Engineers designing technologies and systems produce problems when they do not account for existing biases in society. Designers have a mandate to make technologies efficiently, economically, and ethically. This textbook is written for both students and practicing designers, engineers, researchers, or artists who want to create more ethical designs; it aims to help readers understand how race is implicated in technology design. Learning from historical and contemporary case studies of engineering and architecture projects will help readers see clearly the power of design decisions to either perpetuate or contest racism. Chapter exercises will change engineers’ mental models to see the bias inherent to existing technological design. By incorporating the knowledge and insights of community-based experts into design projects, readers will begin to practice anti-racist leadership and counter-expertise.

From everyday apps to complex algorithms, Ruha Benjamin cuts through tech-industry hype to understand how emerging technologies can reinforce White supremacy and deepen social inequity. Benjamin argues that automation, far from being a sinister story of racist programmers scheming on the dark web, has the potential to hide, speed up, and deepen discrimination while appearing neutral and even benevolent when compared to the racism of a previous era. Presenting the concept of the “New Jim Code,” she shows how a range of discriminatory designs encode inequity by explicitly amplifying racial hierarchies; by ignoring but thereby replicating social divisions; or by aiming to fix racial bias but ultimately doing quite the opposite. Moreover, she makes a compelling case for race itself as a kind of technology, designed to stratify and sanctify social injustice in the architecture of everyday life. This illuminating guide provides conceptual tools for decoding tech promises with sociologically informed skepticism. In doing so, it challenges us to question not only the technologies we are sold but also the ones we ourselves manufacture. Visit the book's free Discussion Guide: www.dropbox.com

Focused on mapping out contemporary and future domains in philosophy of technology, this volume serves as an excellent, forward-looking resource in the field and in cognate areas of study. The 32 chapters, all of them appearing in print here for the first time, were written by both established scholars and fresh voices. They cover topics ranging from data discrimination and engineering design, to art and technology, space junk, and beyond. Spaces for the Future: A Companion to Philosophy of Technology is structured in six parts: (1) Ethical Space and Experience; (2) Political Space and Agency; (3) Virtual Space and Property; (4) Personal Space and Design; (5) Inner Space and Environment; and (6) Outer Space and Imagination. The organization maps out current and emerging spaces of activity in the field and anticipates the big issues that we soon will face.

Acknowledgments -- Introduction: the power of algorithms -- A society, searching -- Searching for Black girls -- Searching for people and communities -- Searching for protections from search engines -- The future of knowledge in the public -- The future of information culture -- Conclusion: algorithms of oppression -- Epilogue -- Notes -- Bibliography -- Index -- About the author

How big data and machine learning encode discrimination and create agitated clusters of comforting rage. In Discriminating Data, Wendy Hui Kyong Chun reveals how polarization is a goal—not an error—within big data and machine learning. These methods, she argues, encode segregation, eugenics, and identity politics through their default assumptions and conditions. Correlation, which grounds big data’s predictive potential, stems from twentieth-century eugenic attempts to “breed” a better future. Recommender systems foster angry clusters of sameness through homophily. Users are “trained” to become authentically predictable via a politics and technology of recognition. Machine learning and data analytics thus seek to disrupt the future by making disruption impossible. Chun, who has a background in systems design engineering as well as media studies and cultural theory, explains that although machine learning algorithms may not officially include race as a category, they embed whiteness as a default. Facial recognition technology, for example, relies on the faces of Hollywood celebrities and university undergraduates—groups not famous for their diversity. Homophily emerged as a concept to describe white U.S. resident attitudes to living in biracial yet segregated public housing. Predictive policing technology deploys models trained on studies of predominantly underserved neighborhoods. Trained on selected and often discriminatory or dirty data, these algorithms are only validated if they mirror this data. How can we release ourselves from the vice-like grip of discriminatory data? Chun calls for alternative algorithms, defaults, and interdisciplinary coalitions in order to desegregate networks and foster a more democratic big data.

This “sobering tale of the real consequences of gender bias” explores how Britain lost its early dominance in computing by systematically discriminating against its most qualified workers: women (Harvard Magazine) In 1944, Britain led the world in electronic computing. By 1974, the British computer industry was all but extinct. What happened in the intervening thirty years holds lessons for all postindustrial superpowers. As Britain struggled to use technology to retain its global power, the nation’s inability to manage its technical labor force hobbled its transition into the information age. In Programmed Inequality, Mar Hicks explores the story of labor feminization and gendered technocracy that undercut British efforts to computerize. That failure sprang from the government’s systematic neglect of its largest trained technical workforce simply because they were women. Women were a hidden engine of growth in high technology from World War II to the 1960s. As computing experienced a gender flip, becoming male-identified in the 1960s and 1970s, labor problems grew into structural ones and gender discrimination caused the nation’s largest computer user—the civil service and sprawling public sector—to make decisions that were disastrous for the British computer industry and the nation as a whole. Drawing on recently opened government files, personal interviews, and the archives of major British computer companies, Programmed Inequality takes aim at the fiction of technological meritocracy. Hicks explains why, even today, possessing technical skill is not enough to ensure that women will rise to the top in science and technology fields. Programmed Inequality shows how the disappearance of women from the field had grave macroeconomic consequences for Britain, and why the United States risks repeating those errors in the twenty-first century.

Discrimination Testing in Sensory Science: A Practical Handbook is a one-stop-shop for practical advice and guidance on the performance and analysis of discrimination testing in sensory science. The book covers all aspects of difference testing: the history and origin of different methods, the practicalities of setting up a difference test, replications, the statistics behind each test, dealing with the analysis, action standards, and the statistical analysis of results with R. The book is written by sensory science experts from both academia and industry, and edited by an independent sensory scientist with over twenty years of experience in planning, running and analyzing discrimination tests. This is an essential text for academics in sensory and consumer science and any sensory scientist working in research and development in food, home, and personal care products, new product development, or quality control. - Contains practical guidance on the performance and analysis of discrimination testing in sensory and consumer science for both food and non-food products - Includes the latest developments in difference testing, including both new methods and state-of-the-art approaches - Features extensive coverage of analysis with a variety of software systems - Provides essential insight for academics in sensory and consumer science and any sensory scientist working in research and development in food, home, and personal care products, new product development, or quality control

How do “human” prejudices reemerge in algorithmic cultures allegedly devised to be blind to them? How do “human” prejudices reemerge in algorithmic cultures allegedly devised to be blind to them? To answer this question, this book investigates a fundamental axiom in computer science: pattern discrimination. By imposing identity on input data, in order to filter—that is, to discriminate—signals from noise, patterns become a highly political issue. Algorithmic identity politics reinstate old forms of social segregation, such as class, race, and gender, through defaults and paradigmatic assumptions about the homophilic nature of connection. Instead of providing a more “objective” basis of decision making, machine-learning algorithms deepen bias and further inscribe inequality into media. Yet pattern discrimination is an essential part of human—and nonhuman—cognition. Bringing together media thinkers and artists from the United States and Germany, this volume asks the urgent questions: How can we discriminate without being discriminatory? How can we filter information out of data without reinserting racist, sexist, and classist beliefs? How can we queer homophilic tendencies within digital cultures?

WINNER: The 2019 Lillian Smith Book Award, 2018 McGannon Center Book Prize, and shortlisted for the Goddard Riverside Stephan Russo Book Prize for Social Justice Astra Taylor, author of The People's Platform: "The single most important book about technology you will read this year." Dorothy Roberts, author of Killing the Black Body: "A must-read." 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. In Automating 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 the process, they weaken democracy and betray our most cherished national values. This deeply researched and passionate book could not be more timely.

The contributors to Captivating Technology examine how carceral technologies such as electronic ankle monitors and predictive-policing algorithms are being deployed to classify and coerce specific populations and whether these innovations can be appropriated and reimagined for more liberatory ends.