Data Feminism
Catherine D’Ignazio and Lauren F. Klein

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.”

[OCS: I wonder how far the influence of feminism reaches into the world of data. Is it acceptable to alter the data to prove a social point? Does a “correct” answer matter if it does not support feminism or contradict its core teachings?]

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?

[OCS: You will notice that the authors did not ask, How can we make the world better?, they appear to want to fundamentally transform the world’s behavior into being compatible with their social construct.

However, this fundamental transformation seems oblivious to the basics of human nature where someone will always be stronger, more agile, better educated, wealthier, or simply be born into privilege. A world where there are leaders and followers including those individuals who will control power and amass assets. Even to the point of subjugating and disadvantaging others. This is a given and unlikely to change in the future.

Moreover, the author’s utopian visions of equality – as expressed in the mythical politics of communism – are unrealistic to say the least. Consider that there are approximately 2 billion Muslims in the world whose fundamental teachings are invariant, intolerant of dissent, and by all modern measures, disadvantage women. No concept of science,  mathematics, or data analysis is likely to reform their viewpoint.]

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.

[OCS: By definition and historical patterns and practices, politics is just that: one group controlling the government, institutions, and governing people to advantage themselves above others. Especially those political systems that pretend to promote equality for all such as socialism, communism, and whatever is the opposite of capitalism. Capitalism has transformed society into a better place – but is still a work in progress.

One need only consider that the endpoint of feminism, socialism, and communism is always authoritarian and backed by force. Whereas the endpoint of capitalism is more capitalism.]

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.

[OCS: The key is the choices you choose to make. Under an egalitarian system, there is no motivation to excel because the fruits of your labor will be distributed to someone who is less able or chooses not to work. I am not saying that we should not care for the disabled among us, but to simply declare the disabled as abled – is not only misleading, it is counterproductive.]

The starting point for data feminism is something that goes mostly unacknowledged in data science: power is not distributed equally in the world.

[OCS: It is a given that power has not, nor is ever likely to be, distributed equally as long as one obeys their biological urges and physiological benefits. Data science is a descriptive subset of mathematics and does not recognize anything beyond true, false or the probabilities that the proposition is one or the other. Please spare me the quantum dilemma of Schrodinger’s Cat because that is not the macro world in which we operate.]

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.

[OCS; Enter the world of Marxism – class warfare, the creation of ideologues who exploit the disadvantaged minorities who are promised a redress of their grievances in return for political power – ostensibly to make things more equal. Change the distribution of power? In reality, taking power from those who govern and accreting it for their own benefit. And, if history prevails, misusing it to enslave millions on the road to equality of death, destruction, and despair.]

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.

[OCS: Fine! But I will wager to say that neither one of the authors is a conservative constitutionalist or proponent of capitalism. In fact, they will probably describe themselves as progressive feminists because alternate, more accurate political labels are offensive to those who are power hungry.]

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.

[OCS: The idea of computerized police statistics and predictive allocation of resources is often attributed to Jack Maple, the New York City deputy police commissioner for crime control strategies, where is was used by New York Police Department Commissioner William Bratton to reduce crime and improve the quality of life for all New Yorkers. It should come as no surprise that the system was introduced in Los Angeles, where not so coincidently, William Bratton became the Chief of Police. It should be noted that Bratton was a progressive socialist democrat and remains one to this day.]

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.

[OCS: This is a grossly false inaccuracy. One, because the program uses more than historical data and reflects current reporting -- which is often weighted heavier than historical data. And two, because crime happens where crime happens. The data and its display is neutral and simply a way to map current events against a pattern of background occurrences.]

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.

[OCS: Here is a question that is easily answered by data analysis. Since many of the areas related to illiteracy, poverty, disease, and crime are governed by progressive Democrats, is it not likely that the public policies and the diversion of funds by the Democrats to themselves and their special interests is a causal factor in ongoing criminal activity? Or, considering the Democrat’s penchant for releasing criminals back into the community or trivializing their criminal activities, is this not a consideration for increasing crime? Or, why do the Democrats allow minority gangs to threaten the minority community or control the distribution of dangerous substances? If anything threatens democracy, it is the behavior of the Democrats.]

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.

[OCS: This is nonsensical … the system is open and the inputs are the data points generated by criminal activity, namely date, location, type of crime, response time, etc. If you want to know what comes out, look at the ongoing crime map here, and select your location and the police agency you want to observe.]

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.

[OCS: An unpleasant truth is that the Democrats are the party of racism, segregation, Jim Crow, and the KKK. And that the endemic corruption of the Democrat Party, to this day, continues to oppress the very people they profess to assist. It was the Democrats who criminalized Blackness in America. And, no matter how much the re-write history, the truth is the truth.]

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.

[OCS: The reason the Democrats punish law-abiding individuals with their gun control restrictions is because they are at the root of the criminal enterprise: be it the poverty pimps, the illegal alien activists, or even the judicial system. They are the ones who allow criminals that steal less than $950 worth of merchandise to be ticketed and released back into the community. They are the ones who plea bargain away the most serious gun charges that allow felons to be eligible for “good behavior” releases. They are the ones who allow the gangs to control the prisons and minority communities. Crime is good for business, bigger government, higher taxes, and restrictions on individual freedoms – in essence, the Democrat Party platform.]