Algorithms of Oppression by
A revealing look at how negative biases against women of color are embedded in search engine results and algorithms Run a Google search for "black girls"--what will you find? "Big Booty" and other sexually explicit terms are likely to come up as top search terms. But, if you type in "white girls," the results are radically different. The suggested porn sites and un-moderated discussions about "why black women are so sassy" or "why black women are so angry" presents a disturbing portrait of black womanhood in modern society. In Algorithms of Oppression, Safiya Umoja Noble challenges the idea that search engines like Google offer an equal playing field for all forms of ideas, identities, and activities. Data discrimination is a real social problem; Noble argues that the combination of private interests in promoting certain sites, along with the monopoly status of a relatively small number of Internet search engines, leads to a biased set of search algorithms that privilege whiteness and discriminate against people of color, specifically women of color. Through an analysis of textual and media searches as well as extensive research on paid online advertising, Noble exposes a culture of racism and sexism in the way discoverability is created online. As search engines and their related companies grow in importance--operating as a source for email, a major vehicle for primary and secondary school learning, and beyond--understanding and reversing these disquieting trends and discriminatory practices is of utmost importance. An original, surprising and, at times, disturbing account of bias on the internet, Algorithms of Oppression contributes to our understanding of how racism is created, maintained, and disseminated in the 21st century.
America Classifies the Immigrants by
When more than twenty million immigrants arrived in the United States between 1880 and 1920, the government attempted to classify them according to prevailing ideas about race and nationality. But this proved hard to do. Ideas about racial or national difference were slippery, contested, and yet consequential--were "Hebrews" a "race," a "religion," or a "people"? As Joel Perlmann shows, a self-appointed pair of officials created the government's 1897 List of Races and Peoples, which shaped exclusionary immigration laws, the wording of the U.S. Census, and federal studies that informed social policy. Its categories served to maintain old divisions and establish new ones. Across the five decades ending in the 1920s, American immigration policy built increasingly upon the belief that some groups of immigrants were desirable, others not. Perlmann traces how the debates over this policy institutionalized race distinctions--between whites and nonwhites, but also among whites--in immigration laws that lasted four decades. Despite a gradual shift among social scientists from "race" to "ethnic group" after the 1920s, the diffusion of this key concept among government officials and the public remained limited until the end of the 1960s. Taking up dramatic changes to racial and ethnic classification since then, America Classifies the Immigrants concentrates on three crucial reforms to the American Census: the introduction of Hispanic origin and ancestry (1980), the recognition of mixed racial origins (2000), and a rethinking of the connections between race and ethnic group (proposed for 2020).
Automating Inequality by
A powerful investigative look at data-based discrimination--and how technology affects civil and human rights and economic equity The State of Indiana denies one million applications for healthcare, foodstamps and cash benefits in three years--because a new computer system interprets any mistake as "failure to cooperate." In Los Angeles, an algorithm calculates the comparative vulnerability of tens of thousands of homeless people in order to prioritize them for an inadequate pool of housing resources. In Pittsburgh, a child welfare agency uses a statistical model to try to predict which children might be future victims of abuse or neglect. Since the dawn of the digital age, decision-making in finance, employment, politics, health and human services has undergone revolutionary change. Today, automated systems--rather than humans--control which neighborhoods get policed, which families attain needed resources, and who is investigated for fraud. While we all live under this new regime of data, the most invasive and punitive systems are aimed at the poor. InAutomating Inequality,Virginia Eubanks systematically investigates the impacts of data mining, policy algorithms, and predictive risk models on poor and working-class people in America. The book is full of heart-wrenching and eye-opening stories, from a woman in Indiana whose benefits are literally cut off as she lays dying to a family in Pennsylvania in daily fear of losing their daughter because they fit a certain statistical profile. The U.S. has always used its most cutting-edge science and technology to contain, investigate, discipline and punish the destitute. Like the county poorhouse and scientific charity before them, digital tracking and automated decision-making hide poverty from the middle-class public and give the nation the ethical distance it needs to make inhumane choices: which families get food and which starve, who has housing and who remains homeless, and which families are broken up by the state. In theprocess, they weaken democracy and betray our most cherished national values. This deeply researched and passionate book could not be more timely.
Race on Campus by
In Race on Campus, Julie J. Park argues that there are surprisingly pervasive and stubborn myths about diversity on college and university campuses, and that these myths obscure the notable significance and admirable effects that diversity has had on campus life. Based on her analysis of extensive research and data about contemporary students and campuses, Park counters these myths and explores their problematic origins. Among the major myths that she addresses are charges of pervasive self-segregation, arguments that affirmative action in college admissions has run its course and become counterproductive, related arguments that Asian Americans are poorly served by affirmative action policies, and suggestions that programs and policies meant to promote diversity have failed to address class-based disadvantages. In the course of responding to these myths, Park presents a far more positive and nuanced portrait of diversity and its place on American college campuses. At a time when diversity has become a central theme and goal of colleges and universities throughout the United States, Race on Campus offers a contemporary, research-based exploration of racial dynamics on today's college campuses.
Weapons of Math Destruction by
We live in the age of the algorithm. Increasingly, the decisions that affect our lives--where we go to school, whether we get a car loan, how much we pay for health insurance--are being made not by humans, but by mathematical models. In theory, this should lead to greater fairness: Everyone is judged according to the same rules, and bias is eliminated. But as Cathy O'Neil reveals in this urgent and necessary book, the opposite is true. The models being used today are opaque, unregulated, and uncontestable, even when they're wrong. Most troubling, they reinforce discrimination: If a poor student can't get a loan because a lending model deems him too risky (by virtue of his zip code), he's then cut off from the kind of education that could pull him out of poverty, and a vicious spiral ensues. Models are propping up the lucky and punishing the downtrodden, creating a "toxic cocktail for democracy." Welcome to the dark side of Big Data. Tracing the arc of a person's life, O'Neil exposes the black box models that shape our future, both as individuals and as a society. These "weapons of math destruction" score teachers and students, sort résumés, grant (or deny) loans, evaluate workers, target voters, set parole, and monitor our health. O'Neil calls on modelers to take more responsibility for their algorithms and on policy makers to regulate their use. But in the end, it's up to us to become more savvy about the models that govern our lives. This important book empowers us to ask the tough questions, uncover the truth, and demand change.
We Are Data by
What identity means in an algorithmic age: how it works, how our lives are controlled by it, and how we can resist it Algorithms are everywhere, organizing the near limitless data that exists in our world. Derived from our every search, like, click, and purchase, algorithms determine the news we get, the ads we see, the information accessible to us and even who our friends are. These complex configurations not only form knowledge and social relationships in the digital and physical world, but also determine who we are and who we can be, both on and offline. Algorithms create and recreate us, using our data to assign and reassign our gender, race, sexuality, and citizenship status. They can recognize us as celebrities or mark us as terrorists. In this era of ubiquitous surveillance, contemporary data collection entails more than gathering information about us. Entities like Google, Facebook, and the NSA also decide what that information means, constructing our worlds and the identities we inhabit in the process. We have little control over who we algorithmically are. Our identities are made useful not for us--but for someone else. Through a series of entertaining and engaging examples, John Cheney-Lippold draws on the social constructions of identity to advance a new understanding of our algorithmic identities. We Are Data will educate and inspire readers who want to wrest back some freedom in our increasingly surveilled and algorithmically-constructed world.
