{"product_id":"discriminating-dataisbn-9780262046221","title":"Discriminating Data","description":"\u003cb\u003eHow big data and machine learning encode discrimination and create agitated clusters of comforting rage.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eIn \u003ci\u003eDiscriminating Data\u003c\/i\u003e, 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.\u003cbr\u003e \u003cbr\u003eChun, 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. \u003cbr\u003e \u003cbr\u003eHow 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. \u003cbr\u003e Preface ix\u003cbr\u003eIntroduction: How to Destroy the World, One Solution at a Time 1\u003cbr\u003eRed Pill Toxicity, or Liberation Envy 29\u003cbr\u003e1 Correlating Eugenics 35\u003cbr\u003eThe Transgressive Hypothesis 75\u003cbr\u003e2 Homophily, or the Swarming of the Segregated Neighborhood 81\u003cbr\u003e3 Algorithmic Authenticity 139\u003cbr\u003eCorrelating Ideology, or What Lies at the Surface 173\u003cbr\u003e4 Recognizing Recognition 185\u003cbr\u003eThe Space Between Us 231\u003cbr\u003eCoda: Living in Difference 239\u003cbr\u003eAcknowledgments 255\u003cbr\u003eNotes 259\u003cbr\u003eReferences for Mathematical Illustrations 317\u003cbr\u003eIndex 319“A shattering book! Chun unveils and dispels many lazy ideas that we—data and network scientists—heedlessly adopted. Her book opens questions critical to our disciplines. We urgently need new methodological tools to tackle them.”\u003cbr\u003e—\u003cb\u003eGiulio Dalla Riva, Senior Lecturer in Data Science, University of Canterbury\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e“Chun teaches readers exactly \u003ci\u003ehow\u003c\/i\u003e digital networks amplify racism and discrimination, guiding the reader toward a different future by analyzing our networked past. This is a brilliant book!”\u003cbr\u003e—\u003cb\u003eLisa Nakamura, Gwendolyn Calvert Baker Collegiate Professor, University of Michigan; author of \u003ci\u003eDigitizing Race: Visual Cultures of the Internet\u003c\/i\u003e\u003c\/b\u003e\u003cbr\u003e\u003cb\u003e \u003c\/b\u003e\u003cbr\u003e“With \u003ci\u003eDiscriminating Data\u003c\/i\u003e, Wendy Chun hits on a core idea of the contemporary internet: homophily, or grouping together ‘like’ people. In brilliant, probing essays, she gives us the analytical, ethical, and political tools to challenge the resulting prejudice and polarization. A \u003ci\u003every\u003c\/i\u003e important book.”\u003cbr\u003e—\u003cb\u003ePeter Galison, Physics and History of Science , Harvard University; coauthor of \u003ci\u003eObjectivity\u003c\/i\u003e; Director of \u003ci\u003eBlack Holes: The Edge of All We Know\u003c\/i\u003e\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e \u003cbr\u003e \u003cbr\u003e“\u003ci\u003eDiscriminating Data\u003c\/i\u003e achieves the Herculean task of teaching us to see contemporary uses of data and algorithms, especially but not exclusively in social media, as historically contingent instead of inevitable. This book is a gift to and for our times!”\u003cbr\u003e—\u003cb\u003eKara Keeling, author of \u003ci\u003eQueer Times, Black Futures\u003c\/i\u003e\u003c\/b\u003e\u003cbr\u003e Wendy Hui Kyong Chun is Simon Fraser University’s Canada 150 Research Chair in New Media and Professor of Communication and Director of the SFU Digital Democracies Institute. She is the author of \u003ci\u003eControl and Freedom\u003c\/i\u003e, \u003ci\u003eProgrammed Visions\u003c\/i\u003e, and \u003ci\u003eUpdating to Remain the Same\u003c\/i\u003e, all published by the MIT Press. \u003cbr\u003e \u003cbr\u003eAlex Barnett is Group Leader for Numerical Analysis at the Center for Computational Mathematics at the Flatiron Institute in New York. He has published more than 50 research papers in scientific computing, differential equations, fluids, waves, imaging, physics, neuroscience, and statistics.\u003cbr\u003e ","brand":"The MIT Press","offers":[{"title":"Default Title","offer_id":46304746668261,"sku":"NP9780262046221","price":29.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780262046220_41bc01b1-2643-4d75-a171-b1544371f59f.jpg?v=1730758310","url":"https:\/\/k12savings.com\/es\/products\/discriminating-dataisbn-9780262046221","provider":"K12savings","version":"1.0","type":"link"}