Yasamin Rezai is a Ph.D. Candidate studying Cultural, Literary and Linguistic Studies at the department of Modern Languages and Literature at University of Miami where she teaches French, Persian and assists with Italian courses through theatre, cinema, and performance art. Her work is situated in New Media Studies and Performance Studies by employing Digital Humanities tools. She is interested in looking at social media and data culture from performative and media perspectives- and also adopting numeric approaches.
This is the source
Looking through the intersectional feminist lens, Catherine D’Ignazio and Lauren Klein introduce data as a tool of power in the past and present world in their book
data justicecan and ought to be redeployed to challenge power.
A review of Catherine D’Ignazio and Lauren Klein's recent work: Data Feminism.
“is a book about power in data science. Because feminism, ultimately, is about power too”
isn’t only about women … it isn’t only for women … it isn’t only about gender either. Feminism is about power – who has it and who doesn’t.
The book offers a list of seven comprehensive acts to prompt data activism. To make Data Feminism possible, the authors provide seven main principles – examining the power, challenging it, elevating emotion and embodiment, rethinking binaries and hierarchies, embracing pluralism, considering context, and eventually making labor visible – and go through them in each of the seven chapters.
None of us are free if some of us are not
The first chapter’s core idea is the exclusion of some communities caused by
the privilege hazard
, meaning ignorance of being on top. While the chapter
is grounded around the concept of the matrix of domination
originally offered
by Patricia Collins, with the main aim to identify and examine the
power, it explains how data are extracted by dominant groups of people and
mostly extracted from others. The authors find it crucial to understand how systems
of oppression work with data as one main ingredient before taking the next step, challenging the power.
In the second chapter, the language employed to address the questions of ethics and
values when discussing data and supporting algorithms is analyzed to reform those
discussions. Imagined objectivity
and concepts that uphold it are compared and
contradicted with real objectivity
and intersectional feminist concepts that
strengthen it. The authors argue that the first locates the
source of the problem in individuals or technical systems
while the
latter acknowledges structural power differentials and works
toward dismantling them
The third chapter, a favorite, challenges the possibility of pure objectivity and
neutrality of data visualization in order to elevate emotion and
embodiment. It attempts to remind readers of the partiality of knowledge,
drawing from Sandra Harding and Donna Haraway’s god
trick
who and what the system is trying to exclude
As the fourth chapter quotes from Joni Seager, What gets counted
counts,
and it stands for rethinking binaries and
hierarchies. Classification of knowledge and constructed social categories
used in science are introduced as other tools of power in data science. The Seager
principle explains how binaries and constructed categories are the product of
cultural and historical sets of values and biases of societies, and are therefore not
only unreliable but also different from one society or community to another. Such
binaries and classification systems are not extensible and need to be questioned and
reconsidered. The peril lies where binaries are translated to codes and then encoded
to technical systems. Such technical systems use data and datasets gathered and
processed in the mentioned categories as input, process them into deliverable social
policies or decisions, and perform them on human bodies and individuals. The authors
invite readers to think of categories that, if left unquestioned, might be reductive,
incomplete, or exclusive, and so too the policies and their deliverables.
Seeking multiple perspectives, the fifth principle and chapter, embracing pluralism, takes up the fourth chapter’s argument with the same
concern of the possible exclusion of local, Indigenous, or experiential ways of
knowing. Through stories of projects such as the Anti-Eviction Mapping Project and
the EJ Atlas, the chapter shows how not leaving the data work entirely to the ones
who are aliens to the subject of a project – assuming that their neutrality in
relation to the subject of the project is the only necessity – can be beneficial.
Employing the communities, including marginalized ways of thoughts, and making the
process more pluralistic by synthesizing multiple
perspectives
The sixth chapter discusses one of the most critical and challenging principles: considering context. The authors discuss how numbers, or raw
data, are alone not enough to show results in works of research that have to deal
with humans. They offer that raw data
are, in fact,
already cooked
in previous systems of bias or exclusion
before becoming a number or data because they are obtained from people, regardless of
the context in which the datasets were produced. Considering, acknowledging, and
naming the systems of oppression at work is essential when talking about numbers,
as they do not speak for themselves
Letting the numbers speak for themselves
is viewed as
unethical and undemocratic, with the potential to do more harm than good by
reinforcing the unjust status quo
Reminiscently, if nothing else, Data Feminism is a valuable project that by itself
starts practicing the seventh principle of making the labor
visible. If read between the lines, the authors leave to the audience two
valuable lists as proof: first, a list of activists and scholars who are members of
previously colonized nations, LGBTQIA+ and Indigenous communities and people of
color, and second, a list of corporations and projects led by people of color. Nearly two-thirds of their citations are from women or non-binary
people; almost every chapter has a project from the Global South; a third of all
citations are from people of color, and nearly half of all projects mentioned in
the book are led by people of color
There is no doubt that the consistency of their chain of thoughts and the logic that
accompanies it are best presented in the threads of real-world well-researched
stories. D’Ignazio and Klein master the art of storytelling in their scholarship by
sublimely taking the readers’ hands and walking them through one question to another.
Each question begins with a story and slowly leads the readers to the following
question, then the story leaves the readers for another story. Questions and stories,
with a touch of the desire to discover a part of the truth behind data industry and
its history, take the reader line by line and chapter by chapter with an excellent
suspension element to find the complete picture of the main idea. D’Ignazio and Klein
are not the first people who point out intersectionality, data ethics, or the history
of colonialism, sexism, or racism, but they are some of the very few who treat them
all as simple layers patiently and gradually added to a body of knowledge, forming an
understandable array of new settings and concepts. As examples they define the
matrix of domination
in the first chapter and auditing the
algorithms
in the second chapter.
Among the readers’ comments and reviews online from across the world, there is one
question everybody asks, and many try to answer: who is this book
For data scientists, engineers, managers, or anyone
data science for whom?, data science by whom?,and
data science with whose interests and goals?
For the ones who neither work with data nor the humanities, it is still worth the
read because of the very simple fact that data surrounds everybody, or it will sooner
or later in life no matter where people live – if it hypothetically has not yet. It
gives readers invaluable insight into the social and data structure of the present
world: how an individual is, or sometimes is not, being counted, what individuals
count and sometimes do not, how an individual is being [mis]presented, and how the
world around is being modeled, displayed and shown to them. It makes the reader think
twice or more critically about god tricks
facts
one has already accepted as their
lifetime assumptions.
Out of the scope of this book, the authors seemingly continue the work while engaging
other scholars. There is a workspace on Slack with more than 300 members from all
over the world, on which anyone can share ideas, news, articles, tweets related to
the data economy to nurture the imagination of what an equitable
data economy might look like