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We Are Data: Algorithms and the Making of Our Digital Selves
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About We Are Data: Algorithms And The Making Of Our
Product Description What identity means in an algorithmic age: how it works, how our lives are controlled by it, and how we can resist itAlgorithms 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. Review "We Are Datais a gem!... This finely crafted book should help us to take a giant collective leap forward." ― International Journal of Communication "We Are Dataspells out the implications of being made of data in the digital age: our new & algorithmic identity. John Cheney-Lippold shows how algorithmic logics that undergird the architecture, regulation, monetization, and uses of the Internet have changed the nature of human experience and identity. Through witty and accessible examples, he eloquently lays out the social and political consequences of transcoding lived identity into measurable types in our new world. Clearly written, carefully researched, timely and intelligent,We Are Datais a compelling and much-needed book." -- Alexandra Juhasz,Chair, Film Department, Brooklyn College "John Cheney-Lippolds deft examination of & measurable typesthe categories by which we are known and assessed, based on our datasheds light on contemporary societys encounter with information systems to scrutiny, and with those eager to identify us for their own ends.We Are Data goes beyond naming possible harms. It helps us think differently about what it means to be & seen by marketers, algorithms, or the NSA as members of shifting categoriesidentifications that structure us and our encounter with the world, but that we have little power to shape." -- Tarleton Gillespie,author of Wired Shut: Copyright and the Shape of Digital Culture "This book sparkles with brilliant insights. It offers us tools and a vocabulary through which we can think about the layers of identities that our data-conjured ghosts inhabit. I dont think I fully grasped the complexity of what these clouds of commercial data did with us and to us until I read We Are Data." -- Siva Vaidhyanathan,author of The Googlization of Everything―and Why We Should Worry "We Are Data is an inspiring and thought-provoking book to read, especially for those interested in the social, political, and cultural aspects of data. It draws on a wide range of well-known literature in the field of Internet and algorithm studies and further engages deeply with the philosophical aspects of the presented themes." ― Mobile Media and Communication "If knowledge is indeed the means by which we can begin to challenge the digital status quo, then Cheney-Lippold has done much to forearm us by so capably elucidating the problem." ― LSE Review of Books "The text moves beyond overdone topics of online privacy to look at how
