In my wildest dreams, I never thought anyone would attempt to subvert mathematics into an ideological tool…
A friend of mine just sent me a copy of “Data Feminism” authored by Catherine D’Ignazio and Lauren F. Klein and published by the MIT (Massachusetts Institute of Technology) Press.
For feminism begins with a belief in the ‘political, social, and economic equality of the sexes,’ as the Merriam-Webster Dictionary defines the term— as does, for the record, Beyoncé. And any definition of feminism also necessarily includes the activist work that is required to turn that belief into reality.
In Data Feminism, we bring these two aspects of feminism together, demonstrating a way of thinking about data, their analysis, and their display, that is informed by this tradition of feminist activism as well as the legacy of feminist critical thought.”
Indeed, a central aim of this book is to describe a form of intersectional feminism that takes the inequities of the present moment as its starting point and begins its own work by asking: How can we use data to remake the world?
Key to the idea of intersectionality is that it does not only describe the intersecting aspects of any particular person’s identity (or positionalities, as they are sometimes termed). It also describes the intersecting forces of privilege and oppression at work in a given society. Oppression involves the systematic mistreatment of certain groups of people by other groups. It happens when power is not distributed equally— when one group controls the institutions of law, education, and culture, and uses its power to systematically exclude other groups while giving its own group unfair advantages (or simply maintaining the status quo). In the case of gender oppression, we can point to the sexism, cissexism, and patriarchy that is evident in everything from political representation to the wage gap to who speaks more often (or more loudly) in a meeting.
The effects of privilege and oppression are not distributed evenly across all individuals and groups, however. For some, they become an obvious and unavoidable part of daily life, particularly for women and people of color and queer people and immigrants: the list goes on. If you are a member of any or all of these (or other) minoritized groups, you experience their effects everywhere, shaping the choices you make (or don’t get to make) each day.
The starting point for data feminism is something that goes mostly unacknowledged in data science: power is not distributed equally in the world.
Those who wield power are disproportionately elite, straight, white, able-bodied, cisgender men from the Global North. The work of data feminism is first to tune into how standard practices in data science serve to reinforce these existing inequalities and second to use data science to challenge and change the distribution of power.
Underlying data feminism is a belief in and commitment to co-liberation: the idea that oppressive systems of power harm all of us, that they undermine the quality and validity of our work, and that they hinder us from creating true and lasting social impact with data science. We wrote this book because we are data scientists and data feminists. Although we speak as a “we” in this book, and share certain identities, experiences, and share certain identities, experiences, and skills, we have distinct life trajectories and motivations for our work on this project.
Although datafication may occasionally verge into the realm of the absurd, it remains a very serious issue. Decisions of civic, economic, and individual importance are already and increasingly being made by automated systems sifting through large amounts of data.
For example, PredPol, a so-called predictive policing company founded in 2012 by an anthropology professor at the University of California, Los Angeles, has been employed by the City of Los Angeles for nearly a decade to determine which neighborhoods to patrol more heavily, and which neighborhoods to (mostly) ignore.
But because PredPol is based on historical crime data and US policing practices have always disproportionately surveilled and patrolled neighborhoods of color, the predictions of where crime will happen in the future look a lot like the racist practices of the past.
These systems create what mathematician and writer Cathy O’Neil, in Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, calls a “pernicious feedback loop,” amplifying the effects of racial bias and of the criminalization of poverty that are already endemic to the United States.
O’Neil’s solution is to open up the computational systems that produce these racist results. Only by knowing what goes in, she argues, can we understand what comes out.
This is a key step in the project of mitigating the effects of biased data. Data feminism additionally requires that we trace those biased data back to their source. PredPol and the “three most objective data points” that it employs certainly amplify existing biases, but they are not the root cause. The cause, rather, is the long history of the criminalization of Blackness in the United States, which produces biased policing practices, which produce biased historical data, which are then used to develop risk models for the future.
Tracing these links to historical and ongoing forces of oppression can help us answer the ethical question, Should this system exist? In the case of PredPol, the answer is a resounding no.
As I replied to my friend, this seems to be a foot-noted virtue-signaling academic exercise written for social scientists, probably more accurately described as socialist scientists. To say I did not like it would be an understatement.
I have always believed that data, like any other tool, is neutral and without ideology. It is those who exploit it for their own agenda who are responsible for the outcome.
Stripped of its pseudo-intellectual jargon and convoluted thought -- it all comes down to divide and conquer using coalitions to build a power base that can be manipulated by promising to redress individual slights and grievances in return for political power - preferably in the form of the mythical egalitarianism of communism.
We are so screwed.
“Nullius in verba.”-- take nobody's word for it!
“Beware of false knowledge; it is more dangerous than ignorance.”-- George Bernard Shaw
“Progressive, liberal, Socialist, Marxist, Democratic Socialist -- they are all COMMUNISTS.”
“The key to fighting the craziness of the progressives is to hold them responsible for their actions, not their intentions.” – OCS "The object in life is not to be on the side of the majority, but to escape finding oneself in the ranks of the insane." -- Marcus Aurelius “A people that elect corrupt politicians, imposters, thieves, and traitors are not victims... but accomplices” -- George Orwell “Fere libenter homines id quod volunt credunt." (The people gladly believe what they wish to.) ~Julius Caesar “Describing the problem is quite different from knowing the solution. Except in politics." ~ OCS
“The key to fighting the craziness of the progressives is to hold them responsible for their actions, not their intentions.” – OCS
"The object in life is not to be on the side of the majority, but to escape finding oneself in the ranks of the insane." -- Marcus Aurelius
“A people that elect corrupt politicians, imposters, thieves, and traitors are not victims... but accomplices” -- George Orwell
“Fere libenter homines id quod volunt credunt." (The people gladly believe what they wish to.) ~Julius Caesar
“Describing the problem is quite different from knowing the solution. Except in politics." ~ OCS