Description
Book SynopsisHow 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?
Trade Review"How are we to contend with the many forms of pattern discrimination in contemporary life? This book shows the complexity of the terrain and reminds us what is at stake."—Kate Crawford, AI Now Institute NYU
"Profound and provocative, this book demonstrates the enduring relevance of theory to contemporary digital dilemmas. Addressing platform capitalism, democratic decay, and the future of labor and play, the authors illuminate the alien intelligence of big data, pattern recognition, machine learning, and artificial intelligence."—Frank Pasquale, University of Maryland
