Wendy Hui Kyong Chun – författare

New Media, Old Media is a comprehensive anthology of original and classic essays that explore the tensions of old and new in digital culture. Leading international media scholars and cultural theorists interrogate new media like the Internet, digital video, and MP3s against the backdrop of earlier media such as television, film, photography, and print. The essays provide new benchmarks for evaluating all those claims; political, social, ethical, made about the digital age. Committed to historical research and to theoretical innovation, they suggest that in the light of digital programmability, seemingly forgotten moments in the history of the media we glibly call old can be rediscovered and transformed. The many topics explored in provocative volume include websites, webcams, the rise and fall of dotcom mania, Internet journalism, the open source movement, and computer viruses. New Media, Old Media is a foundational text for general readers, students, and scholars of new media across the disciplines. It is essential reading for anyone interested in understanding the cultural impact of new media.

This much-expanded and updated second edition of New Media, Old Media brings together original and classic essays that explore the tensions of old and new in digital culture. Touching on topics including media archaeology, archives, software studies, surveillance, big data, social media, organized networks, digital art, and the Internet of Things, this newly revised critical anthology is essential reading for anyone studying the cultural impact of new and digital media.

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 much-expanded and updated second edition of New Media, Old Media brings together original and classic essays that explore the tensions of old and new in digital culture. Touching on topics including media archaeology, archives, software studies, surveillance, big data, social media, organized networks, digital art, and the Internet of Things, this newly revised critical anthology is essential reading for anyone studying the cultural impact of new and digital media.

New Media, Old Media is a comprehensive anthology of original and classic essays that explore the tensions of old and new in digital culture. Leading international media scholars and cultural theorists interrogate new media like the Internet, digital video, and MP3s against the backdrop of earlier media such as television, film, photography, and print. The essays provide new benchmarks for evaluating all those claims; political, social, ethical, made about the digital age. Committed to historical research and to theoretical innovation, they suggest that in the light of digital programmability, seemingly forgotten moments in the history of the media we glibly call old can be rediscovered and transformed. The many topics explored in provocative volume include websites, webcams, the rise and fall of dotcom mania, Internet journalism, the open source movement, and computer viruses. New Media, Old Media is a foundational text for general readers, students, and scholars of new media across the disciplines. It is essential reading for anyone interested in understanding the cultural impact of new media.

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?

Creating communities of care in the digital worldData Fluencies offers a model for enacting theories of data justice, methods of community engagement, and practical approaches to disrupting institutions and infrastructures that have diminished the capacities of everyday digital technology users. Combining humanities-based critical thinking, computational data analyses, arts-based research cocreation, and media making, data fluencies offer creative and imaginative approaches to investigating and countering algorithmic discrimination, digital surveillance, mis- and disinformation, and deep fake versions of reality.Not fixed or static, data fluencies move with, through, and against data streams to build the worlds we want, not the ones algorithms push on us. Presenting real-world examples, the authors demonstrate how data fluencies transcend difference and counter the proliferation of hate and toxicity online. As they outline visions for disrupting the past to reimagine the future, the authors guide the way in devising new ways to think, act, and imagine – to influence and reshape the currents that flow through our data-driven world.Distributed for meson press.Retail e-book files for this title are screen-reader friendly with images accompanied by short alt text and/or extended descriptions.