An Anti-racist Reading List

The opposite of racist isn’t ‘not racist.’ It is ‘anti-racist.’

The heartbeat of racism is denial.

— Ibram X. Kendi

It’s been said that racism is so American that when we protest racism, some assume we’re protesting America.

— Beyoncé Knowles

Now is a good time to read these books, follow these authors, confront our history, and unpack what we need to unpack.

Systemic racism is a machine that runs whether we pull the levers or not, and by just letting it be, we are responsible for what it produces. We have to actually dismantle the machine if we want to make change.

Source: Oluo, Ijeoma. So You Want to Talk About Race (pp. 29-30).

When social scientists describe racism as “systemic,” we’re referring to collective practices and representations that disadvantage categories of human beings on the basis of their perceived “race.” The key word here is “collective.” Much of the racial stupidity we encounter in everyday life derives from the fact that people think of racism as individual prejudice rather than a broader system and structure of power.

The thing about white supremacy is that it socializes all of us to minimize its terror, to systematically deny or underestimate the harm. “We’ve come so far,” “Things are getting better,” “It could be worse”—all of these tropes minimize racial terror. Americans have been socialized to look on the bright side despite centuries of colonial and racial violence, torture, and the oppression of minorities. Our problem is not and has never been overreacting to racial terror. Our problem is the hegemony of under-reaction, denial, minimization. Ours is a society that has always socialized white folks to live in the midst of racial oppression but go on with their lives like normal. At every turn, those who oppose white supremacy have been met with denial, violence, “race card” accusations, or magnificent claims about progress. It seems that in the minds of many white liberals, we should all be celebrating the fact that most of us are not physically in chains. White supremacy wants you to look at four hundred years of uninterrupted racial terror and conclude “Things aren’t so bad.”

Source: Fleming, Crystal Marie. How to Be Less Stupid About Race (p. 12, pp. 128-129).

Being seen racially is a common trigger of white fragility, and thus, to build our stamina, white people must face the first challenge: naming our race.

White people in North America live in a society that is deeply separate and unequal by race, and white people are the beneficiaries of that separation and inequality. As a result, we are insulated from racial stress, at the same time that we come to feel entitled to and deserving of our advantage. Given how seldom we experience racial discomfort in a society we dominate, we haven’t had to build our racial stamina.

Source: DiAngelo, Robin J.. White Fragility (pp. 1-2, p. 7).

White rage is not about visible violence, but rather it works its way through the courts, the legislatures, and a range of government bureaucracies. It wreaks havoc subtly, almost imperceptibly. Too imperceptibly, certainly, for a nation consistently drawn to the spectacular—to what it can see. It’s not the Klan. White rage doesn’t have to wear sheets, burn crosses, or take to the streets. Working the halls of power, it can achieve its ends far more effectively, far more destructively.

The trigger for white rage, inevitably, is black advancement. It is not the mere presence of black people that is the problem; rather, it is blackness with ambition, with drive, with purpose, with aspirations, and with demands for full and equal citizenship.

The truth is, white rage has undermined democracy, warped the Constitution, weakened the nation’s ability to compete economically, squandered billions of dollars on baseless incarceration, rendered an entire region sick, poor, and woefully undereducated, and left cities nothing less than decimated. All this havoc has been wreaked simply because African Americans wanted to work, get an education, live in decent communities, raise their families, and vote. Because they were unwilling to take no for an answer.

Source: Anderson Ph.D., Carol. White Rage (p. 3, p. 6).

History duels: the undeniable history of antiracist progress, the undeniable history of racist progress. Before and after the Civil War, before and after civil rights, before and after the first Black presidency, the White consciousness duels. The White body defines the American body. The White body segregates the Black body from the American body. The White body instructs the Black body to assimilate into the American body. The White body rejects the Black body assimilating into the American body—and history and consciousness duel anew.

The Black body in turn experiences the same duel. The Black body is instructed to become an American body. The American body is the White body. The Black body strives to assimilate into the American body. The American body rejects the Black body. The Black body separates from the American body. The Black body is instructed to assimilate into the American body—and history and consciousness duel anew.

But there is a way to get free. To be antiracist is to emancipate oneself from the dueling consciousness. To be antiracist is to conquer the assimilationist consciousness and the segregationist consciousness. The White body no longer presents itself as the American body; the Black body no longer strives to be the American body, knowing there is no such thing as the American body, only American bodies, racialized by power.

Source: Kendi, Ibram X.. How to Be an Antiracist (p. 33).

Racist and Antiracist Definitions from “How to Be an Antiracist”

How to Be an Antiracist” opens up each chapter with a racist and anti-racist definition. I copied them out for my own reference.

Chapter 1, Definitions

RACIST: One who is supporting a racist policy through their actions or inaction or expressing a racist idea.

ANTIRACIST: One who is supporting an antiracist policy through their actions or expressing an antiracist idea.

Chapter 2, Dueling Consciousness

ASSIMILATIONIST: One who is expressing the racist idea that a racial group is culturally or behaviorally inferior and is supporting cultural or behavioral enrichment programs to develop that racial group.

SEGREGATIONIST: One who is expressing the racist idea that a permanently inferior racial group can never be developed and is supporting policy that segregates away that racial group.

ANTIRACIST: One who is expressing the idea that racial groups are equals and none needs developing, and is supporting policy that reduces racial inequity.

Chapter 3, Power

RACE: A power construct of collected or merged difference that lives socially.

Chapter 4, Biology

BIOLOGICAL RACIST: One who is expressing the idea that the races are meaningfully different in their biology and that these differences create a hierarchy of value.

BIOLOGICAL ANTIRACIST: One who is expressing the idea that the races are meaningfully the same in their biology and there are no genetic racial differences.

Chapter 5, Ethnicity

ETHNIC RACISM: A powerful collection of racist policies that lead to inequity between racialized ethnic groups and are substantiated by racist ideas about racialized ethnic groups.

ETHNIC ANTIRACISM: A powerful collection of antiracist policies that lead to equity between racialized ethnic groups and are substantiated by antiracist ideas about racialized ethnic groups.

Chapter 6, Body

BODILY RACIST: One who is perceiving certain racialized bodies as more animal-like and violent than others.

BODILY ANTIRACIST: One who is humanizing, deracializing, and individualizing nonviolent and violent behavior.

Chapter 7, Culture

CULTURAL RACIST: One who is creating a cultural standard and imposing a cultural hierarchy among racial groups.

CULTURAL ANTIRACIST: One who is rejecting cultural standards and equalizing cultural differences among racial groups.

Chapter 8, Behavior

BEHAVIORAL RACIST: One who is making individuals responsible for the perceived behavior of racial groups and making racial groups responsible for the behavior of individuals.

BEHAVIORAL ANTIRACIST: One who is making racial group behavior fictional and individual behavior real.

Chapter 9, Color

COLORISM: A powerful collection of racist policies that lead to inequities between Light people and Dark people, supported by racist ideas about Light and Dark people.

COLOR ANTIRACISM: A powerful collection of antiracist policies that lead to equity between Light people and Dark people, supported by antiracist ideas about Light and Dark people.

Chapter 10, White

ANTI-WHITE RACIST: One who is classifying people of European descent as biologically, culturally, or behaviorally inferior or conflating the entire race of White people with racist power.

Chapter 11, Black

POWERLESS DEFENSE: The illusory, concealing, disempowering, and racist idea that Black people can’t be racist because Black people don’t have power.

Chapter 12, Class

CLASS RACIST: One who is racializing the classes, supporting policies of racial capitalism against those race-classes, and justifying them by racist ideas about those race-classes.

ANTIRACIST ANTICAPITALIST: One who is opposing racial capitalism.

Chapter 13, Space

SPACE RACISM: A powerful collection of racist policies that lead to resource inequity between racialized spaces or the elimination of certain racialized spaces, which are substantiated by racist ideas about racialized spaces.

SPACE ANTIRACISM: A powerful collection of antiracist policies that lead to racial equity between integrated and protected racialized spaces, which are substantiated by antiracist ideas about racialized spaces.

Chapter 14, Gender

GENDER RACISM: A powerful collection of racist policies that lead to inequity between race-genders and are substantiated by racist ideas about race-genders.

GENDER ANTIRACISM: A powerful collection of antiracist policies that lead to equity between race-genders and are substantiated by antiracist ideas about race-genders.

Chapter 15, Sexuality

QUEER RACISM: A powerful collection of racist policies that lead to inequity between race-sexualities and are substantiated by racist ideas about race-sexualities.

QUEER ANTIRACISM: A powerful collection of antiracist policies that lead to equity between race-sexualities and are substantiated by antiracist ideas about race-sexualities.

Chapter 16, Failure

ACTIVIST: One who has a record of power or policy change.

Algorithms: Opinions Embedded in Math … and Ed-tech

A much more accurate definition of an algorithm is that it’s an opinion embedded in math.

So, we do that every time we build algorithms — we curate our data, we define success, we embed our own values into algorithms.

So when people tell you algorithms make thing objective, you say “no, algorithms make things work for the builders of the algorithms.”

In general, we have a situation where algorithms are extremely powerful in our daily lives but there is a barrier between us and the people building them, and those people are typically coming from a kind of homogenous group of people who have their particular incentives — if it’s in a corporate setting, usually profit and not usually a question of fairness for the people who are subject to their algorithms.

So we always have to penetrate this fortress. We have to be able to question the algorithms themselves.

We live in the age of the algorithm – mathematical models are sorting our job applications, curating our online worlds, influencing our elections, and even deciding whether or not we should go to prison. But how much do we really know about them? Former Wall St quant, Cathy O’Neil, exposes the reality behind the AI, and explains how algorithms are just as prone to bias and discrimination as the humans who program them.

Source: The Truth About Algorithms | Cathy O’Neil – YouTube

Follow the video up with Cathy O’Neil’s book “Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy”. Here are some selected quotes from the introduction.

And then I made a big change. I quit my job and went to work as a quant for D. E. Shaw, a leading hedge fund. In leaving academia for finance, I carried mathematics from abstract theory into practice. The operations we performed on numbers translated into trillions of dollars sloshing from one account to another. At first I was excited and amazed by working in this new laboratory, the global economy. But in the autumn of 2008, after I’d been there for a bit more than a year, it came crashing down.

The crash made it all too clear that mathematics, once my refuge, was not only deeply entangled in the world’s problems but also fueling many of them. The housing crisis, the collapse of major financial institutions, the rise of unemployment- all had been aided and abetted by mathematicians wielding magic formulas. What’s more, thanks to the extraordinary powers that I loved so much, math was able to combine with technology to multiply the chaos and misfortune, adding efficiency and scale to systems that I now recognized as flawed.

If we had been clear-headed, we all would have taken a step back at this point to figure out how math had been misused and how we could prevent a similar catastrophe in the future. But instead, in the wake of the crisis, new mathematical techniques were hotter than ever, and expanding into still more domains. They churned 24/ 7 through petabytes of information, much of it scraped from social media or e-commerce websites. And increasingly they focused not on the movements of global financial markets but on human beings, on us. Mathematicians and statisticians were studying our desires, movements, and spending power. They were predicting our trustworthiness and calculating our potential as students, workers, lovers, criminals.

This was the Big Data economy, and it promised spectacular gains. A computer program could speed through thousands of résumés or loan applications in a second or two and sort them into neat lists, with the most promising candidates on top. This not only saved time but also was marketed as fair and objective.

Yet I saw trouble. The math-powered applications powering the data economy were based on choices made by fallible human beings. Some of these choices were no doubt made with the best intentions. Nevertheless, many of these models encoded human prejudice, misunderstanding, and bias into the software systems that increasingly managed our lives. Like gods, these mathematical models were opaque, their workings invisible to all but the highest priests in their domain: mathematicians and computer scientists. Their verdicts, even when wrong or harmful, were beyond dispute or appeal. And they tended to punish the poor and the oppressed in our society, while making the rich richer.

I came up with a name for these harmful kinds of models: Weapons of Math Destruction, or WMDs for short.

Equally important, statistical systems require feedback- something to tell them when they’re off track. Statisticians use errors to train their models and make them smarter. If Amazon. ​ com, through a faulty correlation, started recommending lawn care books to teenage girls, the clicks would plummet, and the algorithm would be tweaked until it got it right. Without feedback, however, a statistical engine can continue spinning out faulty and damaging analysis while never learning from its mistakes.

Many of the WMDs I’ll be discussing in this book, including the Washington school district’s value-added model, behave like that. They define their own reality and use it to justify their results. This type of model is self-perpetuating, highly destructive- and very common.

In WMDs, many poisonous assumptions are camouflaged by math and go largely untested and unquestioned.

This underscores another common feature of WMDs. They tend to punish the poor. This is, in part, because they are engineered to evaluate large numbers of people. They specialize in bulk, and they’re cheap. That’s part of their appeal. The wealthy, by contrast, often benefit from personal input. A white-shoe law firm or an exclusive prep school will lean far more on recommendations and face-to-face interviews than will a fast-food chain or a cash-strapped urban school district. The privileged, we’ll see time and again, are processed more by people, the masses by machines.

Needless to say, racists don’t spend a lot of time hunting down reliable data to train their twisted models. And once their model morphs into a belief, it becomes hardwired. It generates poisonous assumptions, yet rarely tests them, settling instead for data that seems to confirm and fortify them. Consequently, racism is the most slovenly of predictive models. It is powered by haphazard data gathering and spurious correlations, reinforced by institutional inequities, and polluted by confirmation bias. In this way, oddly enough, racism operates like many of the WMDs I’ll be describing in this book.

Source: Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy (pp. 2-3, pp. 6-8). Crown/Archetype. Kindle Edition.

For how this fits into education, read Weapons of Math Destruction along with Lower Ed and Paying the Price.

Lower Ed shows exploitation of vulnerable. Paying the Price tells how we fail them. Weapons of Math Destruction outlines tools we made to do it.

Source: Kyle Johnson on Twitter

Indeed. These three great books provide a systems view of higher education and its intersections with tech and algorithms. Below, I excerpt from their introductions and book blurbs, provide chapter lists, and select a handful of tweets from authors Tressie McMillan Cottom, Sara Goldrick-Rab, and Cathy O’Neil. They are all active on Twitter and well worth a follow.

Source: Lower Ed, Paying the Price, and Weapons of Math Destruction – Ryan Boren

See also Safiya Umoja Noble’sAlgorithms of Oppression: How Search Engines Reinforce Racism”.

This book is about the power of algorithms in the age of neoliberalism and the ways those digital decisions reinforce oppressive social relationships and enact new modes of racial profiling, which I have termed technological redlining. By making visible the ways that capital, race, and gender are factors in creating unequal conditions, I am bringing light to various forms of technological redlining that are on the rise. The near-ubiquitous use of algorithmically driven software, both visible and invisible to everyday people, demands a closer inspection of what values are prioritized in such automated decision-making systems. Typically, the practice of redlining has been most often used in real estate and banking circles, creating and deepening inequalities by race, such that, for example, people of color are more likely to pay higher interest rates or premiums just because they are Black or Latino, especially if they live in low-income neighborhoods. On the Internet and in our everyday uses of technology, discrimination is also embedded in computer code and, increasingly, in artificial intelligence technologies that we are reliant on, by choice or not. I believe that artificial intelligence will become a major human rights issue in the twenty-first century. We are only beginning to understand the long-term consequences of these decision-making tools in both masking and deepening social inequality. This book is just the start of trying to make these consequences visible. There will be many more, by myself and others, who will try to make sense of the consequences of automated decision making through algorithms in society.

Part of the challenge of understanding algorithmic oppression is to understand that mathematical formulations to drive automated decisions are made by human beings. While we often think of terms such as “big data” and “algorithms” as being benign, neutral, or objective, they are anything but. The people who make these decisions hold all types of values, many of which openly promote racism, sexism, and false notions of meritocracy, which is well documented in studies of Silicon Valley and other tech corridors.

Source: Algorithms of Oppression: How Search Engines Reinforce Racism (Kindle Locations 162-177). NYU Press. Kindle Edition.