to democracy, as is currently coming to the fore as we make sense of information flows through digital
media such as Google and Facebook in the wake of the 2016 United
States presidential election.
I situate my work against the backdrop of a twelve-year professional
career in multicultural marketing and advertising, where I was invested
in building corporate brands and selling products to African Americans
and Latinos (before I became a university professor). Back then, I believed, like many urban marketing professionals, that companies must
pay attention to the needs of people of color and demonstrate respect
for consumers by offering services to communities of color, just as is
done for most everyone else. After all, to be responsive and responsible
to marginalized consumers was to create more market opportunity. I
spent an equal amount of time doing risk management and public relations to insulate companies from any adverse risk to sales that they
might experience from inadvertent or deliberate snubs to consumers of
color who might perceive a brand as racist or insensitive. Protecting my
former clients from enacting racial and gender insensitivity and helping
them bolster their brands by creating deep emotional and psychological attachments to their products among communities of color was my
professional concern for many years, which made an experience I had
in fall 2010 deeply impactful. In just a few minutes while searching on
the web, I experienced the perfect storm of insult and injury that I could
not turn away from. While Googling things on the Internet that might
be interesting to my stepdaughter and nieces, I was overtaken by the
results. My search on the keywords “black girls” yielded HotBlackPussy.
com as the first hit.
Hit indeed.
Since that time, I have spent innumerable hours teaching and researching all the ways in which it could be that Google could completely
fail when it came to providing reliable or credible information about
Noble, Safiya Umoja. Algorithms of Oppression : How Search Engines Reinforce Racism. New York: New York University
Press, 2018. Accessed April 25, 2020. ProQuest Ebook Central.
Created from unh on 2020-04-25 07:19:27.
4
|
Introduction
Copyright © 2018. New York University Press. All rights reserved.
Figure I.1. First search result on keywords “black girls,” September 2011.
women and people of color yet experience seemingly no repercussions
whatsoever. Two years after this incident, I collected searches again, only
to find similar results, as documented in figure I.1.
In 2012, I wrote an article for Bitch magazine about how women and
feminism are marginalized in search results. By August 2012, Panda (an
update to Google’s search algorithm) had been released, and pornography was no longer the first series of results for “black girls”; but other
girls and women of color, such as Latinas and Asians, were still pornified. By August of that year, the algorithm changed, and porn was suppressed in the case of a search on “black girls.” I often wonder what kind
of pressures account for the changing of search results over time. It is
impossible to know when and what influences proprietary algorithmic
design, other than that human beings are designing them and that they
are not up for public discussion, except as we engage in critique and
protest.
This book was born to highlight cases of such algorithmically driven
data failures that are specific to people of color and women and to underscore the structural ways that racism and sexism are fundamental
to what I have coined algorithmic oppression. I am writing in the spirit
of other critical women of color, such as Latoya Peterson, cofounder of
the blog Racialicious, who has opined that racism is the fundamental
application program interface (API) of the Internet. Peterson has argued that anti-Blackness is the foundation on which all racism toward
other groups is predicated. Racism is a standard protocol for organizing behavior on the web. As she has said, so perfectly, “The idea of a
n*gger API makes me think of a racism API, which is one of our core
arguments all along—oppression operates in the same formats, runs the
same scripts over and over. It is tweaked to be context specific, but it’s
all the same source code. And the key to its undoing is recognizing how
many of us are ensnared in these same basic patterns and modifying our
Noble, Safiya Umoja. Algorithms of Oppression : How Search Engines Reinforce Racism. New York: New York University
Press, 2018. Accessed April 25, 2020. ProQuest Ebook Central.
Created from unh on 2020-04-25 07:19:27.
Copyright © 2018. New York University Press. All rights reserved.
Introduction
|
5
own actions.”2 Peterson’s allegation is consistent with what many people
feel about the hostility of the web toward people of color, particularly
in its anti-Blackness, which any perusal of YouTube comments or other
message boards will serve up. On one level, the everyday racism and
commentary on the web is an abhorrent thing in itself, which has been
detailed by others; but it is entirely different with the corporate platform
vis-à-vis an algorithmically crafted web search that offers up racism and
sexism as the first results. This process reflects a corporate logic of either
willful neglect or a profit imperative that makes money from racism and
sexism. This inquiry is the basis of this book.
In the following pages, I discuss how “hot,” “sugary,” or any other
kind of “black pussy” can surface as the primary representation of Black
girls and women on the first page of a Google search, and I suggest that
something other than the best, most credible, or most reliable information output is driving Google. Of course, Google Search is an advertising
company, not a reliable information company. At the very least, we must
ask when we find these kinds of results, Is this the best information?
For whom? We must ask ourselves who the intended audience is for a
variety of things we find, and question the legitimacy of being in a “filter
bubble,”3 when we do not want racism and sexism, yet they still find
their way to us. The implications of algorithmic decision making of this
sort extend to other types of queries in Google and other digital media
platforms, and they are the beginning of a much-needed reassessment
of information as a public good. We need a full-on reevaluation of the
implications of our information resources being governed by corporatecontrolled advertising companies. I am adding my voice to a number
of scholars such as Helen Nissenbaum and Lucas Introna, Siva Vaidhyanathan, Alex Halavais, Christian Fuchs, Frank Pasquale, Kate Crawford, Tarleton Gillespie, Sarah T. Roberts, Jaron Lanier, and Elad Segev,
to name a few, who are raising critiques of Google and other forms of
corporate information control (including artificial intelligence) in hopes
that more people will consider alternatives.
Over the years, I have concentrated my research on unveiling the
many ways that African American people have been contained and
constrained in classification systems, from Google’s commercial search
engine to library databases. The development of this concentration was
born of my research training in library and information science. I think
Noble, Safiya Umoja. Algorithms of Oppression : How Search Engines Reinforce Racism. New York: New York University
Press, 2018. Accessed April 25, 2020. ProQuest Ebook Central.
Created from unh on 2020-04-25 07:19:27.
Copyright © 2018. New York University Press. All rights reserved.
6
| Introduction
of these issues through the lenses of critical information studies and critical race and gender studies. As marketing and advertising have directly
shaped the ways that marginalized people have come to be represented
by digital records such as search results or social network activities, I
have studied why it is that digital media platforms are resoundingly
characterized as “neutral technologies” in the public domain and often,
unfortunately, in academia. Stories of “glitches” found in systems do not
suggest that the organizing logics of the web could be broken but, rather,
that these are occasional one-off moments when something goes terribly
wrong with near-perfect systems. With the exception of the many scholars whom I reference throughout this work and the journalists, bloggers, and whistleblowers whom I will be remiss in not naming, very few
people are taking notice. We need all the voices to come to the fore and
impact public policy on the most unregulated social experiment of our
times: the Internet.
These data aberrations have come to light in various forms. In 2015,
U.S. News and World Report reported that a “glitch” in Google’s algorithm led to a number of problems through auto-tagging and facialrecognition software that was apparently intended to help people search
through images more successfully. The first problem for Google was that
its photo application had automatically tagged African Americans as
“apes” and “animals.”4 The second major issue reported by the Post was
that Google Maps searches on the word “N*gger”5 led to a map of the
White House during Obama’s presidency, a story that went viral on the
Internet after the social media personality Deray McKesson tweeted it.
These incidents were consistent with the reports of Photoshopped
images of a monkey’s face on the image of First Lady Michelle Obama
that were circulating through Google Images search in 2009. In 2015,
you could still find digital traces of the Google autosuggestions that associated Michelle Obama with apes. Protests from the White House led
to Google forcing the image down the image stack, from the first page,
so that it was not as visible.6 In each case, Google’s position is that it
is not responsible for its algorithm and that problems with the results
would be quickly resolved. In the Washington Post article about “N*gger
House,” the response was consistent with other apologies by the company: “‘Some inappropriate results are surfacing in Google Maps that
should not be, and we apologize for any offense this may have caused,’
Noble, Safiya Umoja. Algorithms of Oppression : How Search Engines Reinforce Racism. New York: New York University
Press, 2018. Accessed April 25, 2020. ProQuest Ebook Central.
Created from unh on 2020-04-25 07:19:27.
Copyright © 2018. New York University Press. All rights reserved.
Figure I.2. Google Images results for the keyword “gorillas,” April 7, 2016.
Figure I.3. Google Maps search on “N*gga House” leads to the White House,
April 7, 2016.
7
Noble, Safiya Umoja. Algorithms of Oppression : How Search Engines Reinforce Racism. New York: New York University
Press, 2018. Accessed April 25, 2020. ProQuest Ebook Central.
Created from unh on 2020-04-25 07:19:27.
Copyright © 2018. New York University Press. All rights reserved.
Figure I.4. Tweet by Deray McKesson about Google Maps search and the White
House, 2015.
8
Noble, Safiya Umoja. Algorithms of Oppression : How Search Engines Reinforce Racism. New York: New York University
Press, 2018. Accessed April 25, 2020. ProQuest Ebook Central.
Created from unh on 2020-04-25 07:19:27.
Introduction
|
9
Figure I.5. Standard Google’s “related” searches associates “Michelle Obama” with the
term “ape.”
a Google spokesperson told U.S. News in an email late Tuesday. ‘Our
teams are working to fix this issue quickly.’”7
Copyright © 2018. New York University Press. All rights reserved.
***
These human and machine errors are not without consequence, and
there are several cases that demonstrate how racism and sexism are
part of the architecture and language of technology, an issue that needs
attention and remediation. In many ways, these cases that I present are
specific to the lives and experiences of Black women and girls, people
largely understudied by scholars, who remain ever precarious, despite
our living in the age of Oprah and Beyoncé in Shondaland. The implications of such marginalization are profound. The insights about sexist
or racist biases that I convey here are important because information
organizations, from libraries to schools and universities to governmental
agencies, are increasingly reliant on or being displaced by a variety of
web-based “tools” as if there are no political, social, or economic consequences of doing so. We need to imagine new possibilities in the area of
information access and knowledge generation, particularly as headlines
about “racist algorithms” continue to surface in the media with limited
discussion and analysis beyond the superficial.
Noble, Safiya Umoja. Algorithms of Oppression : How Search Engines Reinforce Racism. New York: New York University
Press, 2018. Accessed April 25, 2020. ProQuest Ebook Central.
Created from unh on 2020-04-25 07:19:27.
Copyright © 2018. New York University Press. All rights reserved.
10
|
Introduction
Inevitably, a book written about algorithms or Google in the twentyfirst century is out of date immediately upon printing. Technology is
changing rapidly, as are technology company configurations via mergers, acquisitions, and dissolutions. Scholars working in the fields of
information, communication, and technology struggle to write about
specific moments in time, in an effort to crystallize a process or a phenomenon that may shift or morph into something else soon thereafter.
As a scholar of information and power, I am most interested in communicating a series of processes that have happened, which provide
evidence of a constellation of concerns that the public might take up
as meaningful and important, particularly as technology impacts social
relations and creates unintended consequences that deserve greater attention. I…
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