Reading: the power chapter

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Read The Power Chapter from Data Feminism and then do the following:

  1. Summarize one example from the reading and explain how the example demonstrates a power dynamic that occurs when people work with data. (4-6 sentences)
  2. How do the lessons from data feminism relate to the work we do in data visualization?  (2-5 sentences)

Warning: This article contains content about violence against women and people of color. If you don’t think you can complete the assignment contact the professor for an alternative assignment.

The original source of the article can be found here:  The Power Chapter from ( Data Feminism  By Catherine D’Ignazio ( and Lauren Klein(

Principle #1 of Data Feminism is

to Examine Power. Data feminism

begins by analyzing how power

operates in the world.
by Catherine D’Ignazio and Lauren Klein
Published on Mar 16, 2020

Principle: Examine

Data feminism begins by analyzing how power operates in the world.

When tennis star Serena Williams disappeared from Instagram in early September
2017, her six million followers assumed they knew what had happened. Several months
earlier, in March of that year, Williams had accidentally announced her pregnancy to the
world via a bathing suit selfie and a caption that was hard to misinterpret: “20 weeks.”
Now, they thought, her baby had finally arrived.

But then they waited, and waited some more. Two weeks later, Williams finally
reappeared, announcing the birth of her daughter and inviting her followers to watch a
video that welcomed Alexis Olympia Ohanian Jr. to the world.1 The video was a
montage of baby bump pics interspersed with clips of a pregnant Williams playing tennis
and having cute conversations with her husband, Reddit cofounder Alexis Ohanian, and
then, finally, the shot that her fans had been waiting for: the first clip of baby Olympia.
Williams was narrating: “So we’re leaving the hospital,” she explains. “It’s been a long

time. We had a lot of complications. But look who we got!” The scene fades to white,
and the video ends with a set of stats: Olympia’s date of birth, birth weight, and number
of grand slam titles: 1. (Williams, as it turned out, was already eight weeks pregnant
when she won the Australian Open earlier that year.)

Williams’s Instagram followers were, for the most part, enchanted. But soon, the
enthusiastic congratulations were superseded by a very different conversation. A
number of her followers—many of them Black women like Williams herself—fixated on
the comment she’d made as she was heading home from the hospital with her baby girl.
Those “complications” that Williams experienced—other women had had1Dilrukshi
Gamage them too. In Williams’s case, the complications had been life-threatening, and
her self-advocacy in the hospital played a major role in her survival.

On Williams’s Instagram feed, dozens of women began posting their own experiences
of childbirth gone horribly wrong. A few months later, Williams returned to social
media—Facebook, this time—to continue the conversation (figure 1.1). Citing a 2017
statement from the US Centers for Disease Control and Prevention (CDC), Williams
wrote that “Black women are over 3 times more likely than white women to die from
pregnancy- or childbirth-related causes.”2

These disparities were already well-known to Black-women-led reproductive justice
groups like SisterSong, the Black Mamas Matter Alliance, and Raising Our Sisters
Everywhere (ROSE), some of whom had been working on the maternal health crisis for
decades. Williams helped to shine a national spotlight on them. The mainstream media
also recently had begun to pay more attention to the crisis as well. A few months earlier,
Nina Martin of the investigative journalism outfit ProPublica, working with Renee
Montagne of NPR, had reported on the same phenomenon.3 “Nothing Protects Black
Women from Dying in Pregnancy and Childbirth,” the headline read. In addition to the
study cited by Williams, Martin and Montagne cited a second study from 2016, which
showed that neither education nor income level—the factors usually invoked when
attempting to account for healthcare outcomes that diverge along racial lines—impacted
the fates of Black women giving birth.4 On the contrary, the data showed that Black
women with college degrees suffered more severe complications of pregnancy and
childbirth than white women without high school diplomas.

A screenshot of a facebook post from Serena Williams on January 15, 2018, with the

following caption:

“I didn’t expect that sharing our family’s story of Olympia’s birth and all of complications

after giving birth would start such an outpouring of discussion from women — especially

black women — who have faced similar complications and women whose problems go


These aren’t just stories: according to the CDC, (Center for Disease Control) black

women are over 3 times more likely than White women to die from pregnancy- or

childbirth-related causes. We have a lot of work to do as a nation and I hope my story

can inspire a conversation that gets us to close this gap.

Let me be clear: EVERY mother, regardless of race, or background deserves to have a

healthy pregnancy and childbirth. I personally want all women of all colors to have the

best experience they can have. My personal experience was not great but it was MY

experience and I’m happy it happened to me. It made me stronger and it made me

appreciate women — both women with and without kids — even more. We are powerful!!!

I want to thank all of you who have opened up through online comments and other

platforms to tell your story. I encourage you to continue to tell those stories. This helps.

We can help others. Our voices are our power.”

So what were these complications, more precisely? And how many women had actually
died as a result? Nobody was counting. A 2014 United Nations report, coauthored by
SisterSong, described the state of data collection on maternal mortality in the United
States as “particularly weak.”5 The situation hadn’t improved in 2017, when ProPublica
began its reporting. In 2018, USA Todayinvestigated these racial disparities, and found
what was an even more fundamental problem: there was still no national system for
tracking complications sustained in pregnancy and childbirth, even though similar
systems had long been in place for tracking any number of other health issues, such as
teen pregnancy, hip replacements, or heart attacks.6 They also found that there was still
no reporting mechanism for ensuring that hospitals follow national safety standards, as
is required for both hip surgery and cardiac care. “Our maternal data is embarrassing,”
stated Stacie Geller, a professor of obstetrics and gynecology at the University of
Illinois, when asked for comment. The chief of the CDC’s Maternal and Infant Health
branch, William Callaghan, makes the significance of this “embarrassing” data more
clear: “What we choose to measure is a statement of what we value in health,” he

explains.7 We might edit his statement to add that it’s a measure of who we value in
health, too.1Sarah Wu8

Why did it take the near-death of an international sports superstar for the media to begin
paying attention to an issue that less famous Black women had been experiencing and
organizing around for decades? 1Annabel LeeWhy did it take reporting by the
predominantly white mainstream press for US cities and states to begin collecting data
on the issue?9 Why are those data still not viewed as big enough, statistically significant
enough, or of high enough quality for those cities and states, and other public
institutions, to justify taking action? And why didn’t those institutions just
#believeblackwomen in the first place?10

The answers to these questions are directly connected to larger issues of power and
privilege. Williams recognized as much when asked by Glamour

(Links to an external site.)

magazine about the fact that she had to demand that her medical team perform
additional tests in order to diagnose her own postnatal complications—and because she
was Serena Williams, twenty-three-time grand slam champion, they complied.11“If I
wasn’t who I am, it could have been me,” she told Glamour, referring to the fact that the
privilege she experienced as a tennis star intersected with the oppression she
experienced as a Black woman, enabling her to avoid becoming a statistic herself. As
Williams asserted, “that’s not fair.”1Sarah Wu12

Needless to say, Williams is right. It’s absolutely not fair. So how do we mitigate this
unfairness? We begin by examining systems of power and how they intersect—like how
the influences of racism, sexism, and celebrity came together first to send Williams into
a medical crisis and then, thankfully, to keep her alive. The complexity of these
intersections is the reason that examine power is the first principle of data feminism,
and the focus of this chapter. Examining power means naming and explaining the forces
of oppression that are so baked into our daily lives—and into our datasets, our
databases, and our algorithms—that we often don’t even see them. 1Sarah WuSeeing
oppression is especially hard for those of us who occupy positions of privilege. But once
we identify these forces and begin to understand how they exert their potent force, then
many of the additional principles of data feminism—like challenging power (chapter 2),
embracing emotion (chapter 3), and making labor visible (chapter 7)—become easier to
undertake.1Sara Blumenstein

Power and the Matrix of

But first, what do we mean by power? We use the term power to describe the current
configuration of structural privilege and structural oppression1A they/them Pollicino, in
which some groups experience unearned advantages11—because various systems
have been designed by people like them and work for people them—and other groups
experience systematic disadvantages—because those same systems were not
designed by them or with people like them in mind1Chongjiu Gao. These mechanisms
are complicated, and there are “few pure victims and oppressors1Sarah Wu,” notes
influential sociologist Patricia Hill Collins. In her landmark text, Black Feminist Thought,
first published in 1990, Collins proposes the concept of the matrix of domination 1Peem
Lerdpto explain how systems of power are configured and experienced.13 It consists of
four domains: the structural, the disciplinary, the hegemonic, and the
interpersonal.1Sara Blumenstein Her emphasis is on the intersection of gender and
race, but she makes clear that other dimensions of identity (sexuality, geography, ability,
etc.) also result in unjust oppression, or unearned privilege, that become apparent
across the same four domains.

The structural domain is the arena of laws and policies, along with schools and
institutions that implement them. This domain organizes and codifies oppression. Take,
for example, the history of voting rights in the United States. The US Constitution did not
originally specify who was authorized to vote, so various states had different policies
that reflected their local politics. Most had to do with owning property, which,
conveniently, only men could do. But with the passage of the Fourteenth Amendment in
1868, which granted the rights of US citizenship to those who had been enslaved, the
nature of those rights—including voting—were required to be spelled out at the national
level for the first time. More specifically, voting was defined as a right reserved for “male
citizens.” This is a clear instance of codified oppression in the structural domain.

Table 1.1: The four domains of the matrix of domination14

Structural domain

Organizes oppression: laws
and policies.

Disciplinary domain

Administers and manages oppression. Implements
and enforces laws and policies.

Hegemonic domain

Circulates oppressive ideas:
culture and media.

Interpersonal domain

Individual experiences of oppression.

It would take until the passage of the Nineteenth Amendment in 1920 for most (but not
all) women to be granted the right to vote.15 Even still, many state voting laws
continued to include literacy tests, residency requirements, and other ways to indirectly
exclude people who were not property-owning white men. These restrictions persist
today, in the form of practices11 like dropping names from voter rolls, requiring photo
IDs, and limits to early voting—the burdens of which are felt disproportionately by
low-income people, people of color, and others who lack the time or resources to jump
through these additional bureaucratic hoops.16 This is the disciplinary domain that
Collins names: the domain that administers and manages oppression through
bureaucracy and hierarchy, rather than through laws that explicitly encode inequality on
the basis of someone’s identity.17

Neither of these domains would be possible without the hegemonic domain, which deals
with the realm of culture, media, and ideas.1Chongjiu GaoDiscriminatory policies and
practices in voting can only be enacted in a world that already circulates oppressive
ideas about, for example, who counts as a citizen in the first place. Consider an
anti-suffragist pamphlet from the 1910s that proclaims, “You do not need a ballot to
clean out your sink spout.”18Pamphlets like these, designed to be literally passed from
hand to hand, reinforced preexisting societal views about the place of women in society.
Today, we have animated GIFs instead of paper pamphlets, but the hegemonic function
is the same: to consolidate ideas about who is entitled to exercise power and who is

The final part of the matrix of domination is the interpersonal domain, which influences
the everyday experience of individuals in the world. How would you feel if you were a
woman who read that pamphlet, for example? Would it have more or less of an impact if
a male family member gave it to you? Or, for a more recent example, how would you
feel if you took time off from your hourly job to go cast your vote, only to discover when
you got there that your name had been purged from the official voting roll or that there

was a line so long that it would require that you miss half a day’s pay, or stand for hours
in the cold, or … the list could go on. These are examples of how it feels to know that
systems of power are not on your side and, at times, are actively seeking to take away
the small amount of power that you do possess.19

The matrix of domination works to uphold the undue privilege of dominant groups while
unfairly oppressing minoritized groups. What does this mean? Beginning in this chapter
and continuing throughout the book, we use the term minoritized to describe groups of
people who are positioned in opposition to a more powerful social group. While the term
minority describes a social group that is comprised of fewer people, minoritized1A
they/them Pollicino indicates that a social group is actively devalued and oppressed by
a dominant group, one that holds more economic, social, and political power. With
respect to gender, for example, men constitute the dominant group, while all other
genders constitute minoritized groups. This remains true even as women actually
constitute a majority of the world population. Sexism is the term that names this form of
oppression. In relation to race, white people constitute the dominant group (racism); in
relation to class, wealthy and educated people constitute the dominant group
(classism); and so on.20

Using the concept of the matrix of domination and the distinction between dominant and
minoritized groups, we can begin to examine how power unfolds in and around data.
This often means asking uncomfortable questions: who is doing the work of data
science (and who is not)? Whose goals are prioritized in data science (and whose are
not)? And who benefits from data science (and who is either overlooked or actively
harmed)?21 These questions are uncomfortable because they unmask the inconvenient
truth that there are groups of people who are disproportionately benefitting from data
science, and there are groups of people who are disproportionately harmed1Sarah Wu.
Asking these who questions allows us, as data scientists ourselves, to start to see how
privilege is baked into our data practices and our data products.22

Data Science by Whom?
It is important to acknowledge the elephant in the server room: the demographics of
data science (and related occupations like software engineering and artificial
intelligence research) do not represent the population as a whole1Sara Blumenstein.
According to the most recent data from the US Bureau of Labor Statistics, released in
2018, only 26 percent of those in “computer and mathematical occupations” are
women.23 And across all of those women, only 12 percent are Black or Latinx women,
even though Black and Latinx women make up 22.5 percent of the US population.24 A
report by the research group AI Now about the diversity crisis in artificial intelligence

notes that women comprise only 15 percent of AI research staff at Facebook and 10
percent at Google.25 These numbers are probably not a surprise. The more surprising
thing is that those numbers are getting worse, not better. According to a research report
published by the American Association of University Women in 2015, women computer
science graduates in the United States peaked in the mid-1980s at 37 percent, and we
have seen a steady decline in the years since then to 26 percent today (figure 1.2).26
As “data analysts” (low-status number crunchers) have become rebranded as “data
scientists” (high status researchers), women are being pushed out in order to make
room for more highly valued and more highly compensated men.27

A graphical representation
of the proportion of men and women awarded computer science (CS) degrees in the
U.S. from 1970 to 2010. The horizontal axis lists all the years from 1970 to 2010,
increasing in 5-year increments, and the vertical axis shows the percentage and the title
of the graph reads “Computer Science, The Man Factory.”In the graph, there is a line
graph showing the percentage of men who were awarded CS degrees. Below this line,
the graph is shaded grey which represents the proportion of men and above the line,
the graph is shaded light purple, which represents the proportion of women. The ratio
starts at around 85% men / 15% women in 1970, then the share of women increases to
63% men / 37% women in 1984 (At this point, there is a caption which reads “Women
received 37% of CS degrees in 1984, the closest we have come to gender parity”), and
then that share decreases back to around 80% men / 20% women in 2010. Throughout
the entire timeline, the amount of men awarded CS degrees is disproportionately larger
than the amount of women.

There are not disparities only along gender lines in the higher education pipeline. The
same report noted specific underrepresentation for Native American women, multiracial

women, white women, and all Black and Latinx people. So is it really a surprise that
each day brings a new example of data science being used to disempower and oppress
minoritized groups? In 2018, it was revealed that Amazon had been developing an
algorithm to screen its first-round job applicants. But because the model had been
trained on the resumes of prior applicants, who were predominantly male, it developed
an even stronger preference for male applicants. It downgraded resumes with the word
women and graduates of women’s colleges. Ultimately, Amazon had to cancel the
project.28 This example reinforces the work of Safiya Umoja Noble, whose book,
Algorithms of Oppression, has shown how both gender and racial biases are encoded
into some of the most pervasive data-driven systems—including Google search, which
boasts over five billion unique web searches per day. Noble describes how, as recently
as 2016, comparable searches for “three Black teenagers” and “three white teenagers”
turned up wildly different representations of those teens. The former returned mugshots,
while the latter returned wholesome stock photography.29

The problems of gender and racial bias in our information systems are complex, but
some of their key causes are plain as day: the data that shape them, and the models
designed to put those data to use, are created by small groups of people and then
scaled up to users around the globe. But those small groups are not at all
representative of the globe as a whole, nor even of a single city in the United States.
When data teams are primarily composed of people from dominant groups, those
perspectives come to exert outsized influence on the decisions being made—to the
exclusion of other identities and perspectives. This is not usually intentional; it comes
from the ignorance of being on top. We describe this deficiency as a privilege hazard.

How does this come to pass? Let’s take a minute to imagine what life is like for
someone who epitomizes the dominant group in data science: a straight, white,
cisgender man with formal technical credentials who lives in the United States. When he
looks for a home or applies for a credit card, people are eager for his business. People
smile when he holds his girlfriend’s hand in public. His body doesn’t change due to
childbirth or breastfeeding, so he does not need to think about workplace
accommodations. He presents his social security number in jobs as a formality, but it
never hinders his application from being processed or brings him unwanted attention.
The ease with which he traverses the world is invisible to him because it has been
designed for people just like him. He does not think about how life might be different for
everyone else. In fact, it is difficult for him to imagine that at all.

This is the privilege hazard: the phenomenon that makes those who occupy the most
privileged positions among us—those with good educations, respected credentials, and
professional accolades—so poorly equipped to recognize instances of oppression in the
world.30 They lack what Anita Gurumurthy, executive director of IT for Change, has
called “the empiricism of lived experience.”31 And this lack of lived experience—this

evidence of how things truly are—profoundly limits their ability to foresee and prevent
harm, to identify existing problems in the world, and to imagine possible solutions.

The privilege hazard occurs at the level of the individual—in the interpersonal domain of
the matrix of domination—but it is much more harmful in aggregate because it reaches
the hegemonic, disciplinary and structural domains as well. So it matters deeply that
data science and artificial intelligence are dominated by elite white men because it
means there is a collective privilege hazard 1A they/them Pollicinoso great that it would
be a profound surprise if they could actually identify instances of bias prior to unleashing
them onto the world. 1Sara BlumensteinSocial scientist Kate Crawford has advanced
the idea that the biggest threat from artificial intelligence systems is not that they will
become smarter than humans, but rather that they will hard-code sexism, racism, and
other forms of discrimination into the digital infrastructure1A they/them Pollicinoof our

What’s more, the same cis het white men responsible for designing those systems lack
the ability to detect harms and biases in their systems once they’ve been released into
the world.33 In the case of the “three teenagers” Google searches, for example, it was a
young Black teenager that pointed out the problem and a Black scholar who wrote
about the problem. The burden consistently falls upon those more intimately familiar
with the privilege hazard—in data science as in life—to call out the creators of those
systems for their limitations.

For example, Joy Buolamwini, a Ghanaian-American graduate student at MIT, was
working on a class project using facial-analysis software.34 But there was a
problem—the software couldn’t “see” Buolamwini’s dark-skinned face (where “seeing”
means that it detected a face in the image, like when a phone camera draws a square
around a person’s face in the frame). It had no problem seeing her lighter-skinned
collaborators. She tried drawing a face on her hand and putting it in front of the camera;
it detected that. Finally, Buolamwini put on a white mask, essentially going in “whiteface”
(figure 1.3).35 The system detected the mask’s facial features perfectly.

Digging deeper into the code and benchmarking data behind these systems,
Buolamwini discovered that the dataset on which many of facial-recognition algorithms
are tested contains 78 percent male faces and 84 percent white faces. When she did an
intersectional breakdown of another test dataset—looking at gender and skin type
together—only 4 percent of the faces in that dataset were women and dark-skinned. In
their evaluation of three commercial systems, Buolamwini and computer scientist Timnit
Gebru showed that darker-skinned women were up to forty-four times more likely to be
misclassified than lighter-skinned males.36 It’s no wonder that the software failed to
detect Buolamwini’s face: both the training data and the benchmarking data relegate
women of color to a tiny fraction of the overall dataset.37

Figure 1.3: Joy Buolamwini found that she had to put on a white mask for the facial

detection program to “see” her face. Buolamwini is now founder of the Algorithmic

Justice League. Courtesy of Joy Buolamwini. Credit: Courtesy of Joy Buolamwini.

This is the privilege hazard in action—that no coder, tester, or user of the software had
previously identified such a problem or even thought to look. Buolamwini’s work has
been widely covered by the national media (by the

(Links to an external site.)

New York Times, by , by the

(Links to an external site.)

CNN Economist, by

(Links to an external site.)

Bloomberg BusinessWeek, and others) in articles that typically contain a hint of
shock.38 This is a testament to the social, political, and technical importance of the
work, as well as to how those in positions of power—not just in the field of data science,
but in the mainstream media, in elected government, and at the heads of
corporations—are so often surprised to learn that their “intelligent technologies” are not
so intelligent after all. (They need to read data journalist Meredith Broussard’s book

Artificial Unintelligence).39 For another example, think back to the introduction of this
book, where we quoted Shetterly as reporting that Christine Darden’s white male
manager was “shocked at the disparity” between the promotion rates of men and
women. We can speculate that Darden herself wasn’t shocked, just as Buolamwini and
Gebru likely were not entirely shocked at the outcome of their study either. When
sexism, racism, and other forms of oppression are publicly unmasked, it is almost never
surprising to those who experience them.2Annabel Lee, A they/them Pollicino

For people in positions of power and privilege, issues of race and gender and class and
ability—to name only a few—are OPP: other people’s problems. Author and antiracist
educator Robin DiAngelo describes instances like the “shock” of Darden’s boss or the
surprise in the media coverage of Buolamwini’s various projects as a symptom of the
“racial innocence” of white people.40 In other words, those who occupy positions of
privilege in society are able to remain innocent of that privilege. Race becomes
something that only people of color have. Gender becomes something that only women
and nonbinary people have. Sexual orientation becomes something that all people
exceptheterosexual people have. And so on. A personal anecdote might help illustrate
this point. When we published the first draft of this book online, Catherine told a
colleague about it. His earnestly enthusiastic response was, “Oh great! I’ll show it to my
female graduate students!” To which Catherine rejoined, “You might want to show it to
your other students, too.”

If things were different—if the 79 percent of engineers at Google who are male were
specifically trained in structural oppression before building their data systems (as social
workers are before they undertake social work)—then their overrepresentation might be
very slightly less of a problem.41 But in the meantime, the onus falls on the individuals
who already feel the adverse effects of those systems of power to prove, over and over
again, that racism and sexism exist—in datasets, in data systems, and in data science,
as in everywhere else.

Buolamwini and Gebru identified how pale and male faces were overrepresented in
facial detection training data. Could we just fix this problem by diversifying the data set?
One solution to the problem would appear to be straightforward: create a more
representative set of training and benchmarking data for facial detection models. In fact,
tech companies are starting to do exactly this. In January 2019, IBM released a
database of one million faces called Diversity in Faces

(Links to an external site.)

(DiF).42 In another example, journalist Amy Hawkins details how CloudWalk, a startup
in China in need of more images of faces of people of African descent, signed a deal
with the Zimbabwean government for it to provide the images the company was
lacking.43 In return for sharing its data, Zimbabwe will receive a national facial database

and “smart” surveillance infrastructure that it can install in airports, railways, and bus

It might sound like an even exchange, but Zimbabwe has a dismal record on human
rights. Making things worse, CloudWalk provides facial recognition technologies to the
Chinese police—a conflict of interest so great that the global nonprofit Human Rights
Watch voiced its concern about the deal.44 Face harvesting is happening in the US as
well. Researchers Os Keyes, Nikki Stevens and Jacqueline Wernimont have shown how
immigrants, abused children, and dead people are some of the groups whose faces
have been used to train software—without their consent.45 So is a diverse database of
faces really a good idea? Voicing his concerns in response to the announcement of
Buolamwini and Gebru’s 2018 study on Twitter, an Indigenous Marine veteran shot
back, “I hope facial recognition software has a problem identifying my face too. That’d
come in handy when the police come rolling around with their facial recognition truck at
peaceful demonstrations of dissent, cataloging all dissenters for ‘safety and
security.’”1nawal zahzah46

Better detection of faces of color cannot be characterized as an unqualified good. More
often than not, it is enlisted in the service of increased oppression, greater surveillance,
and targeted violence. Buolamwini understands these potential harms and has
developed an approach that works across all four domains of the matrix of domination
to address the underlying issues of power that are playing out in facial analysis
technology. Buolamwini and Gebru first quantified the disparities in the dataset—a
technical audit, which falls in the disciplinary domain of the matrix of domination. Then,
Buolamwini went on to launch the Algorithmic Justice League, an organization that
works to highlight and intervene in instances of algorithmic bias. On behalf of the AJL,
Buolamwini1A they/them Pollicino has produced viral poetry projects and given TED
talks—taking action in the hegemonic domain, the realm of culture and ideas. She has
advised on legislation and professional standards for the field of computer vision and
called for a moratorium on facial analysis in policing on national media and in
Congress.47 These are actions operating in the structural domain of the matrix of
domination—the realm of law and policy. Throughout these efforts, the AJL works with
students and researchers to help guide and shape their own work—the interpersonal
domain. Taken together, Buolamwini’s various initiatives demonstrate how any “solution”
to bias in algorithms and datasets must tackle more than technical limitations. In
addition, they present a compelling model for the data scientist as public
intellectual—who, yes, works on technical audits and fixes, but also works on cultural,
legal, and political efforts too.

While equitable representation—in datasets and data science workforces—is important,
it remains window dressing if we don’t also transform the institutions that produce and
reproduce those biased outcomes in the first place. As doctoral health student Arrianna

Planey, quoting Robert M. Young, states, “A racist society will give you a racist
science.”48 We cannot filter out the downstream effects of sexism and racism without
also addressing their root cause.

Data Science for Whom?
One of the downstream effects of the privilege hazard—the risks incurred when people
from dominant groups create most of our data products—is not only that datasets are
biased or unrepresentative, but that they never get collected at all1Peem Lerdp. Mimi
Onuoha—an artist, designer, and educator—has long been asking who questions about
data science. Her project, The Library of Missing Datasets (figure 1.4), is a list of
datasets that one might expect to already exist in the world, because they help to
address pressing social issues, but that in reality have never been created. The project
exists as a website and as an art object. The latter consists of a file cabinet filled with
folders labeled with phrases like: “People excluded from public housing because of
criminal records,” “Mobility for older adults with physical disabilities or cognitive
impairments,” and “Total number of local and state police departments using stingray
phone trackers (IMSI-catchers).” Visitors can tab through the folders and remove any
particular folder of interest, only to reveal that it is empty. They all are. The datasets that
should be there are “missing.”

Photograph of a Black woman’s hands sifting through a white file cabinet of empty
folders from The Library of Missing Datasets. Each folder is labeled with a dataset for
which data doesn’t currently exist.

By compiling a list of the datasets that are missing from our “otherwise data-saturated”
world, Onuoha explains, “we find cultural and colloquial hints of what is deemed
important” and what is not. “Spots that we’ve left blank reveal our hidden social biases
and indifferences,” she continues. And by calling attention to these datasets as
“missing,” she also calls attention to how the matrix of domination encodes these “social
biases and indifferences” across all levels of society.49 Along similar lines, foundations
like Data2X and books like Invisible Women have advanced the idea of a systematic
“gender data gap” due to the fact that the majority of research data in scientific studies
is based around men’s bodies. The downstream effects of the gender data gap range
from annoying—cell phones slightly too large for women’s hands, for example—to fatal.
Until recently, crash test dummies were designed in the size and shape of men, an
oversight that meant that women had a 47 percent higher chance of car injury than

The who question in this case is: Who benefits from data science and who is
overlooked1A they/them Pollicino? Examining those gaps can sometimes mean calling
out missing datasets, as Onuoha does; characterizing them, as Invisible Women does;
and advocating for filling them, as Data2X does. At other times, it can mean collecting
the missing data yourself. Lacking comprehensive data about women who die in
childbirth, for example, ProPublica decided to resort to

(Links to an external site.)

crowdsourcing to learn the names of the estimated seven hundred to nine hundred US
women who died in 2016.51 As of 2019, they’ve identified only 140. Or, for another
example: in 1998, youth living in Roxbury—a neighborhood known as “the heart of
Black culture in Boston”52—were sick and tired of inhaling polluted air. They led a
march demanding clean air and better data collection, which led to the creation of the

(Links to an external site.)

AirBeat community monitoring project.53

Scholars have proposed various names for these instances of ground-up data
collection, including counterdata or agonistic data collection, data activism, statactivism,
and citizen science (when in the service of environmental justice).54 Whatever it’s
called, it’s been going on for a long time. In 1895, civil rights activist and pioneering data
journalist Ida B. Wells assembled a set of statistics on the epidemic of lynching that was
sweeping the United States.55She accompanied her data with a meticulous exposé of

the fraudulent claims made by white people—typically, that a rape, theft, or assault of
some kind had occurred (which it hadn’t in most cases) and that lynching was a justified
response. Today, an organization named after Wells—the Ida B. Wells Society for
Investigative Reporting—continues her mission by training up a new generation of
journalists of color in the skills of data collection and analysis.56

A counterdata initiative in the spirit of Wells is taking place just south of the US border,
in Mexico, where a single woman is compiling a comprehensive dataset on femicides

(Links to an external site.)

—gender-related killings of women and girls.57 María Salguero, who also goes by the
name Princesa, has logged more than five thousand cases of femicide since 2016.58
Her work provides the most accessible information on the subject for journalists,
activists, and victims’ families seeking justice.

The issue of femicide in Mexico rose to global visibility in the mid-2000s with
widespread media coverage about the deaths of poor and working-class women in
Ciudad Juárez. A border town, Juárez is the site of more than three hundred
maquiladoras: factories that employ women to assemble goods and electronics, often
for low wages and in substandard working conditions. Between 1993 and 2005, nearly
four hundred of these women were murdered, with around a third of those murders
exhibiting signs of exceptional brutality or sexual violence. Convictions were made in
only three of those deaths. In response, a number of activist groups like Ni Una Más
(Not One More) and Nuestras Hijas de Regreso a Casa (Our Daughters Back Home)
were formed, largely motivated by mothers demanding justice for their daughters, often
at great personal risk to themselves.59

These groups succeeded in gaining the attention of the Mexican government, which
established a Special Commission on Femicide. But despite the commission and the
fourteen volumes of information about femicide that it produced, and despite a 2009
ruling against the Mexican state by the Inter-American Human Rights Court, and despite
a United Nations Symposium on Femicide in 2012, and despite the fact that sixteen
Latin American countries have now passed laws defining femicide—despite all of this,
deaths in Juárez have continued to rise.60 In 2009 a report pointed out that one of the
reasons that the issue had yet to be sufficiently addressed was the lack of
data.61Needless to say, the problem remains.

How might we explain the missing data around femicides in relation to the four domains
of power that constitute Collins’s matrix of domination? As is true in so many cases of
data collected (or not) about women and other minoritized groups, the collection
environment is compromised by imbalances of power.

The most grave and urgent manifestation of the matrix of domination is within the
interpersonal domain, in which cis and trans women become the victims of violence and
murder at the hands of men. Although law and policy (the structural domain) have
recognized the crime of femicide, no specific policies have been implemented to ensure
adequate information collection, either by federal agencies or local authorities. Thus the
disciplinary domain, in which law and policy are enacted, is characterized by a deferral
of responsibility, a failure to investigate, and victim blaming. This persists in a somewhat
recursive fashion because there are no consequences imposed within the structural
domain. 1nawal zahzahFor example, the Special Commission’s definition of femicide as
a “crime of the state” speaks volumes to how the government of Mexico is deeply
complicit through inattention and indifference.62

Of course, this inaction would not have been tolerated without the assistance of the
hegemonic domain—the realm of media and culture—which presents men as strong
and women as subservient, men as public and women as private, trans people as
deviating from “essential” norms, and nonbinary people as nonexistent altogether.
Indeed, government agencies have used their public platforms to blame victims.
Following the femicide of twenty-two-year-old Mexican student Lesvy Osorio in 2017,
researcher Maria Rodriguez-Dominguez documented how the Public Prosecutor’s
Office of Mexico City shared on social media that the victim was an alcoholic and drug
user who had been living out of wedlock with her boyfriend.63 This led to justified public
backlash, and to the hashtag #SiMeMatan (If they kill me), which prompted sarcastic
tweets such as “#SiMeMatan it’s because I liked to go out at night and drink a lot of

It is into this data collection environment, characterized by extremely asymmetrical
power relations, that María Salguero has inserted her femicides map. Salguero
manually plots a pin on the map for every femicide that she collects through media
reports or through crowdsourced contributions (figure 1.5a). One of her goals is to
“show that these victims [each] had a name and that they had a life,” and so Salguero
logs as many details as she can about each death. These include name, age,
relationship with the perpetrator, mode and place of death, and whether the victim was
transgender, as well as the full content of the news report that served as the source.
Figure 1.5b shows a detailed view for a single report from an unidentified transfemicide,
including the date, time, location, and media article about the killing. It can take
Salguero three to four hours a day to do this unpaid work. She takes occasional breaks
to preserve her mental health, and she typically has a backlog of a month’s worth of
femicides to add to the map.

Although media reportage and crowdsourcing are imperfect ways of collecting data, this
particular map, created and maintained by a single person, fills a vacuum created by
her national government. The map has been used to help find missing women, and

Salguero herself has testified before Mexico’s Congress about the scope of the
problem. Salguero is not affiliated with an activist group, but she makes her data
available to activist groups for their efforts. Parents of victims have called her to give
their thanks for making their daughters visible, and Salguero affirms this function as
well: “This map seeks to make visible the sites where they are killing us, to find patterns,
to bolster arguments about the problem, to georeference aid, to promote prevention and
try to avoid femicides.”


map of Mexico with colored markers to represent locations where femicides have

occurred. The color of the marker corresponds to the year in which the femicide

occurred: red for 2016, purple for 2017, and light blue for 2018. There is an immense

concentration of femicides near southern Mexico, and they become less concentrated

further away.

A zoomed in version of the femicide map over Ciudad Juarez, a Mexican city just south

of El Paso. A purple marker (representing a femicide of a trans woman from 2017) is

selected and a description box to the right of the map contains information about the

attack, including its date & time, its location, and a brief description. The description box

reads the following:

Nombre (Incident Title)

#Transfeminicidio Identidad Reservada

Fecha (Date)


Lugar (Place)

Pedro Meneses Hoyos, Ciudad Juárez, Chihuahua, 32730 México

Hechos (Description)


Juárez, Chih.- Un individuo que aparentemente pertenecía a la comunidad LGBT fue

localizado sin vida por la noche en un fraccionamiento ubicado al sur oriente de la

ciudad, reportaron las corporaciones policicas.

El cuerpo del hombre vestido de mujer y en avanzado estado de descomposición fue

encontrado en el fondo de un pozo de contención de aguas pluviales.

El occiso tení una bolsa de plástico en la cabeza, aunque personal de la Fiscalí

General del estado asegura no le pudieron encontrar huellas externas de violencia.

Al lugar de los hechos llegaron sus familiares y lo identificaron como Hilario Lopez Ruiz,

de quien no se proporcionó más información.

El cuerpo fue enviado al Servicio Médico Forense donde se le practicara la autopsia de

ley y determinar de esa manera las causas reales de su fallecimiento.





It is important to make clear that the example of missing data about femicides in Mexico
is not an isolated case, either in terms of subject matter or geographic location. The
phenomenon of missing data is a regular and expected outcome in all societies
characterized by unequal power relations, in which a gendered, racialized order is
maintained through willful disregard, deferral of responsibility, and organized neglect for
data and statistics about those minoritized bodies who do not hold power. So too are
examples of individuals and communities using strategies like Salguero’s to fill in the
gaps left by these missing datasets—in the United States as around the world.65 If
“quantification is representation,” as data journalist Jonathan Stray asserts, then this
offers one way to hold those in power accountable.1Sarah Wu Collecting counterdata
demonstrates how data science can be enlisted on behalf of individuals and
communities that need more power on their side.66

Data Science with Whose

Interests and Goals?
Far too often, the problem is not that data about minoritized groups are missing but the
reverse: the databases and data systems of powerful institutions are built on the
excessive surveillance of minoritized groups. This results in women, people of color,
and poor people, among others, being overrepresented in the data that these systems
are premised upon. In Automating Inequality, for example, Virginia Eubanks tells the
story of the Allegheny County Office of Children, Youth, and Families in western
Pennsylvania, which employs an algorithmic model to predict the risk of child abuse in
any particular home.67The goal of the model is to remove children from potentially
abusive households before it happens; this would appear to be a very worthy goal. As
Eubanks shows, however, inequities result. For wealthier parents, who can more easily
access private health care and mental health services, there is simply not that much
data to pull into the model. For poor parents, who more often rely on public resources,
the system scoops up records from child welfare services, drug and alcohol treatment
programs, mental health services, Medicaid histories, and more. Because there are far
more data about poor parents, they are oversampled in the model, and so their children
are overtargeted as being at risk for child abuse—a risk that results in children being
removed from their families and homes. Eubanks argues that the model “confuse[s]
parenting while poor with poor parenting.”

This model, like many, was designed under two flawed assumptions: (1) that more data
is always better1Heather Yang and (2) that the data are a neutral input. In practice,
however, the reality is quite different. The higher proportion of poor parents in the
database, with more complete data profiles, the more likely the model will be to find fault
with poor parents. And data are never neutral; they are always the biased output of
unequal social, historical, and economic conditions: this is the matrix of domination once
again.68 Governments can and do use biased data to marshal the power of the matrix
of domination in ways that amplify its effects on the least powerful in society. In this
case, the model becomes a way to administer and manage classism in the disciplinary
domain—with the consequence that poor parents’ attempts to access resources and
improve their lives, when compiled as data, become the same data that remove their
children from their care.

So this raises our next who question: Whose goals are prioritized in data science (and
whose are not)? In this case, the state of Pennsylvania prioritized its bureaucratic goal
of efficiency, which is an oft-cited reason for coming up with a technical solution to a
social and political dilemma. Viewed from the perspective of the state, there were simply
not enough employees to handle all of the potential child abuse cases, so it needed a
mechanism for efficiently deploying limited staff—or so the reasoning goes. This is what
Eubanks has described as a scarcity bias: the idea that there are not enough resources
for everyone so we should think small and allow technology to fill the gaps. Such
thinking, and the technological “solutions” that result, often meet the goals of their
creators—in this case, the Allegheny County Office of Children, Youth, and
Families—but not the goals of the children and families that it purports to serve.1Sara

Corporations also place their own goals ahead of those of the people their products
purport to serve, supported by their outsize wealth and the power that comes with it. For
example, in 2012, the New York Times published an explosive article by Charles
Duhigg, “How Companies Learn Your Secrets,”69 which soon became the stuff of
legend in data and privacy circles. Duhigg describes how Andrew Pole, a data scientist
working at Target, was approached by men from the marketing department who asked,
“If we wanted to figure out if a customer is pregnant, even if she didn’t want us to know,
can you do that?”70 He proceeded to synthesize customers’ purchasing histories with
the timeline of those purchases to give each customer a so-called pregnancy prediction
score (figure 1.6).71 Evidently, pregnancy is the second major life event, after leaving
for college, that determines whether a casual shopper will become a customer for life.

Target turned around and put Pole’s pregnancy detection model into action in an
automated system that sent discount coupons to possibly pregnant customers.
Win-win—or so the company thought, until a Minneapolis teenager’s dad saw the
coupons for baby clothes that she was getting in the mail and marched into his local

Target to read the manager the riot act. Why was his daughter getting coupons for
pregnant women when she was only a teen?!

It turned out that the young woman was indeed pregnant. Pole’s model informed Target
before the teenager informed her family. By analyzing the purchase dates of
approximately twenty-five common products, such as unscented lotion and large bags
of cotton balls, the model found a set of purchase patterns that were highly correlated
with pregnancy status and expected due date. But the win-win quickly became a
lose-lose, as Target lost the trust of its customers in a PR disaster and the Minneapolis
teenager lost far worse: her control over information related to her own body and her

A screenshot from statistician Andrew Pole’s presentation at Predictive Analytics World

about Target’s pregnancy detection model in October 2010. The powerpoint slide reads

the following:

Acquire and convert prenatal mothers before they have their baby.

Analytics.Develop a model to predict if a woman is likely to be pregnant with child.

Data for analysis. Date of purchase and sales of key baby items in store or online, baby

registrant, browse for baby products online, guest age, and children.

Result. Identified 30% more guests to contact with profitable acquisition mailer.

This story has been told many times: first by Pole, the statistician; then by Duhigg, the
New York Times journalist; then by many other commentators on personal privacy and
corporate overreach. But it is not only a story about privacy: it is also a story about
gender injustice—about how corporations approach data relating to women’s bodies
and lives, and about how corporations approach data relating to minoritized populations
more generally. Whose goals are prioritized in this case? The corporation’s, of course.
For Target, the primary motivation was maximizing profit, and quarterly financial reports
to the board are the measurement of success. Whose goals are notprioritized? The
teenager’s and those of every other pregnant woman out there.1Sara Blumenstein

How did we get to the point where data science is used almost exclusively in the service
of profit (for a few), surveillance (of the minoritized), and efficiency (amidst scarcity)? It’s
worth stepping back to make an observation about the organization of the data
economy: data are expensive and resource-intensive, so only already powerful
institutions—corporations, governments, and elite research universities—have the
means to work with them at scale. These resource requirements result in data science
that serves the primary goals of the institutions themselves. 1Peem LerdpWe can think
of these goals as the three Ss: science (universities), surveillance (governments), and
selling (corporations). This is not a normative judgment (e.g., “all science is bad”) but
rather an observation about the organization of resources. If science, surveillance, and
selling are the main goals that data are serving, because that’s who has the money,
then what other goals and purposes are going underserved?

Let’s take “the cloud” as an example. As server farms have taken the place of paper
archives, storing data has come to require large physical spaces. A project by the
Center for Land Use Interpretation (CLUI) makes this last point plain (figure 1.7). In
2014, CLUI set out to map and photograph data centers around the United States, often
in those seemingly empty in-between areas we now call exurbs. In so doing, it called
attention to “a new kind of physical information architecture” sprawling across the United
States: “windowless boxes, often with distinct design features such as an appliqué of
surface graphics or a functional brutalism, surrounded by cooling systems.” The
environmental impacts of the cloud—in the form of electricity and air conditioning—are
enormous. A 2017 Greenpeace report estimated that the global IT sector, which is
largely US-based, accounted for around 7 percent of the world’s energy use. This is
more than some of largest countries in the world, including Russia, Brazil, and Japan.72
Unless that energy comes from renewable sources (which the Greenpeace report
shows that it does not), the cloud has a significant accelerating impact on global climate

So the cloud is not light and it is not airy. And the cloud is not cheap. The cost of
constructing Facebook’s newest data center in Los Lunas, New Mexico, is expected to
reach $1 billion.73 The electrical cost of that center alone is estimated at $31 million per
year.74 These numbers return us to the question about financial resources: Who has
the money to invest in centers like these? Only powerful corporations like Facebook and
Target, along with wealthy governments and elite universities, have the resources to
collect, store, maintain, analyze, and mobilize the largest amounts of data. Next, who is
in charge of these well-resourced institutions? Disproportionately men, even more
disproportionately white men, and even more than that, disproportionately rich white
men. 2Sarah Wu, nawal zahzahWant the data on that? Google’s Board of Directors is
comprised of 82 percent white men. Facebook’s board is 78 percent male and 89
percent white. The 2018 US Congress was 79 percent male—actually a better
percentage than in previous years—and with a median net worth of five times more than
the average American household.75 These are the people who experience the most
privilege within the matrix of domination, and they are also the people who benefit the
most from the current status quo.76

Photograph of the side-view of a data center in North Bergen, NJ under cloudy skies
and in front of a row of bushes. The data center is a 3-story white building with orange
stripes and blue tinted windows.

Photograph of a data center in North Bergen, NJ.

Photograph of a data center in Dalles, OR during a bright, sunny day with a few clouds.
The data center is in a fairly rural area, with an abandoned construction site to the left of
it, large green hilly mountains behind it, and telephone lines running along the side of

the building.

Photograph of a data center in Lockport, NY on a bright, cloudy day, in front of an empty
road. The data center is a 4-story white building with cyan tinted windows and has a
gated fence surrounding the back of the building.

Photograph of a data center in Ashburn, VA during a sunny day with clear skies and in
front of a field of grass. The data center is split into several buildings, all with a light
yellow color.

In the past decade or so, many of these men at the top have described data as “the new
oil.”77 It’s a metaphor that resonates uncannily well—even more than they likely
intended. The idea of data as some sort of untapped natural resource clearly points to
the potential of data for power and profit once they are processed and refined, but it
also helps highlight the exploitative dimensions of extracting data from their
source—people—as well as their ecological cost. Just as the original oil barons were
able to use their riches to wield outsized power in the world (think of John D.
Rockefeller, J. Paul Getty, or, more recently, the Koch brothers), so too do the Targets of
the world use their corporate gain to consolidate control over their customers. But unlike
crude oil, which is extracted from the earth and then sold to people, data are both
extracted from people and sold back to them—in the form of coupons like the one the
Minneapolis teen received in the mail, or far worse.78

This extractive system creates a profound asymmetry between who is collecting,
storing, and analyzing data, and whose data are collected, stored, and analyzed.79 The
goals that drive this process are those of the corporations, governments, and
well-resourced universities that are dominated by elite white men. And those goals are
neither neutral nor democratic—in the sense of having undergone any kind of
participatory, public process. On the contrary, focusing on those three Ss—science,
surveillance, and selling—to the exclusion of other possible objectives results in
significant oversights with life-altering consequences. Consider the Target example as
the flip side of the missing data on maternal health outcomes. Put crudely, there is no
profit to be made collecting data on the women who are dying in childbirth, but there is
significant profit in knowing whether women are pregnant.

How might we prioritize different goals and different people in data science? How might
data scientists undertake a feminist analysis of power in order to tackle bias at its
source? Kimberly Seals Allers, a birth justice advocate and author, is on a mission to do
exactly that in relation to maternal and infant care in the United States. She followed the
Serena Williams story with great interest and watched as Congress passed the
Preventing Maternal Deaths Act of 2018. This bill funded the creation of maternal health
review committees in every state and, for the first time, uniform and comprehensive
data collection at the federal level. But even as more data have begun to be collected
about maternal mortality, Seals Allers has remained frustrated by the public
conversation: “The statistics that are rightfully creating awareness around the Black
maternal mortality crisis are also contributing to this gloom and doom deficit narrative.
White people are like, ‘how can we save Black women?’ And that’s not the solution that
we need the data to produce.”80

flow chart (as a series of screenshots) which shows the sign-up process and the app

platform for Irth, a mobile app which helps brown and Black mothers find prenatal,
birthing, postpartum and pediatric reviews of care. The screenshots of the sign-up
process show the app asking users to input key identifying details such as race,
ethnicity, self-identity, relationship status, etc. There is also an optional page where
users can include information such as their religion and education level. The
screenshots of the app’s platform showcase the main features of the app, including
viewing reviews for specific doctors and nurses, as well as writing reviews based on the
user’s personal experiences.

Seals Allers—and her fifteen-year-old son, Michael—are working on their own
data-driven contribution to the maternal and infant health conversation: a platform and
app called Irth—from birth, but with the b for bias removed (figure 1.8). One of the major
contributing factors to poor birth outcomes, as well as maternal and infant mortality, is
biased care. Hospitals, clinics, and caregivers routinely disregard Black women’s
expressions of pain and wishes for treatment.81 As we saw, Serena Williams’s own
story almost ended in this way, despite the fact that she is an international tennis star.
To combat this, Irth operates like an intersectional Yelp for birth experiences. Users post
ratings and reviews of their prenatal, postpartum, and birth experiences at specific
hospitals and in the hands of specific caregivers. Their reviews include important details
like their race, religion, sexuality, and gender identity, as well as whether they felt that
those identities were respected in the care that they received. The app also has a
taxonomy of bias and asks users to tick boxes to indicate whether and how they may
have experienced different types of bias. Irth allows parents who are seeking care to
search for a review from someone like them—from a racial, ethnic, socioeconomic,
and/or gender perspective—to see how they experienced a certain doctor or hospital.

Seals Allers’s vision is that Irth will be both a public information platform, for individuals
to find better care, and an accountability tool, to hold hospitals and providers
responsible for systemic bias. Ultimately, she would like to present aggregated stories
and data analyses from the platform to hospital networks to push for change grounded
in women’s and parents’ lived experiences. “We keep telling the story of maternal
mortality from the grave,” she says. “We have to start preventing those deaths by
sharing the stories of people who actually lived.”82

Irth illustrates the fact that “doing good with data” requires being deeply attuned to the
things that fall outside the dataset—and in particular to how datasets, and the data
science they enable, too often reflect the structures of power of the world they draw
from. In a world defined by unequal power relations, which shape both social norms and
laws about how data are used and how data science is applied, it remains imperative to
consider who gets to do the “good” and who, conversely, gets someone else’s “good”
done to them.

Examine Power
Data feminism begins by examining how power operates in the world today. This
consists of asking who questions about data science: Who does the work (and who is
pushed out)? Who benefits (and who is neglected or harmed)? Whose priorities get
turned into products (and whose are overlooked)? These questions are relevant at the
level of individuals and organizations, and are absolutely essential at the level of
society. The current answer to most of these questions is “people from dominant
groups,” which has resulted in a privilege hazard so acute that it explains the near-daily
revelations about another sexist or racist data product or algorithm. The matrix of
domination helps us to understand how the privilege hazard—the result of unequal
distributions of power—plays out in different domains. Ultimately, the goal of examining
power is not only to understand it, but also to be able to challenge and change it. In the
next chapter, we explore several approaches for challenging power with data science.

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